{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":2186,"offset":2176,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"```json\n{\n    \"result\": 0.8\n}\n```","behavior":"degenerate_repetition","conversation_hash":"d88d914c589ec119ba9d80a96d6411f6","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Italian","model":"gpt-4o-2024-08-06","n_chars":43859,"n_turns_in_conversation":1,"n_words":7914,"source_row_offset":2176,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:47:08 GMT","turn_id":"821d49cf91b143ad67bd8324","turn_index":0,"user_text":"You are a JSON assistant. You only reply in valid JSON and never in normal text. Assign a difficulty level to the video information provided below. The output should be a single property called \"result\" with a float value between 0 and 1. The difficulty levels are categorized as follows: \n0.0 - Complete Beginner: This level includes very basic vocabulary and simple phrases, accompanied by clear visuals and context, making it suitable for individuals with no prior knowledge of the target language. \n0.3 - Beginner: This level features simple sentences and commonly used vocabulary. The video may provide some visual aids and context to help with understanding. \n0.5 - Intermediate: This level presents more complex sentences and a broader vocabulary range. It may include some idiomatic expressions that require additional background knowledge. \n0.6 - Upper Intermediate: This level includes specialized vocabulary and concepts relevant to specific fields. Viewers should have a reasonable proficiency in the target language for full understanding. \n0.8 - Advanced: This level uses advanced vocabulary and intricate sentence structures, potentially involving nuanced discussions that necessitate a strong command of the language. \n1 - Very Advanced: This level is aimed at fluent speakers, incorporating specialized terminology and concepts that may not be familiar to all native speakers.\n Description: Due uomini quattro donne e una mucca depressa | Commedia | HD | Film completo in italiano \n\nRegia: Anna Di Francisca\nSceneggiatura: Valentina Capecci, Anna Di Francisca, Giuseppe Furno\nStar: Predrag 'Miki' Manojlovic, Maribel Verd\u00fa, Eduard Fern\u00e1ndez\n(2012) \n\nUn compositore italiano in crisi si prende una pausa a Valencia, dove sviluppa un'attrazione per una cantante famosa.\n\nFilm&Clips in Italiano mette a disposizione un'ampia selezione di film in ITALIANO!\nIscriviti a Film&Clips in Italiano qui: http://bit.ly/FilmClipsInItaliano\n\nFilm&Clips in Italiano \u00e8 edito da Film&Clips, il pi\u00f9 bel canale di cinema gratuito e legale di Youtube!\nThe best FREE & LEGAL Youtube Channel: movies, clips, docs, short movies from all around the world - Italian, English, Spanish, French, Deutsch, and other subtitles available!\nIscriviti a Film&Clips: http://bit.ly/Subscribetofilmandclips\nInstagram: https://www.instagram.com/filmandclips_/\n\nBuona visione su Film&Clips in Italiano, il canale di cinema in lingua ITALIANA!\n\n#filmcompleto #italiano \n\nDue uomini quattro donne e una mucca depressa | Commedia | HD | Film completo in italiano\nTitle: Due uomini quattro donne e una mucca depressa | Commedia | HD | Film completo in italiano\nCaption: [Musica] [Applauso] [Musica] ero profondamente smarrita Maria e non trovavo la forza di reagire il mondo tutto Mi sembrava un orrore senza pi\u00f9 una famiglia senza pi\u00f9 l&#39;amore ero completamente sola una notte disperata ho cominciato a pregare improvvisamente una sensazione di pace mai provata prima le tenebre lasciano il posto a una luce e quella luce Mi salva questo ti invito a fare cara abbandona questo mondo Segui la luce sar\u00e0 la tua guida la tua compagna Fedele Qui ci vuole tutta una cosa dei violini uno con l&#39;arte tua il nome tuo e i premi che hai beccato Ma li fai impazzire Ciao me ne vado come Ciao deve venire tutta la parte pi\u00f9 bella e poi che gli dico alla rete che ha voluto proprio te per le musiche Dova Edoardo [Musica] [Applauso] [Musica] C Ma che succede il solito manifestazione a Piazza Venezia processione merda senta mi deve far passare ho un&#39;urgenza un&#39;urgenza di che tipo di andarmene da qui una brutta giornata eh Mia moglie mi ha mollato mia figlia mi ha mollato il motorino ho lasciato il lavoro ho un colesterolo alle stelle odio la chiesa Senta Io quest&#39;estate ho fatto un corso di psicologia per aiutare vittime di incidenti crimini vari l&#39;ascolto sente questi cazzo di tamburelli Questo \u00e8 un corteo lei pu\u00f2 immaginare un corteo immenso muto bisogna fare paura Washington agosto 89 la marcia silenziosa dei Nevi venga faccio passare Alice Alice puoi abbassare un po&#39; la musica meglio cos\u00ec molto meglio come stai tesoro Bene pap\u00e0 tu come al solito esci con qualcuno no perch\u00e9 mi chiedi sempre la stessa cosa perch\u00e9 ti farebbe bene eh vieni a trovarmi no adesso \u00e8 impensabile vado male a scuola e ho una strega in casa che tutto il giorno mi rompe le palle perch\u00e9 studi non parlare cos\u00ec di tua madre dai tu dici molto di peggio Senti mi manchi non ti deprimere capito E ho un&#39;altra chiamata in arrivo Ci sentiamo dopo Ok va bene Ciao e Trovati una fidanzata Ciao [Musica] im para apreciar son Las condiciones que estar dominando Al menos Para los siguientes Y de Nueva cuenta llegar fin de semana vemos una mejor Y Esto es AGR repica meic [Musica] [Musica] compl [Musica] K [Musica] C [Musica] Hola Ci mancava soltanto questo s\u00ec E dove sta Emilio adesso uno sbarco in piena regola Eh s\u00ec uno sbarco uno sbarco molti arrivano con dei barconi e affogano nello Stretto \u00e8 un&#39;evidente strategia di invasione \u00e8 una tattica che invent\u00f2 un comandante inglese David Lampard E chi me lo dice che tutti questi nomi non se li inventa lei siamo gi\u00e0 stati invasi dagli arabi e ci abbiamo messo OT secoli per liberarcene E allora non si preoccupi Non credo che vivr\u00e0 cos\u00ec tanto e quello che \u00e8 peggio \u00e8 che ora ci invadono con il permesso del governo e vedi come si moltiplicano come conigli Beh allora se per quello dovremmo imparare da loro visto che scopiamo sempre meno stor par eremo mai nulla corsi e ricorsi Questo lo disse un principe italiano Gianluca Maldini dico Giambattista Vico un filosofo del X secolo e lei chi \u00e8 mi chiamo Edoardo Edoardo Leri italiano di Roma ci mancava anche questo prima arrivarono gli arabi e ora i romani chi saranno i prossimi i trogloditi generale per favore vuole tagliarsi i capelli Oh no no no no no sto cercando Emilio Ramirez mi hanno detto che era qui c&#39;era ma se n&#39;\u00e8 andato Adesso pu\u00f2 trovarlo quella cretinata da [\u00a0__\u00a0] che ci vadano le donne ancora ancora Ma un uomo che si possa definire tale Come si fa l&#39;addestramento gli farai fare l&#39;addestramento [Musica] [Risate] per\u00f2 Emilio non potevi aspettare che finissimo ha ragione padre che lui \u00e8 mio ex parente Appen arrivato da Roma ex parente e questo che significa \u00e8 il cugino della mia ex moglie e anche il mio migliore amico Per questo non mi vergogno che ci abbia sentiti vergogna \u00e8 che Edoardo \u00e8 uno dei tre migliori musicisti italiani e chi sarebbero gli altri due e come l&#39;ai sembrato il nostro coro Ah l&#39;emozione di rivedere il mio amico mi ha distratto ma sono sicuro che con la sua direzione questo coro sar\u00e0 delizioso sei sempre cos\u00ec Franco e cos\u00ec sincero ma riguarda l&#39;emozione era vero S\u00ec un&#39;altra [Musica] volta zucchero No no va ben\u00e8 Dam un po&#39; ti conosco alcolisti anonimi Ti ricordi quella volta che eravamo in macchina ubriachi e fradici si ferma la polizia ci dice identificat Devi e io gli ho detto Non posso sono un alcolista anonimo al alcolista ano Cosa vogliamo fare per il tuo compleanno niente da quando tua cugina mha lasciato non festeggio pi\u00f9 niente n\u00e9 compleanno n\u00e9 capodanno niente Tu stai sbagliando e perch\u00e9 passate sempre davanti ti adora chi ci Met ho bisogno che la mia piccola Alice venga a vuoi che festeggi il mio compleanno per vedere Alice e tu che ci fai in un coro e lei No no no la Violetta No lo farei volentieri un peccatuccio con italiano troppo raffinato sicuramente \u00e8 gay perch\u00e9 gay a me sembra molto virile invece pi\u00f9 di tuo padre Mio padre non \u00e8 virile \u00e8 militare che \u00e8 molto diverso di sicuro \u00e8 sposato e con figli no no no no no se fosse stato sposato non sarebbe venuto solo sei single \u00e8 gay sicuro Sara come minimo \u00e8 divorziato ormai tutto il mondo divorzia e lo dici per me Ma no tesoro no per me tu sei pi\u00f9 che giustificata tu vali 1000 volte di pi\u00f9 di quel Barbi ruccio quella bella no no no no no no no \u00e8 quella che sta di qua la piccolina ah la piccolina tanto non mi prendi Mi sono innamorato innamorato innamorato \u00e8 cos\u00ec dolce dolce come la tua ex No no la mia ex no della mia ex preferirei non parlare Edoardo e tu cosa pensi dell&#39;italiano io prima dovrei sentirlo parlare sentirlo parlare S\u00ec gli uomini mi incantano con la parlantina interessante Beh il tuo ex \u00e8 un bel chiacchierone quello \u00e8 il problema che scopava come parlava troppo e male dai e tu non ci pensare quello non piacer\u00e0 mai al generale almeno questo \u00e8 maturo il mio ultimo fidanzato non gli piaceva perch\u00e9 era nato dopo la morte di Franco dimenticatevi gay che mania No non \u00e8 gay ho portato Ancora con questa storia della barba finta \u00e8 un elemento di intimidazione e anche un travestimento Perfetto Adesso mangiati il budino c&#39; haai messo il caramello Il medico ha detto che devi moderare lo zucchero e quindi niente caramello medico non capisce niente niente Sono Argentina e che diamine cessole cosa cosa cosa cosa vuoi fare una di quelle bestiacce ha deciso di fare il nido sull&#39;antenna mangia il budino che me ne occupo io 5 minuti ti do C Poi proseguo con l&#39;operazione uccellaccio [Musica] volatilizzato Mi dispiace per\u00f2 te ne devi andare vola Vattene vattene vola via vattene via via prendi il volo vola ah [Musica] oh questo piano dovrebbe essere accordato non \u00e8 il piano mamma Sono io che non lo so suonare da sola non imparerei mai tua zia Rosa ha preso per anni lezioni da nostra madre Gi\u00e0 ma io non ho nessuno che mi insegni e non ho i soldi per pagare un professore sempre con questi soldi e Maledetti soldi Vabb\u00e8 preparo la cena mamma mamma non devi dirmi nulla sul tuo chaufer \u00e8 che l&#39;ho incontrato che stava chiudendo il negozio mi ha offerto un caff\u00e8 ed \u00e8 stato cos\u00ec gentile da portarmi con la moto a casa m traduzione hai aspettato che uscisse dal lavoro hai finto di incontrarlo per caso e hai passato il pomeriggio a parlargli di me ma \u00e8 un brav&#39;uomo ma un pessimo marito mamma Se tornassi con Carlos avresti soldi per le lezioni di piano e non dovresti lavorare in quella fabbrica cos\u00ec cos\u00ec cos\u00ec orrenda La devi smettere mamma non mi va di discutere bene ma devo dirti un&#39;ultima cosa da quando hai divorziato la tua vita va sempre peggio Dimmi una cosa Oggi non ci vai al [Musica] Pingo Molte grazie Irma Gra cucina \u00e8 una meraviglia Soprattutto ricette argentine togli questa musica che modesto mi \u00e8 stata molto utile per la depressione e anche a Luis anche lei \u00e8 depressa S\u00ec s\u00ec s\u00ec s\u00ec ha sofferto molto la nostra separazione Ho provato un Vivaldi e a Luisa piace Edoardo Leri Ora vedo il mio futuro pi\u00f9 chiaro Edoardo Lerry sinfonie per rumin anti depressi S\u00ec s\u00ec E qual \u00e8 la tua tattica con Vittoria Per il momento la presenza la presenza la presenza stare l\u00ec io sto l\u00ec per tutto quello che vuole Emilio non hai 15 anni gi\u00e0 ma non potrei sopportare un altro no Ma almeno parlerai con lei no per questo sei entrato nel coro canto malissimo anche lei s\u00ec eh potremmo parlare di quanto cantiamo male bene ha altri interessi oltre al coro le piacciono gli animaletti Se scopri che sono un cacciatore Ciao ciao buonanotte cosa fai Manuela il linguaggio dei sordomuti lo sto imparando e perch\u00e9 Ma come perch\u00e9 voglio lavorare come volontaria per i bambini sordomuti non ti basta il comitato delle feste e il mercatino artigianale e l&#39;aiuto alle donne disoccupate Ma vuoi smettere mi stai deconcentrato conosco un modo migliore per deconcentrare domani devo alzarmi presto bene se stiamo nella media non ci porter\u00e0 via pi\u00f9 di 10 minuti sono stanco Manuela Carmelo per favore da pi\u00f9 di un mese che non non esagerare 36 giorni per essere esatti io ti amo lo stesso [Musica] tesoro ciao Sara ciao Mi sono persa qualcosa \u00c8 appena passata una delle liridi con una scia molto evidente passer\u00e0 ancora sicuramente oggi \u00e8 uno dei giorni di massima attivit\u00e0 perch\u00e9 non mi mostri Venere \u00e8 il mio pianeta favorito [Musica] volentieri eh Che spavento Eh vado a farmi un infuso di rabarbaro cosa rabarbaro ma che ma non er stanchissimo S\u00ec per\u00f2 qui non c&#39;\u00e8 il rumore eh il rumorino non c&#39;\u00e8 il tipico delle citt\u00e0 e poi c&#39;\u00e8 troppo buio se apro gli occhi non mi vedo neanche le mani Prova a dormire con gli occhi chiusi mi sembra di essere morto nel nulla pi\u00f9 assoluto e mi sta salendo l&#39;ansia sei proprio da psichiatra amico mio Come hai fatto abituarti a questo per un aiutino goccio di vino e dormi Ehi ancora basta me ne vado col mio infuso eh Me ne vado con l&#39;infuso me ne vado col mio infuso [Musica] [Musica] Gallo di [Musica] merda [Musica] campane di [Musica] merda pi\u00f9 Brio pi\u00f9 [Applauso] [Musica] Brio poveretto si \u00e8 fatto male \u00e8 meglio non muoverlo per faore CH vado ti accompagno sta bene padre No figliuola credo di essermi rotto il femore Vediamo quello non \u00e8 il femore Oh Mi scusi sta tutto l\u00ec vicino Eh mi sono [Musica] sbagliato S\u00ec bene Allora verr\u00e0 un prete sostituto da Valencia ma solo per la messa e per la confessione perch\u00e9 il coro non \u00e8 prioritario che pen nessuno di voi sa cantare ad eccezione di uno dei contratti uno si distr l&#39;altro stona non me ne frega niente Noi non siamo dei professionisti Ard \u00e8 solo per passare il tempo per me \u00e8 stata come una terapia ti dico la verit\u00e0 non lo voglio fare Edoardo Per favore ho promesso che ti avrei Edoardo se tu mi dici di s\u00ec Vittoria penser\u00e0 che non sono un tipo qualunque ma uno speciale cui fa il favore un artista importante come te e potremmo far progredire la nostra relazione tu non hai una relazione ma forse potrei iniziarlo una relazione Edoardo ma senza il coro non potr\u00f2 vedere Vittoria per favore Mi dispiace Emilio Io voglio che tu sia felice con vior per\u00f2 non ho voglia di dirigere il coro Va bene ti propongo una cosa un favore per un favore qui pro quo Come si dice in italiano \u00e8 latino e non significa quello va bene Ascoltami Tu mi fai il favore di dirigere il coro e io ti faccio un altro favore Dovrebbe essere m molto [Musica] grande Lorena tu canti un po&#39; troppo forte io Sono Patrizia Lorena sono io voi due invece Formate una coppia perfetta Ah s\u00ec m S\u00ec sentito nessuno dei due sa respirare eh Ma insomma Certo che sappiamo respirare sen\u00f2 saremo morti Ascolta non si canta solo con la gola tutto comincia da qui continua nel petto e nei risonatori siamo dei Dilettanti Edoardo e tutti facciamo molti errori a parte Julia che ha un altro livello ma siamo qui perch\u00e9 ci piace cantare e vogliamo imparare se vogliamo migliorare dobbiamo lavorare moltissimo e con convinzione esercitarci anche fuori da qui e sarebbe meglio se non cantasse canzoni religiose ma siamo un coro di chiesa S\u00ec lo so per\u00f2 deve essere anche un divertimento per tutti dobbiamo decidere come gli chiederei di farti lezione di canto sicuramente mi dir\u00e0 che non si pu\u00f2 fare Io ho l&#39;impressione che Edoardo sia una di quelle persone che dice sempre no a quello che chiedi Ma poi alla fine lo fai Fidati di me Ciao Julia Ciao mamma Ti lascio tuo nipote che succede oggi la stessa cosa di ieri il passaggio delle Meteore non dura un solo giorno Stai attenta capito Mamma ho 25 anni Ciao Marta mi dice sempre la stessa cosa ho 25 anni Io ne ho 45 Allora prego 54 l&#39;ordine dei fattori non Insomma dovresti essere orgogliosa di avere una figlia astronoma \u00e8 sempre stata fra le nuvole Ora sta un po&#39; pi\u00f9 in alto vuole andare a lavorare in America come se fosse facile intanto tutte le notti guarda le stelle con il suo telescopio e mi lascia la figlia notte e giorno ti ho detto di non parlari in inglese ma tu capisci cosa dice s\u00ec le piace tanto la torta come a me che carina la nudit\u00e0 non \u00e8 l&#39;equipaggiamento abituale per un soldato ma \u00e8 lo stato naturale di un corpo umano soprattutto per portare a termine certe attivit\u00e0 ti sbagli il colonnello Russo Alexander mostovoi non si tolse l&#39;uniforme per tutta la Seconda Guerra Mondiale Eppure rese gravide sei Partigiane e una spia tedesca Certo che lei ha delle vertebre molto schiacciate le ho cos\u00ec dal Vietnam Vietnam la Spagna non partecip\u00f2 alla guerra del Vietnam Certo che no sono stato l\u00ec in vacanza Sono scivolato nella vasca dell&#39;hotel maledetta manodopera cinese hai visto No Che cosa una stella cadente non l&#39;hai vista No non ne ho mai visto una in tutta la mia vita mai mai concentrati Edoardo concentrati questi sono aboli Muoviti cos\u00ec capiscono che siamo vivi civette Le civette civette gufi Questa \u00e8 una [\u00a0__\u00a0] [Risate] giungla ti manca a Roma non ci sto molto bene nel mio paese No tu sei un bastardo mezzzo ingese mezzo francese senti Idiota Ho provato a sentirmi italiano no E tu Emilio a te non manca la vita che facevi Eh no no no non sai che il lavoro \u00e8 far fuori l&#39;eredit\u00e0 di mio padre \u00e8 rimasta solo questa casa La casa di famiglia dei nonni e sto qui solo perch\u00e9 credi che vada dietro a Victoria perch\u00e9 non si pu\u00f2 vivere senza donne si pu\u00f2 si pu\u00f2 non si pu\u00f2 Si pu\u00f2 si pu\u00f2 quello che non si pu\u00f2 \u00e8 essere felici e ora c&#39;\u00e8 copertura sembra di s\u00ec Guarda guarda se c&#39;\u00e8 copertura da S\u00ec c&#39;\u00e8 copertura Alice Ciao tesoro come va da D Dammela bene sto bene S\u00ec \u00e8 molto diverso dall&#39;Italia \u00c8 molto s\u00ec s\u00ec s\u00ec s\u00ec Emilio \u00e8 qui ti vuole parlare te lo passo Emilio Eh va gi\u00f9 Alice Alice come stai bellissima s\u00ec quanto te s\u00ec Bene bene ben s\u00ec no s\u00ec non ti preoccupare No mi occupo io di lui tieni questo bicchiere seno una cosa [Musica] Alice io l&#39;amavo veramente Luisa era bello educato un signore era un amore della mia vita Manolo era un torero di Altri [Musica] Tempi da qualche anno tutto quello che semino \u00e8 ecologico Ora sto cercando di coltivare Qualcosa di unico che cosa pomodori Incas Incas Incas pomodori Incas esattamente gli stessi pomodori che coltivavano gli Incas prima della conquista spagnola \u00e8 un prodotto 100% naturale biologico che c&#39;\u00e8 prima i pomodori li tiravi alla polizia e adesso li coltivi se ti piace mi piace mi piace Immagino che tutti quelli Delo No tutti no perch\u00e9 no non parlo con Sara Perch\u00e9 cose mie quello che andremo a fare \u00e8 che durante la prossima prova del coro mi farai una serie di domande a cui io ti risponder\u00f2 perfettamente e brillantemente Perch\u00e9 perch\u00e9 tu prima mi darai le risposte come un concorso truccato Esattamente alle donne pi\u00f9 che quello che interessa di un uomo pi\u00f9 dei soldi intelligenza Non ci credere guarda i calciatori hanno tutti delle donne spettacolari Allora il costo per una cabina individuale \u00e8 di Uh \u20ac900 pensione completa 8 giorni e si rimorchia di sicuro Questo dipende dalla persona perch\u00e9 con quei soldi potrei andare pi\u00f9 volte a [\u00a0__\u00a0] come fanno tutti i miei amici Io no per\u00f2 io non sono cos\u00ec io sono pi\u00f9 romantico Per\u00f2 voglio perdere la verginit\u00e0 \u00e8 per questo che ho pensato alla crociera Pablo Non voglio prenderti in giro La maggior parte delle donne che vanno in crociera hanno una certa et\u00e0 [Musica] meglio Ciao Che cosa hai venduto a Pablo che era cos\u00ec concentrato Per adesso niente sta progettando Come Dove Quando perdere la verginit\u00e0 e quello che \u00e8 pi\u00f9 importante quanto gli coster\u00e0 \u00e8 vergine pensavo che i ragazzi di oggi non lo fossero pi\u00f9 Pablo \u00e8 diverso \u00e8 romantico comunque inizia a rendersi conto di che cos&#39;\u00e8 che muove il mondo e che cosa lo muove il sesso io credevo fosse l&#39;amore Ah te e pu\u00f2 darsi per\u00f2 siccome \u00e8 cos\u00ec difficile innamorarsi \u00c8 vero \u00c8 vero \u00c8 vero \u00c8 vero \u00c8 vero \u00c8 vero m m cio\u00e8 no A volte abbiamo davanti a noi l&#39;amore della nostra vita non ce ne rendiamo conto Beh cosa ti serve s\u00ec Ehm due cose mi piacerebbe invitarti alla festa del mio compleanno festa Che bello da tanto che non vado una festa quanti anni compi Quanti me ne dai 55 50 Perdonami Perdonami perdonami non sono capace a indovinare non \u00e8 successo niente niente fa l&#39;altra cosa Ah s\u00ec avrei bisogno di un biglietto da Roma qui per favore vediamo Roma non sar\u00e0 per la tua ex No no non pago il viaggio al mio torturatore No no \u00e8 una sorpresina per Edoardo una sorpresa Mh sua moglie no \u00e8 separato ah ah Perch\u00e9 Ah no no Ah niente e quanto tempo siete stati sposati quasi 10 anni Oh il tempo \u00e8 il peggior nemico dell&#39;amore no per me non \u00e8 cos\u00ec Julia continua a essere l&#39;amore della mia vita e l&#39;amo ancora Julia \u00e8 una donna molto attraente e ha una voce meravigliosa non so in Italia per\u00f2 in Spagna non \u00e8 ben visto fare i complimenti alla moglie di un altro Julia non \u00e8 pi\u00f9 sua moglie mi perdoni ma non bisognerebbe far arrabbiare nessuno che abbia un&#39;arma in mano \u00e8 una premessa elementare di strategia militare su il mento dovrebbe tagliarsi i capelli me li taglio come lei se lei si taglia i baffi no i baffi sono simbolo di virilit\u00e0 Tutto il contrario dei capelli lunghi con i capelli lunghi o con i baffi tutti vogliamo lo stesso che le donne ci guardino il generale non esercita esercito esercita Mah se le condizioni sono favorevoli sul campo di battaglia e quali sono che la signora sia rispettabile e soprattutto che abbia la pelle come il latte la prima la vedo possibile ma la seconda limita le possibilit\u00e0 Eh gi\u00e0 se lei \u00e8 come sua cugina cosa sa di mia cugina che \u00e8 una straniera che ha fatto di Emilio lo zimbello del paese ma tanta gente si separa Carlos \u00e8 un esempio non \u00e8 vero non \u00e8 mica lo stesso Chiedilo a [Musica] Emilio qui ci vuole ancora un po&#39; d&#39;acqua ma quante volte devo dirtelo caldoso Aros caldoso sei molto pesante mamma molto pesante Che vergogna che mia figlia non sappia cucinare perch\u00e9 quell&#39;italiano viene a cena qui stasera perch\u00e9 tua figlia deve chiedergli un favore a quell&#39;italiano non sta bene che venga a casa di una divorziata avresti preferito che fosse venuto quando ero sposata no sta bene punto bene tu Stasera Andrai al Bingo con le tue amiche Ora lo sai S\u00ec per\u00f2 con quali soldi Ah Se uscisse il 69 almeno una volta mi manda in alti c&#39;\u00e8 un repertorio Corale sacro e un repertorio corale e profano la bellezza dipende dalla qualit\u00e0 della musica chi pu\u00f2 farmi un esempio di un&#39;opera che abbia un colo Mh flauto magico Aida Schumacher e ha delle aree meravigliose Schuman Schuman Schuman Volevo dire Schuman concentrati e con passione rayaba una canzone degli anni 40 bum bum bum nella quale il coro oltre a cantare le parole deve imitare il suono degli strumenti di [Musica] un&#39;orchestra B no non glielo chiedo non lo faccio \u00e8 una follia invitarlo a casa C Ma no che non lo \u00e8 Non fare la ragazzina Muoviti e vai a dirglielo vai Ciao Eh ti piacerebbe provare l&#39;arros caldoso C&#39;\u00e8 un ristorante in paese che lo fa S\u00ec no cio\u00e8 volevo dire C&#39;\u00e8 chi lo fa per\u00f2 non mi riferisco a un ristorante Lo preparer\u00e0 lei [Musica] delizioso non \u00e8 accordato Ma \u00e8 un bel piano \u00e8 molto antico S\u00ec era di mia nonna \u00e8 l&#39;unica cosa che si \u00e8 salvata dalla guerra Purtroppo non ho mai imparato a suonare bene abbiamo dei difetti Io non ho mai imparato a cantare non \u00e8 la stessa cosa S\u00ec no fai ancora in tempo a imparare Ti sarebbe utile per lavorare sulla voce Prova a cantare No Prova un po&#39; No veramente Dai va bene questo \u00e8 l&#39;ultimo pezzo che sto provando [Musica] sin [Musica] palabas Pal con oos [Musica] Pal [Musica] gi\u00e0 andiamo sen\u00f2 si raffredda vino come lo vuoi bianco o rosso acqua Ho il colesterolo molto alto non ti \u00e8 piaciuto per niente No al contrario era buonissimo [Musica] Gi quando hai detto che dovevo prendere lezioni ti riferivi ad una scuola di canto con un bravo maestro \u00e8 sufficiente Se sei disposta ad andare a Madrid posso parlare con una mia amica no non posso non posso andare a Madrid non posso lasciare mia madre sola la pensione che prende \u00e8 una miseria ha bisogno di me ha bisogno dei tuoi soldi non di te \u00e8 lo stesso non ho nulla Se lasciassi il mio lavoro per andare a Madrid non potrei mantenermi e neanche lei la mia amica ti aiuterebbe Io non so fare niente il lavoro che ho l&#39;ho ottenuto grazie a Manuela non ho un diploma sono un disastro ho sempre lasciato tutto a met\u00e0 la contabilit\u00e0 l&#39;inglese il corso da infermiera tutto a met\u00e0 compreso il mio matrimonio se facciamo qualcosa che non ci piace la volont\u00e0 \u00e8 la prima cosa che manca e mentre sei qui perch\u00e9 non mi dai tu lezioni vedi Julia questo non \u00e8 un buon momento della mia vita e per questo mi sono ripromesso di non fare niente che non mi piaccia e insegnare lo odio seguo il coro per Emilio perch\u00e9 gli devo un favore S\u00ec capisco niente di personale Io direi di no a chiunque Per\u00f2 l&#39;hai detto a me te la sei presa te la sei presa no davvero no Devi scusarmi ma domattina devo alzarmi [Musica] presto [Musica] Ola [Musica] straniero sospiri [Musica] Con voce dai [Musica] sosp l&#39; Sign ormai [Musica] sente con grande posso pi\u00f9 [Musica] tard con grande sfido non posso pi\u00f9 tardi anch&#39;io il [Musica] sospirando il Pastor sospirando [Musica] si che cing la mente la felice solte so viss a noi [Musica] morte che [Musica] felice [Musica] noi quello che avete appena cantato \u00e8 una scena d&#39;amore un orgasmo tra un pastore e una Ninfa scritto pi\u00f9 di 400 anni fa Immagino che anche tu sarai presa dalla festa no no no no non ti preoccupare io non ci vado perch\u00e9 non sono stata invitata io pensavo che ci andasse tutto il coro Mia madre si sta preparando da una settimana Emilio e io non andiamo d&#39;accordo S\u00ec ho sentito qualcosa Cosa hai sentito niente idiozia lo sai com\u00e8 la gente in questo paese pu\u00f2 essere molto crudele quando vuole quello che odio di pi\u00f9 in questa vita \u00e8 la codardia e non solo sul campo di di battaglia che cosa intende che se vuoi riconquistare Julia devi farlo di persona e a volto scoperto e se verrai sconfitto ti cercherai un&#39;altra donna e buonanotte Eduardo Senti ti volevo dire allora io e Emilio Volevamo provare la respirazione per\u00f2 c&#39;\u00e8 una cosa che non capisco tu avevi detto cos abbassavo Manuela Manuela sai cosa \u00e8 successo a Julia sembra che le abbia fatto male la cena \u00e8 malata No non ci arrivo con la voce non ci [Musica] riesco mi stai dicendo che non sono venuta al coro perch\u00e9 non vuoi darmi lezioni qualcosa del genere sbagli non sono venuta perch\u00e9 sto seguendo i tuoi consigli non ti capisco hai detto che quando facciamo qualcosa che non ci piace ci manca la volont\u00e0 no Ma a te piace il coro ti piace cantare non esserne cos\u00ec sicuro la maggior parte della gente in questo paese ci va Per distrarsi per non annoiarsi in un posto dove non succede mai niente per me \u00e8 importante che tu venga senza di te il coro non \u00e8 lo stesso senza di te succedono meno cose emigrante di [Musica] merda stai stai prima o poi facciamo i [Musica] conti lo fai bene Ah uso un trucco Eh penso che sto galleggiando nel Cosmo senza gravit\u00e0 ma senza gravit\u00e0 non si pu\u00f2 respirare dentro un&#39;astronave m dentro un&#39;astronave dentro un&#39;astronave s\u00ec eh eh eh eh eh Sei mai stata a Roma \u00e8 una citt\u00e0 stupenda tutte le grandi citt\u00e0 mi soffocano una volta sono stata a Madrid non mi \u00e8 piaciuta per niente Non avevo una buona guida conosco Roma meglio di questo paese dai proviamo che devo fare da mangiare a mio padre Ah il generale non Guida no il generale No non guida non cucina non fa niente sa solo comandare dai tratten [Musica] ilpo [Musica] or non trova pensieri il pubblico strano [Musica] sentire quatti fa [Musica] cos\u00ec mentre [Musica] [Musica] questo molto molto meglio no con i bassi pi\u00f9 veloci e i contralti pi\u00f9 brillanti Scusa Eduardo Ma nella mia partitura manca un bom bom hai il pollice sopra SC vendo tutto vendo tutto Julia andiamo a Madrid Io faccio il barbiere l\u00ec e tu ti dedichi allo studio del canto e tua madre ovviamente viene con noi Io ti amo Julia e voglio il meglio per te Se volessi il meglio per me Non verresti qui a farmi questo tipo di proposte nell&#39;italiano ma non lo capisci Non si tratta n\u00e9 di te n\u00e9 di Edoardo Qui si tratta di me della mia vita Eh voglio che succeda qualcosa nella mia vita che mi renda felice Hai capito ora no non ti capisco Certo \u00e8 questo il problema che non mi hai mai capita [Musica] mai [Musica] Irma Nascondilo bene sembra interessante questo libro che hai comprato S\u00ec soprattutto dove dice che Le Cicogne possono farlo 10 volte al giorno i leoni fino a 30 no S\u00ec Siamo dei Dilettanti Immagina di avere questa capacit\u00e0 e passare tutta la vita senza una compagna deve essere lei lei lei lei lei il libro il libro il libro da da Dammi il libro Alice Ma cosa ci fai con un fucile Vieni qua vieni vieni qui questo \u00e8 un ricatto io non lo chiamerei cos\u00ec le sto solo chiedendo un favore In cambio per\u00f2 tu terrai la bocca chiusa \u00e8 una trattativa A me non piace trattare lo disse anche il maresciallo francese Lor Basta con queste cazzate ci sta o no ci sto Lo faremo per\u00f2 lo faremo alla mia maniera c&#39;\u00e8 qualcosa che non si fa alla sua maniera niente ecco [Musica] gradisce No grazie succo da Lancia Grazie Prego di niente come ti stavo dicendo quando uno Arriva a una certa et\u00e0 nono mancarti certe cose bene a me sono sempre piaciuti bambini in senso buono chiaro veramente Mi stai chiedendo di avere figlio lo dici sul serio certo suppongo che prima di tutto Dobbiamo farlo un altro No bene cosa volevi farmi vedere il libro ce l&#39;hai \u00e8 il mio preferito quello mio per\u00f2 dizion pi\u00f9 vecchia ma questa \u00e8 nuova magari ci sono delle fotografie che non non ci sono nel tuo Ma se se ti va se ti fa piacere sfogliarlo lo trovi qui davvero non sapevo che ti piacessero cos\u00ec tanto gli animali da sempre io non mi sono perso un episodio di Felix Rodriguez della fluente ricordi bum bum bum bum bumo aspettando la praato Ma tu non andavi a checi con mio padre secoli fa No ora sono cambiato molto ora mi piacciono molto gli animali li rispetto li curo li amo per me gli animali Oh andiamo a ballare con la rugiada attaccata ai polmoni come una sanguisuga marciamo in terra ostile per raggiungere l&#39;obiettivo dietro le linee del nemico che fa la telecronaca Come fosse una partita di calcio La guerra \u00e8 lo sport pi\u00f9 crudele ragazzo mio molto crudele tira fuori meglio il peggio da ogni uomo solo gli autentici campioni sopravvivono e io sono uno di loro Agli ordini generale Dio erano anni che non mi sentivo cos\u00ec [Musica] vivo [Musica] quest&#39;estate Vorrei passare qualche giorno a Roma tutto il mondo dice che \u00e8 una citt\u00e0 meravigliosa S\u00ec \u00e8 molto animata ci sono processioni manifestazioni maratone [\u00a0__\u00a0] ma \u00e8 la Cetta eterna no S\u00ec s\u00ec Tu saresti la guida ideale Emilio sarebbe la guida ideale conosce perfettamente Roma \u00e8 meglio io no siamo troppo simili Che carina questa canzone Scusami [Applauso] Scusate un momento un momento un momento devo dirvi una cosa allora sto per portarvi un regalo ma per per tutto il paese mi hanno appena comunicato che il tradizionale concorso dei cori organizzati dalla regione quest&#39;anno si terr\u00e0 qui nel nostro paese Vamo sprare un&#39;occasione come questa E cos\u00ec ho pensato e spero che vi piaccia l&#39;idea che visto che abbiamo l&#39;onore di avere fra noi un un compositore e direttore di prestigio internazionale lui potrebbe comporre una canzone che ci faccia vincere il concorso e porti il nome del nostro paese molto in alto Edoardo Edoardo Edoardo Edoardo edoard Ard Ard Ard Ard [Applauso] [Musica] ballyi Tu lo sapevi Eh tu cosa dici Io per\u00f2 non c&#39;entro niente \u00c8 sempre Manuela che organizza questo genere di cose collette feste club di lettura lo sai com&#39;\u00e8 E tu che pensi dovrei comporre La canzone Non lo so che ti dice la tua volont\u00e0 la mia volont\u00e0 \u00e8 di ballare con te e Allora balliamo questa postazione \u00e8 perfetta perfetta detto lo stesso anche di quelle precedenti un applauso [Applauso] Ciao che mi nel Forse per come parlavano di lei Pensavo fosse una bimba molto meglio che non lo sia al Festival di Sanremo mio amico del cuore Ardo Ler Vai [Applauso] vai perfetta traiettoria [Musica] [Musica] retta [Musica] e tu Patrizia che cosa mi dici ti conviene chiedere a mia sorella perch\u00e9 io sono Lorena e non voglio avere figli con te per me \u00e8 uguale con l&#39;uno con l&#39;alt non [Musica] c&#39;\u00e8 resistenza del vento bene e non lo faccia fuori mai \u00e8 stata messa in dubbio la mia capacit\u00e0 balistica eh mai e poi con questo fucile \u00e8 impossibile ammazzare una persona [Musica] Eccolo eccolo vuoi conservarlo per ricordo No grazie Stai pensando anche tu la stessa cosa Non lo so a cosa stai pensando tu che \u00e8 stato il tuo ex il mio ex S\u00ec il mio ex Carlos No no \u00e8 impossibile \u00e8 impossibile non ha mai sparato in vita sua aspetta in pi\u00f9 non ti avrebbe preso neanche se avessi il culo come la luna di Valencia se torni nel coro compongo la canzone bene Forse dovresti seguire i tuoi stessi consigli e comporla solamente se ti va mi andrebbe se tu ritornassi nel coro non dicevi qui non succede mai niente Forse perch\u00e9 nessuno fa niente S\u00ec hai ragione La verit\u00e0 \u00e8 che qui nessuno fa [Musica] niente Te ne vai da Che spavento il lupo prima di mezzanotte come ce n&#39; rentola sai come ha fatto mio padre mi sono divertita molto Grazie dell&#39;invito ho ballato tantissimo Aspetta Ti voglio dire una cosa [Musica] Eh ti volevo dire questo l&#39;hai detto s\u00ec tu non vedi no hai capito Pablo S\u00ec in spagnolo come si dice bere bere bere Ah be B Ah bene tu suoni suono la batteria Ah s\u00ec io il piano Ah come si dice suonare la batteria tocar no tocar tocar tocar la batteria tocar S\u00ec in italiano toccare \u00e8 un&#39;altra cosa \u00e8 toccare e bacio bacio Quando le feste finiscono e si \u00e8 soli mi viene una gran tristezza Ma tu parli in italiano ma io non sono muta Io sono nata a Sasso Marconi vicino a Bologna sono venuta qui per amore lui era un torero \u00e8 morto nella plazza de toros e E da quel giorno Io ho smesso di parlare Che storia [Musica] triste quello non mi sembra un infuso di rabarbaro ogni giorno \u00e8 un altro giorno che c&#39;\u00e8 ho baciato Victoria e tu con Julia Ah bene non L&#39;ho baciata per\u00f2 mi ha toccato il culo il vecchio trucco del ferito sul capo sembra che tu abbia un&#39;invitata sorpresa sai di non essere la Benvenuta in questa casa senti Emilio credo che sia arrivato il momento di risolvere i nostri conflitti dobbiamo farlo proprio oggi perch\u00e9 cambia qualcosa no Cambia Cambia \u00e8 il mio compleanno Un giorno in cui sono un po&#39; felice Non mi va che me lo rovini un&#39;altra volta per favore Emilio Va bene va bene Sei invitata alla festa bevi quello che vuoi Mangia quello che vuoi e prendi tutto quello che vuoi Quello lo sai fare molto bene Emilio Tranquilla tranquilla cosa \u00e8 successo tra te e Emilio con lui niente E allora perch\u00e9 \u00e8 arrabbiato con te per tua cugina Lei ha provato a fare una vita normale ma non c&#39;\u00e8 riuscita e se n&#39;\u00e8 andata Io invece sono rimasta eravamo innamorate quando torn\u00f2 in Italia mi chiese di seguirla Perch\u00e9 non hai [Musica] fatto ho avuto paura non devi averne la cosa migliore per una persona \u00e8 quello che si \u00e8 sai quando si vive in un paese non \u00e8 cos\u00ec facile tutto il mondo ti guarda ti controlla ti spia Hai paura di guardare di toccare persino di sorridere sai qualcosa di lei molto poco quasi niente ora vive a Firenze se andassi a trovarla ti servirebbe per iniziare una nuova vita con lei o senza di lei una nuova vita come te qui io qui non ho iniziato una nuova vita Sto recuperando quella precedente pap\u00e0 pap\u00e0 ti fa molto male No no per\u00f2 S\u00ec sei sempre stato una piaga Ah va bene sto cambiando sono molto felice che tu sia qui Anch&#39;io sono contenta di essere venuta poi Pablo \u00e8 fighissimo sai \u00e8 uno schianto tu sei felice soltanto perch\u00e9 hai conosciuto Pablo dai Pap\u00e0 non fare scemo parliamo seriamente le lezioni di violino Pap\u00e0 ho finito col violino Che vuoi dire pap\u00e0 Ho finito con il violino sono la batteria non ne Acc la batteria batteria tu non puoi ti piace veramente s\u00ec E allora fallo Grazie papi ah Che carini che tenerezza Va bene vado a dormire Buonanotte Buonanotte Alice Buonanotte buonanotte che bella eh e vuole bene al suo pap\u00e0 al suo pap\u00e0 bella festa eh \u00c8 vero riuscita Avevo ragione \u00c8 stata un&#39;idea mia l&#39;avrei fatta lo stesso con coro senza coro uguale perch\u00e9 non mi hai raccontato di Sara e mia cugina orgoglio maschile sai te l&#39;avrei raccontato se non fosse stata tua cugina \u00e8 molto strano essere lasciato per una donna molto strano Molto strano almeno ti ha lasciato per qualcuno a me Monica mi ha lasciato per [Musica] nessuno [Musica] Ciao ciao Marta [Musica] s\u00ec mi hanno detto che dopo 10 minuti \u00e8 tornato alla festa bello pimpante ma che cosa pensavi a quella distanza e con un fucile adaria compressa che gli aprissi un altro buco nel culo Pensavo che sarebbe stato fuori combattimento per un po&#39; abbastanza per lasciare in pace Julia perch\u00e9 io potessi riconquistarla io rispettato la mia parte del contratto Ora tocca a te rispettare la tua e se non tieni la bocca chiusa inizier\u00f2 con le rappresaglie rappresaglie cosa vuole fare un consiglio di guerra stanotte hai potuto verificare che i miei istinti di Combattente sono intatti quindi devi stare attento Non smetta di dire scemenza questa situazione non mi piace per niente ma la barba gi\u00e0 sai com&#39;\u00e8 tuo padre io ho pensato fosse meglio chiedergli prima il permesso cos\u00ec non se ne esce bene potreste smettere di parlare come se io non ci fossi Ti ringrazio per essere venuta a chiedere la mano di mia figlia ringrazio cosa no no no no la mano no sono venuto a chieder il corpo intero per\u00f2 non solo per sposarla semplicemente per uscire insieme Eh Peccato Io avevo sempre sognato un matrimonio militare per mia figlia per\u00f2 tu sei gi\u00e0 divorziato E beh E che non si dica che il generale sarmento non si sa adeguare ai tempi che corrono Eh e Diciamoci la verit\u00e0 \u00e8 arrivata allora che qualcuno mi dia il cambio e si prenda cura di lei altrimenti la povera Victoria mi passa di cottura non sono una paia padre bene bene bene Come diceva il comandante olandese R Schneider Prima di attaccare vediamo i pro e i contro di contro hai solo che tua moglie ti ha lasciato Ma siccome era Straniera in realt\u00e0 non conta molto a favore Hai molte molte cose una buona situazione economica una Salute Pi\u00f9 o meno stabile e un coraggio pi\u00f9 che notevole come hai dimostrato nel venire qui a parlare con me a faccia a faccia S\u00ec mi sembri proprio un buon partito Quindi vi do la mia benedizione Eh grazie Ah un momento un momento dimenticavo In pi\u00f9 tu sei il migliore Ah dopo di me \u00e8 chiaro il migliore in cosa come in cosa nella caccia ha il polso di un basco verde e la mira di un legionario francese nella nostra ultima battuta ha fatto fuori quasi la stessa quantit\u00e0 di pernici che ho ucciso io vero Quante quante erano 18 In che secolo \u00e8 stata questa carneficina \u00e8 passato molto tempo come In che secolo In questo du settimane fa assassino 22 erano 22 Eh oh 22 Ciao ciao Come sta andando abbastanza bene Vuoi sentire qualcosa della canzone Tu che dici Mamma puoi andare al bingo con le tue amiche [Musica] deloro [Musica] del dai S solo [Musica] solo Marta io volevo volevo dirti una cosa \u00e8 una cosa Ah ancora guardare le stelle voi due S\u00ec siamo in un periodo di intensa attivit\u00e0 per i meteoriti Ah che meraviglia magari ne cascasse uno sulla testa di chi so io di chi di nessuno anzi s\u00ec di uno Allora Mar Scusate Avete visto la figlia del generale S\u00ec \u00e8 andata da quella parte molte grazie bene Marta quello quello che volevo dirti Sara io non sono innamorata di te eh neanch&#39;io di te mi piaci ma solo come amica quello che volevo dirti a me piacciono gli uomini lo so lo so \u00e8 questo mi riferisco al fatto che per trovare l&#39;amore dobbiamo andarcene da qui tu in America e io credo io in Italia Sei ancora innamorata di lei Credo di s\u00ec Non lo so Non lo so Salve scusi \u00e8 lei in gari S\u00ec venga a vedere lei stessa pap\u00e0 ma tu sei nudo in qualche modo S\u00ec figlia mia ho ancora i calzini ai piedi Sar\u00e0 meglio che vi lascio soli Grazie [Musica] Beh non \u00e8 niente da dirmi la cosa \u00e8 pi\u00f9 che Chiara figlia Un&#39;immagine vale pi\u00f9 di 1le parole Mi dispiace che tu mi veda cos\u00ec e che mi era caduta la pasti non sto dicendo di quello sto parlando di ingari non dicevi che ti attirava solo la la pelle bianca come il latte va bene va bene te lo racconto te lo racconto e copriti per faore Allora Namibia 1989 una manovra congiunta della NATO tua madre che Dio l&#39;abbia in gloria aveva cessato di esister e io mi sentivo cos\u00ec solo figlia mia cos\u00ec solo in un piccolissimo miserabile villaggio una donna viveva in una catapecchia immonda aveva malapena da mangiare colpita sistematicamente dalle difficolt\u00e0 di una villa esistenza quella donna aveva una bambina la mia coscienza mi imped\u00ec di rimanere indifferente inerte davanti a quella Sciagurata situazione figlia mia e quella bambina era angari Eh la bambina Ma che dici \u00c8 che da quel momento le negre mi hanno sempre fatto impazzire Beh Beh dopo tutte queste sciocchezze adesso le cose cambieranno io commando TR un poi e si fa quello che dico io E allora basta con tutte quelle stronzate militari le idiozie razziste le battute di caccia e E questa barba che si mette un giorno s\u00ec e l&#39;altro pure e tutte le altre merite cazzate Questo \u00e8 un colpo di stato in piena regola guarda una stella cadente niente non arrivo mai in tempo Sai a volte penso che le persone siano come le stelle cadenti il nostro passaggio nel mondo dura solo un istante e ci vede poca gente ma se in Quell&#39;attimo siamo molto brillanti possiamo lasciare un segno che ci far\u00e0 ricordare per sempre dove il lato buono io vedo quello cattivo Edoardo prima o poi tu tornerai a Roma e io devo continuare a vivere qui e non sono neppure una donna del tipo che prima ti prende e poi ti lascio cosa dici che che prima ti prende poi l\u00e0 Ah aspetta fare l&#39;amore e dopo Ciao [Musica] Ciao Andiamo a casa tua Ma allora non hai capito quello che ti ho appena detto non per questo Julia devo cambiare una cosa della canzone ah [Musica] non vita maata [Musica] cantano bene noi cantiamo molto [Musica] meglio Bravi bravi Signore signori dopo l&#39;esecuzione del coro di Gandia diamo il benvenuto al coro di bocca re un grande non \u00e8 importante vincere o perdere ma essere soddisfatti di quello che abbiamo fatto sar\u00e0 cos\u00ec in Italia qui in Spagna ci piace vincere [Musica] andiamo succede No non sto bene tu mi vedi bene S\u00ec ti vedo a me piace sempre \u00e8 meglio non posso [Musica] respirare [Musica] meglio S\u00ec ho distrutto tutti i miei fucili bruciato tutti i Tropi di caccia e per te far\u00f2 tutto quello che vuoi smetter\u00f2 di bere smetter\u00f2 di fumare non voglio che cambi e questo mi basta visto che s\u00ec se se mio padre ha una storia con una nera io posso averla con un ex Cacciatore anche se un poco alcolizzato Viva le nere viva iglio che Baciami un bago come non [Musica] filma [Musica] [Applauso] Bra Signore e signori vediamo allultima esibizione del concorso e poi l&#39;emozione della votazione della giuria e la consegna del primo premio al vincitore Che bello Signore e signori a voi il coroada dobbiamo andare a [Applauso] cantare b s b s Bo S solo solo solo desorientado solo solo solo desencantado solo solo solo indifferente solo solo solo [Musica] tormentato [Musica] Sol un Al coraz sol sol solente solo solo solo solo [Musica] [Applauso] sol [Musica] sol [Musica] Sol es la Canion es la passiona poa [Applauso] [Musica] Sonic Delo Meo [Musica] son [Musica] ilum [Musica] [Musica] can [Musica] Si vi stasa [Musica] [Musica] Bravo bravo Brava brava bravo bravo bravo bravissima S\u00ec Signore signori [Musica] sin palabas sin palabas con t ojos lo sientes dir [Musica] sin Pal que tus mi pero nun [Musica] TAR sin palabas palabas tus caricias Io Sa comprend sin PaLab Conus Besos ardientes dime eternamente me tendr [Musica] sin palabras Habla S [Musica] palabas con t sientes palabas che t labios mienten pero Nuna tus ojos engar sin [Musica] PaLab sin palabas tus caricias Io Sa comprender sin palabas con tus Bes entes me tendr [Musica] che signore inizialmente e all&#39;unanimit\u00e0 la giuria aveva deciso di dare il premio al coro di posal per\u00f2 secondo il comma 2 articolo 17 del regolamento del concorso di Cori regionali la Canzone interpretata avrebbe dovuto includere obbligatoriamente l&#39;espressione Ave Maria S\u00ec deve essere inclusa nel testo e la canzone del coro vincitore non la includ Pertanto il vincitore del concorso diventa il coro che aveva otuto il secondo posto \u00c8 una [Musica] vergogna [Musica] Reg [Musica] e\nLanguage: it"},{"assistant_initiates":false,"asst_text":"Certainly! Refactoring your `portfolio_optimizer.py` will enhance readability, maintainability, and reduce redundancy. Below are specific sections identified for improvement along with their updated code snippets and step-by-step instructions for replacement.\n\n---\n\n### **Section 1: Streamline Session State Initialization**\n\n**Issue Identified:**\nThere is redundant initialization of the `st.session_state.portfolios`. The `initialize_session_state` function already sets up the `portfolios`, making the subsequent standalone initialization unnecessary.\n\n**Updated Code:**\n\n```python\n# ----------------------------\n# Session State Initialization\n# ----------------------------\n\ndef initialize_session_state():\n    \"\"\"\n    Initializes Streamlit session state with default values.\n    \"\"\"\n    # Initialize example portfolios\n    st.session_state.setdefault('portfolios', create_example_portfolios())\n    \n    # Other session state variables\n    st.session_state.setdefault('backtest_results', {})\n    st.session_state.setdefault('step', \"Configure Portfolio\")\n    st.session_state.setdefault('edit_portfolio', None)\n    st.session_state.setdefault('add_new_portfolio', False)\n    st.session_state.setdefault('default_config', {\n        'rf_rate': 0.02,\n        'broker_fee': 0.0,\n        'benchmark_symbol': '^GSPC'\n    })\n    st.session_state.setdefault('show_proceed', False)\n    st.session_state.setdefault('selected_time_frames', ['Annually'])\n    st.session_state.setdefault('selected_rolling_periods', [252])\n\n# Initialize session state\ninitialize_session_state()\n```\n\n**Instructions for Replacement:**\n\n1. **Open the File:**\n   - Navigate to your project directory and open `portfolio_optimizer.py` using your preferred text editor or IDE.\n\n2. **Locate the Session State Initialization:**\n   - Find the section in your code that defines the `initialize_session_state` function. It typically appears after the global configurations and example portfolios setup.\n\n3. **Replace the Existing Function:**\n   - **Replace** the entire existing `initialize_session_state` function with the updated version provided above.\n   \n4. **Remove Redundant Initialization:**\n   - **Delete** the following redundant block of code that appears **after** the `initialize_session_state()` call:\n     ```python\n     # Initialize example portfolios if not already in session state\n     if 'portfolios' not in st.session_state or not st.session_state.portfolios:\n         st.session_state.portfolios = copy.deepcopy(example_portfolios)\n     ```\n   - This removal ensures that `portfolios` are only initialized once via the `initialize_session_state` function.\n\n5. **Save the File:**\n   - After making the replacements and deletions, **save** `portfolio_optimizer.py`.\n\n---\n\n### **Section 2: Consolidate and Generalize Plotting Functions**\n\n**Issue Identified:**\nThe `plot_box` function is defined multiple times with slight variations, leading to redundancy. Consolidating these into a single, more flexible function will simplify the codebase.\n\n**Updated Code:**\n\n```python\ndef plot_box(\n    returns: Union[pd.Series, pd.DataFrame],\n    title: str = \"Returns Distribution Box Plot\",\n    labels: dict = {\"Return\": \"Returns (%)\", \"Asset\": \"Asset\"},\n    color_sequence: list = px.colors.qualitative.Bold,\n    multiple: bool = False,\n    portfolio_names: list = None\n):\n    \"\"\"\n    Generic function to plot box plots for portfolio returns.\n\n    Parameters:\n    - returns (pd.DataFrame or pd.Series): Returns data.\n    - title (str): Title of the plot.\n    - labels (dict): Axis labels.\n    - color_sequence (list): Colors for the boxes.\n    - multiple (bool): If True, handle multiple portfolios.\n    - portfolio_names (list): Names of the portfolios for labeling.\n    \"\"\"\n    if isinstance(returns, pd.DataFrame):\n        if multiple and portfolio_names:\n            for i, col in enumerate(returns.columns):\n                fig = px.box(\n                    returns[[col]].reset_index(drop=True),\n                    y=col,\n                    title=f\"{title} for {portfolio_names[i]}\",\n                    labels=labels,\n                    points='all',\n                    template=THEME,\n                    color_discrete_sequence=[color_sequence[i % len(color_sequence)]]\n                )\n                fig.update_traces(boxmean='sd')\n                fig.update_layout(xaxis_title=\"Assets\", yaxis_title=labels.get('Return', 'Returns (%)'))\n                st.plotly_chart(fig, use_container_width=True)\n        else:\n            melted_returns = returns.reset_index().melt(id_vars=returns.index.name if returns.index.name else 'Date', var_name='Asset', value_name='Return')\n            fig = px.box(\n                melted_returns,\n                x='Asset',\n                y='Return',\n                title=title,\n                labels=labels,\n                points='all',\n                template=THEME,\n                color='Asset',\n                color_discrete_sequence=color_sequence\n            )\n    elif isinstance(returns, pd.Series):\n        fig = px.box(\n            returns.rename(\"Return\"),\n            y='Return',\n            title=title,\n            labels=labels,\n            points='all',\n            template=THEME,\n            color_discrete_sequence=['cyan']\n        )\n    else:\n        st.warning(\"Returns data is neither a DataFrame nor a Series.\")\n        return\n\n    fig.update_traces(boxmean='sd')\n    fig.update_layout(\n        xaxis_title=labels.get(\"Asset\", \"Assets\"),\n        yaxis_title=labels.get(\"Return\", \"Returns (%)\"),\n        legend_title=\"Assets\"\n    )\n    st.plotly_chart(fig, use_container_width=True)\n\n    st.markdown(\"\"\"\n        **\ud83d\udcd6 Interpretation:**\n        - **Boxes:** Represent the interquartile range (IQR) where the middle 50% of the data lies.\n        - **Median Line:** Indicates the median return.\n        - **Whiskers:** Extend to show the range of the data excluding outliers.\n        - **Outliers:** Individual points outside the whiskers represent atypical returns.\n        - **Box Mean (Shaded):** Shows the mean and standard deviation of returns.\n    \"\"\")\n```\n\n**Instructions for Replacement:**\n\n1. **Open the File:**\n   - Ensure `portfolio_optimizer.py` is open in your text editor or IDE.\n\n2. **Locate Existing `plot_box` Functions:**\n   - Search for all existing definitions of the `plot_box` function. These are typically found in the **Helper Functions** section.\n\n3. **Delete Redundant Functions:**\n   - **Remove** all current implementations of `plot_box` to eliminate redundancy.\n\n4. **Insert the Consolidated Function:**\n   - **Paste** the updated `plot_box` function provided above into the **Helper Functions** section.\n\n5. **Adjust Function Calls (If Necessary):**\n   - Review where `plot_box` is called within your code (e.g., `display_backtest_results`, `display_portfolio_vs_benchmark`, etc.). Ensure that the function calls now include parameters like `multiple=True` and `portfolio_names` when plotting multiple portfolios.\n   - **Example Adjustment:**\n     ```python\n     plot_box(\n         returns=returns,\n         title=\"\ud83e\udd67 Optimized Allocation\",\n         multiple=False  # Set to True if plotting multiple portfolios\n     )\n     ```\n   - Modify these calls as needed to utilize the new parameters effectively.\n\n6. **Save the File:**\n   - After replacing the functions and adjusting any necessary calls, **save** `portfolio_optimizer.py`.\n\n---\n\n### **Additional Recommendations:**\n\n1. **Consistent Naming Conventions:**\n   - Ensure that all function names, variable names, and parameters follow a consistent naming convention (e.g., snake_case) to enhance readability.\n\n2. **Group Related Functions:**\n   - Organize helper functions into logical groups with clear comment headers. For example, group all plotting functions together, all metric calculations together, etc. This structure aids in navigation and maintenance.\n\n3. **Remove Unused Imports:**\n   - Review the import statements at the beginning of your file and remove any that are no longer necessary after refactoring. This reduces clutter and potential confusion.\n\n4. **Modularize Code (Optional):**\n   - For a larger project, consider breaking down the code into multiple modules or files. For instance, separate helper functions, plotting utilities, and core logic into distinct Python files. This modular approach improves scalability and manageability.\n\n5. **Document Changes:**\n   - Update any existing documentation or comments to reflect the refactored code. Clear documentation assists future development and onboarding of new contributors.\n\n6. **Test Thoroughly:**\n   - After refactoring, run your Streamlit app to ensure all functionalities operate as expected. Pay special attention to plotting sections and session state behaviors to confirm that the consolidation hasn't introduced any issues.\n\n---\n\nBy implementing these targeted refactorings, your `portfolio_optimizer.py` will become more organized, efficient, and easier to manage. Remember to back up your original file before making extensive changes to prevent any accidental data loss.\n\nIf you encounter specific sections or functions that still appear disorganized after these changes, feel free to seek further assistance!","behavior":"degenerate_repetition","conversation_hash":"58f695833926991940478b909bb7f34d","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":184316,"n_turns_in_conversation":1,"n_words":14730,"source_row_offset":2177,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:47:10 GMT","turn_id":"944dfb525984cb2ca45729d2","turn_index":0,"user_text":"%%writefile portfolio_optimizer.py\nimport streamlit as st\nimport pandas as pd\nimport plotly.graph_objects as go\nimport numpy as np\nimport yfinance as yf\nimport statsmodels.api as sm\nimport datetime\nfrom datetime import datetime, timedelta\nfrom pandas_datareader import data as pdr\nimport plotly.express as px\nimport warnings, json, requests, copy\nfrom requests.adapters import HTTPAdapter\nfrom urllib3.util.retry import Retry\nfrom scipy.stats import skew, kurtosis, norm\nfrom joblib import Parallel, delayed\nfrom sklearn.linear_model import LinearRegression\nfrom statsmodels.stats.stattools import durbin_watson\nfrom functools import lru_cache, wraps\nfrom concurrent.futures import ProcessPoolExecutor\nfrom scipy.optimize import minimize\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\n\nwarnings.filterwarnings('ignore')\n\n# ----------------------------\n# Frequency Mapping\n# ----------------------------\nfrequency_mapping = {\n    \"Daily\": \"D\",\n    \"Weekly\": \"W\",\n    \"Monthly\": \"M\",\n    \"Quarterly\": \"Q\",\n    \"Yearly\": \"Y\"\n}\n\n# Inverse mapping to get frequency name from code\nrebalance_freq_inverse_mapping = {v: k for k, v in frequency_mapping.items()}\n\n# ----------------------------\n# Example Portfolios Configuration\n# ----------------------------\n\ndef create_example_portfolios() -> list:\n    \"\"\"\n    Creates a list of predefined example portfolios.\n    \n    Returns:\n        list: A list of dictionaries, each representing a portfolio.\n    \"\"\"\n    today = pd.to_datetime(datetime.today())\n    example_definitions = [\n        {\n            'name': \"Retirement Portfolio\",\n            'years': 10,\n            'benchmark_symbol': '^GSPC',\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'MSFT', 'GOOGL', 'JPM', 'XOM'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Tech-Heavy Portfolio\",\n            'years': 5,\n            'benchmark_symbol': '^IXIC',\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META'],\n            'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]\n        },\n        {\n            'name': \"Balanced Risk-Return Portfolio\",\n            'years': 7,\n            'benchmark_symbol': '^GSPC',\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'JNJ', 'V', 'PG', 'XOM'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Income-Focused Portfolio\",\n            'years': 7,\n            'benchmark_symbol': '^DJI',\n            'rebalance_freq': 'Q',\n            'selected': ['PG', 'KO', 'JNJ', 'T', 'PFE'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Growth-Oriented Portfolio\",\n            'years': 3,\n            'benchmark_symbol': '^IXIC',\n            'rebalance_freq': 'M',\n            'selected': ['TSLA', 'NVDA', 'AMD', 'META', 'NFLX'],\n            'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]\n        },\n        {\n            'name': \"Value Portfolio\",\n            'years': 10,\n            'benchmark_symbol': '^DJI',\n            'rebalance_freq': 'Q',\n            'selected': ['KO', 'PFE', 'XOM', 'WMT', 'CVX'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Dividend-Focused Portfolio\",\n            'years': 7,\n            'benchmark_symbol': 'DVY',\n            'rebalance_freq': 'Q',\n            'selected': ['T', 'VZ', 'PFE', 'KO', 'IBM'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Global Diversification Portfolio\",\n            'years': 5,\n            'benchmark_symbol': 'ACWI',\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'TSM', 'BABA', 'SAP', 'UL'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"High-Risk Growth Portfolio\",\n            'years': 3,\n            'benchmark_symbol': '^IXIC',\n            'rebalance_freq': 'M',\n            'selected': ['TSLA', 'ARKK', 'NVDA', 'SHOP', 'CRWD'],\n            'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]\n        },\n        {\n            'name': \"Emerging Markets Portfolio\",\n            'years': 5,\n            'benchmark_symbol': 'EEM',\n            'rebalance_freq': 'M',\n            'selected': ['BABA', 'TSM', 'PDD', 'INFY', 'VALE'],\n            'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]\n        }\n    ]\n    \n    portfolios = []\n    for port_def in example_definitions:\n        portfolio = {\n            'name': port_def['name'],\n            'start_date': today - timedelta(days=365 * port_def['years']),\n            'end_date': today,\n            'rf_rate': 0.02,\n            'broker_fee': 0.0,\n            'benchmark_symbol': port_def['benchmark_symbol'],\n            'rebalance_freq': port_def['rebalance_freq'],\n            'selected': port_def['selected'],\n            'allocations': port_def['allocations']\n        }\n        portfolios.append(portfolio)\n    \n    return portfolios\n\n# Initialize example portfolios if not already in session state\nif 'portfolios' not in st.session_state or not st.session_state.portfolios:\n    st.session_state.portfolios = create_example_portfolios()\n# ----------------------------\n# Global Settings and Configurations\n# ----------------------------\n\n# Suppress only the InsecureRequestWarning, if bypassing SSL\nwarnings.simplefilter('ignore', InsecureRequestWarning)\nrequests.packages.urllib3.disable_warnings()  # Use cautiously\n# ----------------------------\n# Global Theme Configuration\n# ----------------------------\nTHEME = 'plotly_dark'  # Options: 'plotly_dark', 'plotly_white', 'seaborn', etc.\n# ----------------------------\n# Global Plot Layout Configuration\n# ----------------------------\nFIG_HEIGHT = 600\nHOVERMODE = 'x unified'\n\n# ----------------------------\n# Session State Initialization\n# ----------------------------\n\ndef initialize_session_state():\n    \"\"\"\n    Initializes Streamlit session state with default values.\n    \"\"\"\n    st.session_state.setdefault('portfolios', copy.deepcopy(create_example_portfolios()))\n    st.session_state.setdefault('backtest_results', {})\n    st.session_state.setdefault('step', \"Configure Portfolio\")\n    st.session_state.setdefault('edit_portfolio', None)\n    st.session_state.setdefault('add_new_portfolio', False)\n    st.session_state.setdefault('default_config', {\n        'rf_rate': 0.02,\n        'broker_fee': 0.0,\n        'benchmark_symbol': '^GSPC'\n    })\n\n# Initialize session state\ninitialize_session_state()\n\n# Initialize example portfolios if not already in session state\nif 'portfolios' not in st.session_state or not st.session_state.portfolios:\n    st.session_state.portfolios = copy.deepcopy(example_portfolios)\n\n# ----------------------------\n# Utility Decorators\n# ----------------------------\n\ndef handle_exceptions(func):\n    \"\"\"\n    Decorator to handle exceptions in Streamlit applications.\n    Displays errors using Streamlit's error message system.\n    \"\"\"\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n        try:\n            return func(*args, **kwargs)\n        except Exception as e:\n            st.error(f\"Error in {func.__name__}: {e}\")\n            return None\n    return wrapper\n\n# ----------------------------\n# Helper Functions\n# ----------------------------\n\n@handle_exceptions\ndef get_company_name(ticker_df: pd.DataFrame, ticker: str) -> str:\n    \"\"\"\n    Retrieve the company name for a given ticker.\n\n    Parameters:\n    - ticker_df (pd.DataFrame): DataFrame containing 'Ticker' and 'Company Name' columns.\n    - ticker (str): Stock ticker symbol.\n\n    Returns:\n    - str: Company name or \"Unknown\" if not found.\n    \"\"\"\n    match = ticker_df[ticker_df['Ticker'] == ticker]\n    if not match.empty:\n        return match.iloc[0]['Company Name']\n    return \"Unknown\"\n\ndef format_asset_option(ticker: str, company_name: str) -> str:\n    \"\"\"\n    Format the asset option display string.\n\n    Parameters:\n    - ticker (str): Stock ticker symbol.\n    - company_name (str): Name of the company.\n\n    Returns:\n    - str: Formatted string combining ticker and company name.\n    \"\"\"\n    return f\"{ticker} - {company_name}\"\n\ndef handle_portfolio_data(portfolio, date_range=None):\n    \"\"\"\n    Helper function to download data and handle missing tickers.\n\n    Parameters:\n    - portfolio (dict): Portfolio details.\n    - date_range (tuple, optional): (start_date, end_date)\n\n    Returns:\n    - available_selected (list): Tickers with available data.\n    - missing_selected (set): Tickers missing data.\n    - price_data (pd.DataFrame): Adjusted close prices.\n    \"\"\"\n    start_date = date_range[0] if date_range else portfolio['start_date']\n    end_date = date_range[1] if date_range else portfolio['end_date']\n    price_data = download_data(portfolio['selected'], start_date, end_date)\n    if price_data.empty:\n        return [], set(portfolio['selected']), price_data\n    available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n    missing_selected = set(portfolio['selected']) - set(available_selected)\n    if missing_selected:\n        st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n    return available_selected, missing_selected, price_data\n\ndef run_portfolio_comparison(comparison_type, portfolio1, portfolio2=None):\n        \"\"\"\n        Generalized function to run portfolio comparisons.\n\n        Parameters:\n        - comparison_type (str): \"vs_benchmark\" or \"vs_portfolio\"\n        - portfolio1 (dict): Primary portfolio.\n        - portfolio2 (dict, optional): Secondary portfolio for \"vs_portfolio\".\n\n        Returns:\n        - result (dict): Backtest results or error.\n        \"\"\"\n        try:\n            if comparison_type == \"vs_benchmark\":\n                available, missing, price_data = handle_portfolio_data(portfolio1)\n                if not available:\n                    return {\"error\": \"No valid tickers with available data.\"}\n                allocations = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available])\n                returns, cum_returns = backtest(\n                    weights=np.array(allocations) / 100,\n                    prices=price_data[available],\n                    rebalance_freq=portfolio1['rebalance_freq'],\n                    broker_fee=portfolio1['broker_fee']\n                )\n                if cum_returns.empty:\n                    return {\"error\": \"Cumulative returns are empty.\"}\n                # Benchmark processing...\n                benchmark_data = download_data(\n                    [portfolio1['benchmark_symbol']],\n                    portfolio1['start_date'].strftime('%Y-%m-%d'),\n                    portfolio1['end_date'].strftime('%Y-%m-%d')\n                )\n                if benchmark_data.empty:\n                    return {\"error\": \"Benchmark data could not be downloaded.\"}\n                \n                benchmark_returns, benchmark_cum_returns, benchmark_metrics = process_benchmark(benchmark_data, returns, portfolio1)\n                portfolio_metrics = calculate_metrics(returns, cum_returns, portfolio1['rf_rate'], benchmark_returns)\n                \n                # Compile backtest results\n                backtest_results = {\n                    'returns': returns,\n                    'cum_returns': cum_returns,\n                    'weights': allocations,\n                    'price_data': price_data[available],\n                    'metrics': portfolio_metrics,\n                    'benchmark_returns': benchmark_returns,\n                    'benchmark_cum_returns': benchmark_cum_returns,\n                    'benchmark_metrics': benchmark_metrics\n                }\n                \n                # **Added Return Statement**\n                return {\"backtest_results\": backtest_results}\n    \n            elif comparison_type == \"vs_portfolio\":\n                overlap_start = max(portfolio1['start_date'], portfolio2['start_date'])\n                overlap_end = min(portfolio1['end_date'], portfolio2['end_date'])\n                if overlap_start >= overlap_end:\n                    return {\"error\": \"No overlapping date ranges.\"}\n                available_a, missing_a, price_data_a = handle_portfolio_data(portfolio1, (overlap_start, overlap_end))\n                available_b, missing_b, price_data_b = handle_portfolio_data(portfolio2, (overlap_start, overlap_end))\n                if not available_a or not available_b:\n                    return {\"error\": \"One of the portfolios has no valid tickers with available data.\"}\n                allocations_a = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available_a])\n                allocations_b = adjust_allocations([portfolio2['allocations'][portfolio2['selected'].index(t)] for t in available_b])\n                returns_a, cum_returns_a = backtest(\n                    weights=np.array(allocations_a) / 100,\n                    prices=price_data_a[available_a],\n                    rebalance_freq=portfolio1['rebalance_freq'],\n                    broker_fee=portfolio1['broker_fee']\n                )\n                returns_b, cum_returns_b = backtest(\n                    weights=np.array(allocations_b) / 100,\n                    prices=price_data_b[available_b],\n                    rebalance_freq=portfolio2['rebalance_freq'],\n                    broker_fee=portfolio2['broker_fee']\n                )\n                if cum_returns_a.empty or cum_returns_b.empty:\n                    return {\"error\": \"One of the cumulative returns is empty.\"}\n                common_index = cum_returns_a.index.intersection(cum_returns_b.index)\n                if common_index.empty:\n                    return {\"error\": \"No overlapping dates after backtesting.\"}\n                metrics_a = calculate_metrics(returns_a.loc[common_index], cum_returns_a.loc[common_index], portfolio1['rf_rate'])\n                metrics_b = calculate_metrics(returns_b.loc[common_index], cum_returns_b.loc[common_index], portfolio2['rf_rate'])\n                backtest_results = {\n                    'returns_a': returns_a.loc[common_index],\n                    'cum_returns_a': cum_returns_a.loc[common_index],\n                    'weights_a': allocations_a,\n                    'price_data_a': price_data_a[available_a],\n                    'metrics_a': metrics_a,\n                    'returns_b': returns_b.loc[common_index],\n                    'cum_returns_b': cum_returns_b.loc[common_index],\n                    'weights_b': allocations_b,\n                    'price_data_b': price_data_b[available_b],\n                    'metrics_b': metrics_b,\n                    'benchmark_cum_returns': pd.Series(dtype=float)\n                }\n                return {\"backtest_results\": backtest_results}\n        except Exception as e:\n            return {\"error\": f\"An unexpected error occurred: {e}\"}\n\ndef adjust_allocations(allocations):\n    \"\"\"Adjust allocations to sum to 100% if necessary.\"\"\"\n    total = sum(allocations)\n    if not np.isclose(total, 100.0, atol=1e-4):\n        st.warning(\"Allocations do not sum to 100%. Adjusting allocations proportionally.\")\n        allocations = [a / total * 100 for a in allocations]\n    return allocations\n\ndef process_portfolio_allocations(portfolio, price_data):\n    \"\"\"Processes portfolio allocations and handles missing tickers.\"\"\"\n    available = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n    missing = set(portfolio['selected']) - set(available)\n    if missing:\n        st.warning(f\"Excluded tickers from '{portfolio['name']}': {', '.join(missing)}\")\n    if not available:\n        st.error(f\"No valid tickers for portfolio '{portfolio['name']}' in the overlapping period.\")\n        st.stop()\n    allocations = [portfolio['allocations'][i] for i, ticker in enumerate(portfolio['selected']) if ticker in available]\n    allocations = adjust_allocations(allocations)\n    return allocations, available, missing\n\ndef initialize_benchmark_metrics():\n    \"\"\"Initializes benchmark metrics when benchmark data is unavailable.\"\"\"\n    metrics = {metric: \"N/A\" for metric in [\n        'Start Balance', 'End Balance', 'Annualized Return (CAGR)', 'Best Year', 'Worst Year',\n        'Arithmetic Mean (Monthly)', 'Arithmetic Mean (Annualized)', 'Geometric Mean (Monthly)',\n        'Geometric Mean (Annualized)', 'Standard Deviation (Monthly)', 'Standard Deviation (Annualized)',\n        'Downside Deviation (Monthly)', 'Maximum Drawdown', 'Sharpe Ratio', 'Sortino Ratio',\n        'Gain/Loss Ratio', 'Skewness', 'Excess Kurtosis', 'Safe Withdrawal Rate',\n        'Perpetual Withdrawal Rate', 'Positive Periods', 'Benchmark Correlation', 'Beta',\n        'Alpha (annualized)', 'R2', 'Treynor Ratio', 'Calmar Ratio',\n        'Modigliani\u2013Modigliani Measure', 'Information Ratio', 'Tracking Error',\n        'Active Return', 'Upside Capture Ratio', 'Downside Capture Ratio'\n    ]}\n    return None, pd.Series(dtype=float), metrics\n\ndef process_benchmark(benchmark_data, returns, portfolio):\n    \"\"\"Processes benchmark data and calculates benchmark metrics.\"\"\"\n    benchmark_returns = benchmark_data[portfolio['benchmark_symbol']].pct_change().dropna()\n    benchmark_returns = benchmark_returns.reindex(returns.index, method='ffill').dropna()\n    common_index = returns.index.intersection(benchmark_returns.index)\n    if common_index.empty:\n        st.error(\"No overlapping dates between portfolio returns and benchmark returns after alignment.\")\n        st.stop()\n    returns_aligned = returns.loc[common_index]\n    cum_returns_aligned = (1 + benchmark_returns.loc[common_index]).cumprod()\n    benchmark_metrics = calculate_metrics(\n        benchmark_returns, cum_returns_aligned, portfolio['rf_rate'], None\n    )\n    return benchmark_returns, cum_returns_aligned, benchmark_metrics\n\ndef create_new_portfolio():\n    \"\"\"Creates a new empty portfolio template with default values.\"\"\"\n    return {\n        'name': \"\",\n        'start_date': pd.to_datetime(datetime.today() - timedelta(days=365)),\n        'end_date': pd.to_datetime(datetime.today()),\n        'rf_rate': st.session_state.default_config['rf_rate'],\n        'broker_fee': st.session_state.default_config['broker_fee'],\n        'benchmark_symbol': st.session_state.default_config['benchmark_symbol'],\n        'rebalance_freq': 'M',\n        'selected': [],\n        'allocations': []\n    }\n\ndef configure_portfolio(portfolio):\n    \"\"\"Handles the configuration of a portfolio.\"\"\"\n    col1, col2 = st.columns(2)\n    \n    with col1:\n        portfolio['name'] = st.text_input(\"Portfolio Name\", value=portfolio.get('name', ''))\n        portfolio['start_date'] = st.date_input(\n            \"Start Date\",\n            value=portfolio.get('start_date', pd.to_datetime(datetime.today() - timedelta(days=365)))\n        )\n        portfolio['rf_rate'] = st.number_input(\n            \"Risk-Free Rate (%)\",\n            value=portfolio.get('rf_rate', 0.02) * 100,\n            step=0.1\n        ) / 100\n        portfolio['rebalance_freq'] = st.selectbox(\n            \"Rebalancing Frequency\",\n            options=list(frequency_mapping.keys()),\n            index=list(frequency_mapping.values()).index(portfolio.get('rebalance_freq', 'M'))\n        )\n    \n    with col2:\n        portfolio['benchmark_symbol'] = st.text_input(\n            \"Benchmark Symbol\",\n            value=portfolio.get('benchmark_symbol', '^GSPC')\n        )\n        portfolio['end_date'] = st.date_input(\n            \"End Date\",\n            value=portfolio.get('end_date', pd.to_datetime(datetime.today()))\n        )\n        portfolio['broker_fee'] = st.number_input(\n            \"Broker Fee (%)\",\n            value=portfolio.get('broker_fee', 0.0) * 100,\n            step=0.01\n        ) / 100\n    \n    return portfolio\n\ndef update_session_state(updates: dict):\n    \"\"\"\n    Helper function to update Streamlit session state.\n\n    Parameters:\n    - updates (dict): Dictionary of key-value pairs to update in session state.\n    \"\"\"\n    for key, value in updates.items():\n        st.session_state[key] = value\n\ndef display_backtest_results(results, comparison_type, portfolio1, portfolio2):\n    \"\"\"Displays the backtest results in the Streamlit app.\"\"\"\n    if comparison_type == \"Portfolio vs Benchmark\":\n        display_portfolio_vs_benchmark(results)\n    else:\n        display_portfolio_vs_portfolio(results, portfolio1, portfolio2)\n\ndef display_portfolio_vs_benchmark(results):\n    \"\"\"Displays results for Portfolio vs Benchmark comparison.\"\"\"\n    returns = results['returns']\n    cum_returns = results['cum_returns']\n    weights = results['weights']\n    price_data = results['price_data']\n    metrics = results['metrics']\n    benchmark_metrics = results['benchmark_metrics']\n    benchmark_cum_returns = results['benchmark_cum_returns']\n\n    # Key Metrics\n    st.markdown(\"### \ud83d\udd11 Key Metrics\")\n    key_metrics = {\n        'Annualized Return (CAGR)': metrics.get('Annualized Return (CAGR)', \"N/A\"),\n        'Sharpe Ratio': metrics.get('Sharpe Ratio', \"N/A\"),\n        'Maximum Drawdown': metrics.get('Maximum Drawdown', \"N/A\")\n    }\n    cols = st.columns(len(key_metrics))\n    for col, (metric, value) in zip(cols, key_metrics.items()):\n        col.metric(label=metric, value=value)\n\n    # Tabs for Organized Sections\n    tabs = st.tabs([\"Overview\", \"Performance Statistics\", \"Advanced Metrics\", \"Drawdowns\", \"Visualizations\"])\n\n    with tabs[0]:\n        st.header(\"\ud83d\udcc8 Portfolio Performance Overview\")\n        st.write(f\"**Portfolio Name:** {portfolio1}\")\n        st.write(f\"**Start Date:** {price_data.index.min().strftime('%Y-%m-%d')}\")\n        st.write(f\"**End Date:** {price_data.index.max().strftime('%Y-%m-%d')}\")\n        st.write(f\"**Benchmark:** {results.get('benchmark_symbol', 'N/A')}\")\n\n    with tabs[1]:\n        st.header(\"\ud83d\udcca Performance Statistics\")\n        st.subheader(\"\ud83d\udee0\ufe0f Basic Metrics\")\n        performance_df_stats = create_performance_stats_df(metrics, benchmark_metrics)\n        st.table(performance_df_stats)\n\n    with tabs[2]:\n        st.header(\"\ud83d\udccb Detail Comparisons\")\n        st.subheader(\"\ud83e\uddee Advanced Metrics\")\n        performance_df_advanced = create_advanced_metrics_df(metrics, benchmark_metrics)\n        st.table(performance_df_advanced)\n        # Add Explanation for \"N/A\" Metrics\n        st.write(\"\"\"\n        **\u2139\ufe0f Note:** Metrics marked as \"N/A\" indicate that they are specific to the portfolio and are not applicable to the benchmark. \n        This is because these metrics require individual portfolio analysis and do not apply to benchmark-only data.\n        \"\"\")\n        st.subheader(\"\ud83d\udd0d Risk Factor Attribution Analysis\")\n        perform_risk_factor_attribution(returns)\n\n    with tabs[3]:\n        st.header(\"\ud83d\udcc9 Detailed Drawdowns\")\n        display_drawdowns(cum_returns, benchmark_cum_returns)\n\n    with tabs[4]:\n        st.header(\"\ud83d\udcca Visualizations\")\n        display_visualizations(cum_returns, benchmark_cum_returns, returns, price_data, weights, metrics, benchmark_metrics)\n\n    update_session_state({\n        'backtest_results': results,\n        'show_proceed': True \n    })\n    st.success(\"Backtest completed!\")\n\ndef display_portfolio_vs_portfolio(results, portfolio1, portfolio2):\n    \"\"\"Displays results for Portfolio vs Portfolio comparison.\"\"\"\n    returns_a = results['returns_a']\n    cum_returns_a = results['cum_returns_a']\n    weights_a = results['weights_a']\n    price_data_a = results['price_data_a']\n    metrics_a = results['metrics_a']\n\n    returns_b = results['returns_b']\n    cum_returns_b = results['cum_returns_b']\n    weights_b = results['weights_b']\n    price_data_b = results['price_data_b']\n    metrics_b = results['metrics_b']\n\n    # Key Metrics with Additional Metrics\n    st.markdown(\"### \ud83d\udd11 Key Metrics\")\n    key_metrics = {\n        'Metric': [\n            'Annualized Return (CAGR)', \n            'Sharpe Ratio', \n            'Maximum Drawdown',\n            'Benchmark Correlation',\n            'Beta'\n        ],\n        portfolio1: [\n            metrics_a.get('Annualized Return (CAGR)', \"N/A\"),\n            metrics_a.get('Sharpe Ratio', \"N/A\"),\n            metrics_a.get('Maximum Drawdown', \"N/A\"),\n            metrics_a.get('Benchmark Correlation', \"N/A\"),\n            metrics_a.get('Beta', \"N/A\")\n        ],\n        portfolio2: [\n            metrics_b.get('Annualized Return (CAGR)', \"N/A\"),\n            metrics_b.get('Sharpe Ratio', \"N/A\"),\n            metrics_b.get('Maximum Drawdown', \"N/A\"),\n            metrics_b.get('Benchmark Correlation', \"N/A\"),\n            metrics_b.get('Beta', \"N/A\")\n        ]\n    }\n    metrics_df = pd.DataFrame(key_metrics)\n    metrics_df.set_index('Metric', inplace=True)\n    st.table(metrics_df)\n\n    # Tabs for Organized Sections with Enhanced Content\n    tabs = st.tabs([\"Overview\", \"Performance Statistics\", \"Advanced Metrics\", \"Drawdowns\", \"Visualizations\"])\n\n    with tabs[0]:\n        st.header(\"\ud83d\udcc8 Portfolio Performance Overview\")\n        st.write(f\"**Portfolio A Name:** {portfolio1}\")\n        st.write(f\"**Portfolio B Name:** {portfolio2}\")\n        st.write(f\"**Start Date:** {min(returns_a.index.min(), returns_b.index.min()).strftime('%Y-%m-%d')}\")\n        st.write(f\"**End Date:** {max(returns_a.index.max(), returns_b.index.max()).strftime('%Y-%m-%d')}\")\n        st.write(f\"**Benchmark Correlation:** {metrics_a.get('Benchmark Correlation', 'N/A')}\")\n        st.write(f\"**Beta of {portfolio1} relative to {portfolio2}:** {metrics_a.get('Beta', 'N/A')}\")\n\n    with tabs[1]:\n        st.header(\"\ud83d\udcca Performance Statistics\")\n        st.subheader(\"\ud83d\udee0\ufe0f Basic Metrics\")\n        performance_df_stats = create_performance_stats_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2)\n        st.table(performance_df_stats)\n\n    with tabs[2]:\n        st.header(\"\ud83d\udcc9 Advanced Metrics\")\n        performance_df_advanced = create_advanced_metrics_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2)\n        st.table(performance_df_advanced)\n        # Add Explanation for \"N/A\" Metrics\n        st.write(\"\"\"\n        **\u2139\ufe0f Note:** Metrics marked as \"N/A\" indicate that certain data was unavailable or not applicable \n        during the comparison between the two portfolios. Ensure both portfolios have sufficient data \n        to calculate all metrics.\n        \"\"\")\n\n    with tabs[3]:\n        st.header(\"\ud83d\udcc9 Drawdowns\")\n        st.markdown(\"### \ud83d\udcc9 Detailed Drawdowns for Both Portfolios\")\n        st.markdown(f\"#### \ud83d\udcc9 Drawdowns for {portfolio1}\")\n        drawdowns_a = get_drawdown_details(cum_returns_a)\n        if drawdowns_a:\n            st.table(pd.DataFrame(drawdowns_a))\n        else:\n            st.write(\"No drawdowns detected for this portfolio.\")\n\n        st.markdown(f\"#### \ud83d\udcc9 Drawdowns for {portfolio2}\")\n        drawdowns_b = get_drawdown_details(cum_returns_b)\n        if drawdowns_b:\n            st.table(pd.DataFrame(drawdowns_b))\n        else:\n            st.write(\"No drawdowns detected for this portfolio.\")\n\n    with tabs[4]:\n        st.header(\"\ud83d\udcca Visualizations\")\n        display_visualizations_portfolio_vs_portfolio(\n        cum_returns_a, cum_returns_b, returns_a, returns_b,\n        price_data_a, price_data_b, weights_a, weights_b,\n        portfolio1, portfolio2, metrics_a, metrics_b\n    )\n\n    # Store results\n    update_session_state({\n        'backtest_results': results,\n        'show_proceed': True\n    })\n    st.success(\"Backtest completed!\")\n\ndef create_performance_stats_df(metrics, benchmark_metrics):\n    \"\"\"Creates a DataFrame for performance statistics.\"\"\"\n    return pd.DataFrame({\n        'Metric': [\n            'Start Balance', 'End Balance', 'Annualized Return (CAGR)',\n            'Standard Deviation (Annualized)', 'Best Year', 'Worst Year',\n            'Maximum Drawdown', 'Sharpe Ratio', 'Sortino Ratio', 'Benchmark Correlation'\n        ],\n        'Portfolio': [\n            metrics.get('Start Balance', \"N/A\"),\n            metrics.get('End Balance', \"N/A\"),\n            metrics.get('Annualized Return (CAGR)', \"N/A\"),\n            metrics.get('Standard Deviation (Annualized)', \"N/A\"),\n            metrics.get('Best Year', \"N/A\"),\n            metrics.get('Worst Year', \"N/A\"),\n            metrics.get('Maximum Drawdown', \"N/A\"),\n            metrics.get('Sharpe Ratio', \"N/A\"),\n            metrics.get('Sortino Ratio', \"N/A\"),\n            metrics.get('Benchmark Correlation', \"N/A\")\n        ],\n        'Benchmark': [\n            benchmark_metrics.get('Start Balance', \"N/A\"),\n            benchmark_metrics.get('End Balance', \"N/A\"),\n            benchmark_metrics.get('Annualized Return (CAGR)', \"N/A\"),\n            benchmark_metrics.get('Standard Deviation (Annualized)', \"N/A\"),\n            benchmark_metrics.get('Best Year', \"N/A\"),\n            benchmark_metrics.get('Worst Year', \"N/A\"),\n            benchmark_metrics.get('Maximum Drawdown', \"N/A\"),\n            benchmark_metrics.get('Sharpe Ratio', \"N/A\"),\n            benchmark_metrics.get('Sortino Ratio', \"N/A\"),\n            benchmark_metrics.get('Benchmark Correlation', \"N/A\")\n        ]\n    }).set_index('Metric')\n\ndef create_advanced_metrics_df(metrics, benchmark_metrics):\n    \"\"\"Creates a DataFrame for advanced metrics.\"\"\"\n    return pd.DataFrame({\n        'Metric': list(metrics.keys()),\n        'Portfolio': list(metrics.values()),\n        'Benchmark': list(benchmark_metrics.values())\n    }).set_index('Metric')\n\ndef create_performance_stats_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2):\n    \"\"\"Creates a DataFrame for Portfolio vs Portfolio performance statistics.\"\"\"\n    return pd.DataFrame({\n        'Metric': [\n            'Start Balance', 'End Balance', 'Annualized Return (CAGR)',\n            'Standard Deviation (Annualized)', 'Best Year', 'Worst Year',\n            'Maximum Drawdown', 'Sharpe Ratio', 'Sortino Ratio', 'Benchmark Correlation'\n        ],\n        portfolio1: [\n            metrics_a.get('Start Balance', \"N/A\"),\n            metrics_a.get('End Balance', \"N/A\"),\n            metrics_a.get('Annualized Return (CAGR)', \"N/A\"),\n            metrics_a.get('Standard Deviation (Annualized)', \"N/A\"),\n            metrics_a.get('Best Year', \"N/A\"),\n            metrics_a.get('Worst Year', \"N/A\"),\n            metrics_a.get('Maximum Drawdown', \"N/A\"),\n            metrics_a.get('Sharpe Ratio', \"N/A\"),\n            metrics_a.get('Sortino Ratio', \"N/A\"),\n            metrics_a.get('Benchmark Correlation', \"N/A\")\n        ],\n        portfolio2: [\n            metrics_b.get('Start Balance', \"N/A\"),\n            metrics_b.get('End Balance', \"N/A\"),\n            metrics_b.get('Annualized Return (CAGR)', \"N/A\"),\n            metrics_b.get('Standard Deviation (Annualized)', \"N/A\"),\n            metrics_b.get('Best Year', \"N/A\"),\n            metrics_b.get('Worst Year', \"N/A\"),\n            metrics_b.get('Maximum Drawdown', \"N/A\"),\n            metrics_b.get('Sharpe Ratio', \"N/A\"),\n            metrics_b.get('Sortino Ratio', \"N/A\"),\n            metrics_b.get('Benchmark Correlation', \"N/A\")\n        ]\n    }).set_index('Metric')\n\ndef create_advanced_metrics_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2):\n    \"\"\"Creates a DataFrame for Portfolio vs Portfolio advanced metrics.\"\"\"\n    return pd.DataFrame({\n        'Metric': list(metrics_a.keys()),\n        portfolio1: list(metrics_a.values()),\n        portfolio2: list(metrics_b.values())\n    }).set_index('Metric')\n\ndef display_drawdowns(cum_returns, benchmark_cum_returns):\n    \"\"\"Displays drawdown details for portfolio and benchmark.\"\"\"\n    st.markdown(\"### \ud83d\udcc8 Drawdowns for Portfolio\")\n    portfolio_drawdowns = get_drawdown_details(cum_returns)\n    if portfolio_drawdowns:\n        st.table(pd.DataFrame(portfolio_drawdowns))\n    else:\n        st.write(\"No drawdowns detected for the portfolio.\")\n\n    if not benchmark_cum_returns.empty:\n        st.markdown(\"### \ud83d\udcc8 Drawdowns for Benchmark\")\n        benchmark_drawdowns = get_drawdown_details(benchmark_cum_returns)\n        if benchmark_drawdowns:\n            st.table(pd.DataFrame(benchmark_drawdowns))\n        else:\n            st.write(\"No drawdowns detected for the benchmark.\")\n    else:\n        st.write(\"Benchmark data not available for drawdown analysis.\")\n\ndef perform_risk_factor_attribution(returns):\n    \"\"\"Performs and displays risk factor attribution analysis.\"\"\"\n    start_date = returns.index.min().strftime('%Y-%m-%d')\n    end_date = returns.index.max().strftime('%Y-%m-%d')\n    factors = fetch_fama_french_factors(start_date, end_date)\n    if factors.empty:\n        st.write(\"Failed to retrieve factor data.\")\n        return\n    factors = factors.asfreq(returns.index.freq, method='ffill')\n    attribution, error = calculate_risk_factor_attribution(returns, factors)\n\n    if error:\n        st.error(error)\n        st.write(\"Risk factor attribution analysis is unavailable.\")\n    elif attribution.empty:\n        st.write(\"Risk factor attribution analysis is unavailable.\")\n    else:\n        st.write(\"Risk Factor Attribution:\")\n        st.table(attribution)\n        fig_attribution = px.bar(\n            attribution,\n            x='Factor',\n            y='Contribution (%)',\n            title='Risk Factor Attribution',\n            labels={'Contribution (%)': 'Contribution (%)'},\n            template=THEME\n        )\n        st.plotly_chart(fig_attribution, use_container_width=True)\n\ndef display_visualizations(cum_returns, benchmark_cum_returns, returns, price_data, weights, metrics, benchmark_metrics):\n    \"\"\"Displays an enhanced set of visualizations for Portfolio vs Benchmark with improved interactivity and aesthetics.\"\"\"\n    if not cum_returns.empty and not benchmark_cum_returns.empty:\n        plot_growth_comparison(cum_returns, benchmark_cum_returns, portfolio_name=\"Portfolio\", benchmark_name=\"Benchmark\")\n    else:\n        st.warning(\"Insufficient data to display Growth Comparison. Ensure both portfolio and benchmark have data.\")\n\n    plot_drawdown_comparison(\n        (cum_returns / cum_returns.expanding().max() - 1) * 100,\n        (benchmark_cum_returns / benchmark_cum_returns.expanding().max() - 1) * 100,\n        portfolio_name=\"Portfolio\",\n        benchmark_name=\"Benchmark\"\n    )\n\n    st.subheader(\"\ud83d\udcc8 Compound Annual Growth Rate (CAGR) Over Time\")\n    if st.session_state.selected_time_frames:\n        plot_cagr_over_time(cum_returns, time_frames=st.session_state.selected_time_frames)\n    else:\n        st.warning(\"Please select at least one time frame for CAGR.\")\n\n    st.subheader(\"\ud83d\udce6 Returns Distribution Box Plot\")\n    plot_box(returns)\n\n    st.subheader(\"\ud83d\udd25 Correlation Heatmap of Assets\")\n    plot_correlation_heatmap(price_data.pct_change().dropna())\n\n    st.subheader(\"\ud83d\udd0d Risk-Return Attribution Analysis\")\n    plot_risk_return_attribution(returns, weights)\n    \n    # **Corrected Rolling Beta Calculation**\n    st.subheader(\"\ud83d\udcc9 Rolling Beta Over Time\")\n    if not benchmark_cum_returns.empty:\n        # Align benchmark returns with portfolio returns\n        benchmark_returns = benchmark_cum_returns.pct_change().dropna().reindex(returns.index, method='ffill').dropna()\n        \n        # Calculate rolling covariance and variance\n        rolling_cov = returns.rolling(window=252).cov(benchmark_returns)\n        rolling_var = benchmark_returns.rolling(window=252).var()\n        \n        # Compute rolling beta\n        rolling_beta = rolling_cov / rolling_var\n        \n        # Align rolling_beta with returns\n        rolling_beta = rolling_beta.dropna()\n        \n        fig_rolling_beta = px.line(\n            rolling_beta,\n            title='Rolling Beta (1-Year Window)',\n            labels={'index': 'Date', 'value': 'Beta'},\n            template=THEME\n        )\n        st.plotly_chart(fig_rolling_beta, use_container_width=True)\n    else:\n        st.warning(\"Benchmark returns not available to plot Rolling Beta.\")\n\n    st.subheader(\"\ud83d\udd25 Cumulative Returns Heatmap\")\n    plot_cumulative_returns_heatmap(cum_returns)\n\n    st.subheader(\"\ud83e\udd67 Portfolio Allocation Pie Chart\")\n    plot_allocation_pie(weights, price_data.columns, title=\"\ud83e\udd67 Portfolio Allocation\", hover_info=\"percent+name\")\n\n    st.subheader(\"\ud83d\udcc9 Rolling Metrics\")\n    if st.session_state.selected_rolling_periods:\n        plot_rolling_metrics(returns, windows=st.session_state.selected_rolling_periods)\n    else:\n        st.warning(\"Please select at least one rolling period to display metrics.\")\n    # **Added: Rolling Sharpe Ratio Graph**\n    st.subheader(\"\ud83d\udcc8 Rolling Sharpe Ratio Over Time\")\n    if not benchmark_cum_returns.empty:\n        # Calculate rolling Sharpe Ratio\n        rolling_sharpe = (returns.rolling(window=252).mean() - st.session_state.default_config['rf_rate']) / returns.rolling(window=252).std()\n        rolling_sharpe = rolling_sharpe.dropna()\n        \n        fig_rolling_sharpe = px.line(\n            rolling_sharpe,\n            title='Rolling Sharpe Ratio (1-Year Window)',\n            labels={'index': 'Date', 'value': 'Sharpe Ratio'},\n            template=THEME\n        )\n        st.plotly_chart(fig_rolling_sharpe, use_container_width=True)\n    else:\n        st.warning(\"Benchmark returns not available to plot Rolling Sharpe Ratio.\")\n    st.markdown(\"### \ud83d\udca1 Recommendations\")\n    recommendations = generate_recommendations(\n        allocations=[alloc for alloc in weights if alloc > 0],\n        available_selected=price_data.columns\n    )\n    if recommendations:\n        for rec in recommendations:\n            st.markdown(f\"\u2022 {rec}\")\n    else:\n        st.success(\"\ud83c\udf89 Your portfolio allocations are well-balanced!\")\n\n    st.markdown(\"### \ud83d\udcdd Final Score\")\n    portfolio_score = calculate_final_score(metrics, benchmark_metrics)\n    st.metric(\"\ud83d\udcca Portfolio Score\", f\"{portfolio_score:.2f} / 100\")\n    # Add interpretation/comment similar to Risk Factor Attribution\n    st.markdown(\"\"\"\n    **\ud83d\udcd6 Interpretation:**\n    - The **Final Score** represents a comprehensive evaluation of your portfolio's performance based on selected metrics.\n    - A higher score indicates a better balance between risk and return, aligning with your investment objectives.\n    - Use this score to assess overall portfolio health and identify areas for improvement.\n    \"\"\")\n\ndef display_visualizations_portfolio_vs_portfolio(\n    cum_returns_a, cum_returns_b, returns_a, returns_b,\n    price_data_a, price_data_b, weights_a, weights_b,\n    portfolio1, portfolio2, metrics_a, metrics_b\n):\n    \"\"\"Displays various visualizations for Portfolio vs Portfolio.\"\"\"\n    # Growth Comparison\n    fig_growth = px.line(title='Growth Comparison')\n    fig_growth.add_scatter(x=cum_returns_a.index, y=cum_returns_a, mode='lines', name=portfolio1)\n    fig_growth.add_scatter(x=cum_returns_b.index, y=cum_returns_b, mode='lines', name=portfolio2)\n    st.plotly_chart(fig_growth, use_container_width=True)\n\n    # Drawdown Comparison\n    plot_drawdown_comparison(\n        (cum_returns_a / cum_returns_a.expanding().max() - 1) * 100,\n        (cum_returns_b / cum_returns_b.expanding().max() - 1) * 100\n    )\n\n    # Box Plot for Returns Distribution\n    st.subheader(\"\ud83d\udce6 Returns Distribution Box Plot\")\n    plot_box(returns_a, returns_b, portfolio1, portfolio2)\n\n    # Heatmap of Correlations Between Assets\n    st.subheader(\"\ud83d\udd25 Correlation Heatmap of Assets\")\n    plot_correlation_heatmap(price_data_a.pct_change().dropna())\n    plot_correlation_heatmap(price_data_b.pct_change().dropna())\n\n    # Portfolio Allocation Pie Chart\n    st.subheader(\"\ud83e\udd67 Portfolio Allocation Pie Chart\")\n    plot_allocation_pie(weights_a, price_data_a.columns, title=f\"{portfolio1} Allocation\", hover_info=\"percent+name\")\n    plot_allocation_pie(weights_b, price_data_b.columns, title=f\"{portfolio2} Allocation\", hover_info=\"percent+name\")\n\n    st.markdown(\"### \ud83d\udcdd Final Score\")\n    score_a = calculate_final_score(metrics_a, metrics_b)\n    score_b = calculate_final_score(metrics_b, metrics_a)\n    st.write(f\"**{portfolio1}:** {score_a:.2f} / 100\")\n    st.write(f\"**{portfolio2}:** {score_b:.2f} / 100\")\n\ndef plot_box(\n    returns, \n    title=\"Returns Distribution Box Plot\", \n    labels={\"Return\": \"Returns (%)\", \"Asset\": \"Asset\"}, \n    color_sequence=px.colors.qualitative.Bold,\n    multiple=False,\n    portfolio_names=None\n):\n    \"\"\"\n    Generic function to plot box plots for portfolio returns.\n\n    Parameters:\n    - returns (pd.DataFrame or pd.Series): Returns data.\n    - title (str): Title of the plot.\n    - labels (dict): Axis labels.\n    - color_sequence (list): Colors for the boxes.\n    - multiple (bool): If True, handle multiple portfolios.\n    - portfolio_names (list): Names of the portfolios for labeling.\n    \"\"\"\n    if isinstance(returns, pd.DataFrame):\n        if multiple and portfolio_names:\n            for i, col in enumerate(returns.columns):\n                fig = px.box(\n                    returns[[col]].reset_index(drop=True),\n                    y=col,\n                    title=f\"{title} for {portfolio_names[i]}\",\n                    labels=labels,\n                    points='all',\n                    template=THEME,\n                    color_discrete_sequence=[color_sequence[i % len(color_sequence)]]\n                )\n                fig.update_traces(boxmean='sd')\n                fig.update_layout(xaxis_title=\"Assets\", yaxis_title=labels.get('Return', 'Returns (%)'))\n                st.plotly_chart(fig, use_container_width=True)\n        else:\n            melted_returns = returns.reset_index().melt(id_vars=returns.index.name if returns.index.name else 'Date', var_name='Asset', value_name='Return')\n            fig = px.box(\n                melted_returns,\n                x='Asset',\n                y='Return',\n                title=title,\n                labels=labels,\n                points='all',\n                template=THEME,\n                color='Asset',\n                color_discrete_sequence=color_sequence\n            )\n    elif isinstance(returns, pd.Series):\n        fig = px.box(\n            returns.rename(\"Return\"),\n            y='Return',\n            title=title,\n            labels=labels,\n            points='all',\n            template=THEME,\n            color_discrete_sequence=['cyan']\n        )\n    else:\n        st.warning(\"Returns data is neither a DataFrame nor a Series.\")\n        return\n    \n    fig.update_traces(boxmean='sd')\n    fig.update_layout(\n        xaxis_title=labels.get(\"Asset\", \"Assets\"),\n        yaxis_title=labels.get(\"Return\", \"Returns (%)\"),\n        legend_title=\"Assets\"\n    )\n    st.plotly_chart(fig, use_container_width=True)\n    \n    st.markdown(\"\"\"\n        **\ud83d\udcd6 Interpretation:**\n        - **Boxes:** Represent the interquartile range (IQR) where the middle 50% of the data lies.\n        - **Median Line:** Indicates the median return.\n        - **Whiskers:** Extend to show the range of the data excluding outliers.\n        - **Outliers:** Individual points outside the whiskers represent atypical returns.\n        - **Box Mean (Shaded):** Shows the mean and standard deviation of returns.\n    \"\"\")\n\ndef plot_cumulative_returns_heatmap(cum_returns):\n    \"\"\"Plots the cumulative returns heatmap.\"\"\"\n    try:\n        cum_returns_normalized = cum_returns / cum_returns.max()\n        fig_heatmap = px.imshow(\n            cum_returns_normalized.to_frame().T,\n            labels=dict(x=\"Date\", y=\"Portfolio\", color=\"Normalized Cumulative Return\"),\n            title=\"Cumulative Returns Heatmap\",\n            aspect=\"auto\",\n            color_continuous_scale='Viridis',\n            template=THEME\n        )\n        st.plotly_chart(fig_heatmap, use_container_width=True)\n        st.markdown(\n            \"**Interpretation:** The heatmap visualizes the normalized cumulative returns over time, \"\n            \"allowing for an intuitive comparison of portfolio performance across different periods.\"\n        )\n    except Exception as e:\n        st.error(f\"Error plotting cumulative returns heatmap: {e}\")\n\ndef plot_returns_correlation_heatmap(price_data):\n    \"\"\"Plots the correlation heatmap of asset returns.\"\"\"\n    correlation_data = price_data.pct_change().dropna()\n    plot_correlation_heatmap(correlation_data)\n\ndef plot_correlation_heatmap(correlation_data):\n    \"\"\"Plots an enhanced correlation heatmap using Plotly with improved aesthetics.\"\"\"\n    if correlation_data.empty:\n        st.warning(\"No data available to plot correlation heatmap.\")\n        return\n    corr = correlation_data.corr()\n    fig_heatmap = px.imshow(\n        corr,\n        title=\"\ud83d\udcc8 Correlation Heatmap of Assets\",\n        labels=dict(x=\"Asset\", y=\"Asset\", color=\"Correlation\"),\n        color_continuous_scale='RdBu',\n        zmin=-1,\n        zmax=1,\n        text_auto=True,\n        aspect=\"auto\",\n        template=THEME\n    )\n    fig_heatmap.update_layout(\n        title_x=0.5,\n        xaxis_title=\"Assets\",\n        yaxis_title=\"Assets\"\n    )\n    fig_heatmap.update_traces(\n        hovertemplate=\"Asset1: %{x}<br>Asset2: %{y}<br>Correlation: %{z:.2f}<extra></extra>\"\n    )\n    st.plotly_chart(fig_heatmap, use_container_width=True)\n\ndef plot_growth_comparison(cum_returns, benchmark_cum_returns, portfolio_name='Portfolio', benchmark_name='Benchmark'):\n    \"\"\"Plots an enhanced growth comparison between portfolio and benchmark with improved interactivity.\"\"\"\n    fig_growth = px.line(\n        title='\ud83d\udcc8 Growth Comparison',\n        labels={'value': 'Cumulative Returns', 'index': 'Date'},\n        template=THEME\n    )\n    \n    fig_growth.add_scatter(\n        x=cum_returns.index,\n        y=cum_returns,\n        mode='lines',\n        name=portfolio_name,\n        line=dict(color='cyan', width=2)\n    )\n    fig_growth.add_scatter(\n        x=benchmark_cum_returns.index,\n        y=benchmark_cum_returns,\n        mode='lines',\n        name=benchmark_name,\n        line=dict(color='orange', width=2, dash='dash')\n    )\n    \n    fig_growth.update_layout(\n        hovermode='x unified',\n        xaxis=dict(showgrid=True),\n        yaxis=dict(showgrid=True),\n        legend=dict(title=\"Legend\", x=0.01, y=0.99),\n        height=600  # Added height parameter\n    )\n    \n    st.plotly_chart(fig_growth, use_container_width=True, height=600, config={'responsive': True})\n\ndef plot_drawdown_comparison(drawdown_portfolio: pd.Series, drawdown_benchmark: pd.Series, portfolio_name='Portfolio', benchmark_name='Benchmark') -> None:\n    \"\"\"Plots an enhanced drawdown comparison between portfolio and benchmark with improved visuals.\"\"\"\n    fig_drawdown = px.line(\n        title='\ud83d\udcc9 Drawdown Comparison',\n        labels={'value': 'Drawdown (%)', 'index': 'Date'},\n        template=THEME\n    )\n    fig_drawdown.add_scatter(\n        x=drawdown_portfolio.index,\n        y=drawdown_portfolio,\n        mode='lines',\n        name=portfolio_name,\n        line=dict(color='red', width=2)\n    )\n    if not drawdown_benchmark.empty:\n        fig_drawdown.add_scatter(\n            x=drawdown_benchmark.index,\n            y=drawdown_benchmark,\n            mode='lines',\n            name=benchmark_name,\n            line=dict(color='blue', width=2, dash='dash')\n        )\n    fig_drawdown.update_layout(\n        hovermode=HOVERMODE,\n        yaxis=dict(showgrid=True),\n        xaxis=dict(showgrid=True),\n        legend=dict(title=\"Legend\", x=0.01, y=0.99),\n        height=FIG_HEIGHT\n    )\n    fig_drawdown.update_traces(hovertemplate=\"Date: %{x}<br>Drawdown: %{y:.2f}%\")\n    st.plotly_chart(fig_drawdown, use_container_width=True, height=600, config={'responsive': True})\n\ndef plot_cagr_over_time(cum_returns, time_frames):\n    \"\"\"Plots CAGR over different time frames.\"\"\"\n    for frame in time_frames:\n        plot_cagr(cum_returns, frame)\n\ndef plot_risk_return_attribution(returns, weights):\n    \"\"\"Plots the risk-return attribution analysis.\"\"\"\n    fig_rra = px.scatter(\n        x=returns.mean() * 252,\n        y=returns.std() * np.sqrt(252),\n        size=weights*100,\n        color=returns.mean() / returns.std(),\n        hover_name=returns.index,\n        title='Risk-Return Attribution',\n        labels={'x': 'Annualized Return', 'y': 'Annualized Risk (Std Dev)'},\n        template='plotly_dark',\n        size_max=60\n    )\n    st.plotly_chart(fig_rra, use_container_width=True)\n\ndef plot_rolling_metrics(returns, windows):\n    \"\"\"Plots rolling metrics based on selected window periods.\"\"\"\n    for window in windows:\n        rolling_mean = returns.rolling(window=window).mean()\n        rolling_std = returns.rolling(window=window).std()\n        fig = px.line(title=f'Rolling {window}-Day Mean and Std Dev')\n        fig.add_scatter(x=rolling_mean.index, y=rolling_mean, mode='lines', name='Rolling Mean')\n        fig.add_scatter(x=rolling_std.index, y=rolling_std, mode='lines', name='Rolling Std Dev')\n        st.plotly_chart(fig, use_container_width=True)\n\n@handle_exceptions\ndef calculate_risk_factor_attribution(returns: pd.Series, factors: pd.DataFrame, annualization_factor: int = 252) -> tuple:\n    \"\"\"\n    Decompose portfolio returns by risk factors using linear regression and provide\n    comments on the factor contributions.\n\n    Parameters:\n    - returns (pd.Series): Portfolio returns.\n    - factors (pd.DataFrame): DataFrame where each column represents a risk factor.\n    - annualization_factor (int): Annualization factor (252 for daily, 12 for monthly returns).\n\n    Returns:\n    - pd.DataFrame: Factor names, their contribution percentages, and explanatory comments.\n    - None: If an exception occurs, handled by the decorator.\n    \"\"\"\n    # Ensure returns has a name, or assign one if missing\n    if returns.name is None:\n        returns = returns.rename(\"Portfolio_Returns\")\n\n    # Convert returns to a DataFrame\n    returns = returns.to_frame()\n\n    # Ensure both returns and factors are in decimal format\n    if returns['Portfolio_Returns'].max() > 1:\n        returns /= 100\n\n    if factors.max().max() > 1:\n        factors /= 100\n\n    # Adjust returns to excess returns by subtracting RF (if it exists in factors)\n    if 'RF' in factors.columns:\n        returns['Portfolio_Returns'] -= factors['RF']\n        factors = factors.drop(columns=['RF'])\n\n    # Align the data using an inner join and drop any remaining NaN values\n    aligned_data = pd.merge(returns, factors, left_index=True, right_index=True, how='inner').dropna()\n\n    # Check that we have data after merging\n    if aligned_data.empty:\n        raise ValueError(\"No overlapping dates between returns and factors after dropping NaNs.\")\n\n    # Prepare X and y for regression\n    X = aligned_data[factors.columns]\n    X = sm.add_constant(X)  # Add intercept\n    y = aligned_data['Portfolio_Returns']\n\n    # Run the regression model\n    model = sm.OLS(y, X).fit()\n    coefficients = model.params.drop('const', errors='ignore')\n\n    # Calculate factor contributions (non-annualized)\n    factor_means = X.mean().drop('const', errors='ignore')\n    contributions = coefficients * factor_means\n\n    # Calculate total return\n    total_return = returns['Portfolio_Returns'].mean()\n\n    # Check if total_return is zero to avoid division by zero\n    if np.isclose(total_return, 0):\n        st.warning(\"Total portfolio return is zero. Cannot compute contribution percentages.\")\n        attribution_df = pd.DataFrame({\n            'Factor': contributions.index,\n            'Contribution (%)': [0.0] * len(contributions)\n        })\n    else:\n        # Calculate percentage contributions\n        attribution_df = pd.DataFrame({\n            'Factor': contributions.index,\n            'Contribution (%)': (contributions / total_return * 100).round(2)\n        })\n\n    # Remove the constant term if it exists\n    if 'const' in attribution_df['Factor'].values:\n        attribution_df = attribution_df[attribution_df['Factor'] != 'const']\n\n    # Use full names for factors\n    factor_full_names = {\n        'Mkt-RF': 'Market Risk Premium',\n        'SMB': 'Small Minus Big',\n        'HML': 'High Minus Low'\n    }\n    attribution_df['Factor'] = attribution_df['Factor'].replace(factor_full_names)\n\n    # Generate detailed comments based on factor contributions\n    comments = []\n    for _, row in attribution_df.iterrows():\n        factor_name = row['Factor']\n        contribution = row['Contribution (%)']\n\n        # Market Risk Premium (Mkt-RF) Analysis\n        if factor_name == 'Market Risk Premium':\n            if contribution > 75:\n                comments.append(f\"{factor_name} = {contribution}%: Extremely high sensitivity to market movements.\")\n            elif contribution > 50:\n                comments.append(f\"{factor_name} = {contribution}%: High sensitivity to overall market movements.\")\n            elif contribution > 30:\n                comments.append(f\"{factor_name} = {contribution}%: Notable sensitivity to market risk.\")\n            elif contribution > 10:\n                comments.append(f\"{factor_name} = {contribution}%: Moderate exposure to market risk.\")\n            else:\n                comments.append(f\"{factor_name} = {contribution}%: Low exposure to market risk.\")\n\n        # Small Minus Big (SMB) Analysis\n        elif factor_name == 'Small Minus Big':\n            if contribution > 5:\n                comments.append(f\"{factor_name} = {contribution}%: Strong tilt towards small-cap stocks.\")\n            elif contribution > 0:\n                comments.append(f\"{factor_name} = {contribution}%: Mild preference for small-cap stocks.\")\n            elif contribution > -5:\n                comments.append(f\"{factor_name} = {contribution}%: Slight tilt towards larger-cap stocks.\")\n            else:\n                comments.append(f\"{factor_name} = {contribution}%: Clear preference for large-cap stocks.\")\n\n        # High Minus Low (HML) Analysis\n        elif factor_name == 'High Minus Low':\n            if contribution > 5:\n                comments.append(f\"{factor_name} = {contribution}%: Strong tilt toward value stocks.\")\n            elif contribution > 0:\n                comments.append(f\"{factor_name} = {contribution}%: Slight positive tilt toward value stocks.\")\n            elif contribution > -5:\n                comments.append(f\"{factor_name} = {contribution}%: Minor negative tilt toward growth stocks.\")\n            else:\n                comments.append(f\"{factor_name} = {contribution}%: Clear preference for growth stocks.\")\n\n    # Add comments to the DataFrame\n    attribution_df['Comment'] = comments\n\n    return attribution_df, None\n\n@handle_exceptions\ndef fetch_fama_french_factors(start_date: str, end_date: str) -> pd.DataFrame:\n    \"\"\"\n    Fetch Fama-French daily factor data between specified dates.\n\n    Parameters:\n    - start_date (str): Start date in 'YYYY-MM-DD' format.\n    - end_date (str): End date in 'YYYY-MM-DD' format.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing Fama-French factors.\n    \"\"\"\n    # [Function Implementation Remains Unchanged]\n    try:\n        # Fetch daily Fama-French factors\n        ff_data = pdr.get_data_famafrench('F-F_Research_Data_Factors_daily', start=start_date, end=end_date)\n        if not ff_data:\n            st.error(\"Fama-French data not found.\")\n            return pd.DataFrame()\n\n        factors = ff_data[0]\n        factors = factors.rename(columns=lambda x: x.strip())  # Remove any leading/trailing spaces\n        factors /= 100  # Convert percentage returns to decimal format\n\n        # Fill missing data by forward and backward filling\n        factors = factors.fillna(method='ffill').fillna(method='bfill')\n\n        # Check for necessary columns\n        required_columns = {'Mkt-RF', 'SMB', 'HML', 'RF'}\n        if not required_columns.issubset(factors.columns):\n            missing = required_columns - set(factors.columns)\n            st.error(f\"Missing factor columns: {', '.join(missing)}\")\n            return pd.DataFrame()\n\n        return factors\n    except Exception as e:\n        st.error(f\"Error fetching factor data: {e}\")\n        return pd.DataFrame()\n\n@handle_exceptions\ndef calculate_final_score(primary_metrics: dict, comparison_metrics: dict) -> float:\n    metrics_to_compare = [\n        'Annualized Return (CAGR)',\n        'Sharpe Ratio',\n        'Sortino Ratio',\n        'Treynor Ratio',\n        'Calmar Ratio',\n        'Alpha (annualized)',\n        'Information Ratio',\n        'Modigliani\u2013Modigliani Measure',\n        'Upside Capture Ratio',\n        'Gain/Loss Ratio'\n    ]\n    metric_weights = {\n        'Annualized Return (CAGR)': 15,\n        'Sharpe Ratio': 15,\n        'Sortino Ratio': 10,\n        'Treynor Ratio': 10,\n        'Calmar Ratio': 10,\n        'Alpha (annualized)': 10,\n        'Information Ratio': 10,\n        'Modigliani\u2013Modigliani Measure': 10,\n        'Upside Capture Ratio': 5,\n        'Gain/Loss Ratio': 5\n    }\n\n    score = 0\n    total = 0\n\n    for metric in metrics_to_compare:\n        primary = primary_metrics.get(metric, \"N/A\")\n        comparison = comparison_metrics.get(metric, \"N/A\")\n        if primary != \"N/A\" and comparison != \"N/A\":\n            try:\n                primary_val = float(primary.strip('%')) if '%' in primary else float(primary)\n                comparison_val = float(comparison.strip('%')) if '%' in comparison else float(comparison)\n                weight = metric_weights.get(metric, 1)\n                if comparison_val == 0:\n                    st.warning(f\"Comparison metric '{metric}' has a value of zero. Skipping this metric to avoid division by zero.\")\n                    continue\n                if primary_val > comparison_val:\n                    score += weight * (primary_val / comparison_val)\n                else:\n                    score += weight * (primary_val / comparison_val) * 0.5  # Partial credit\n                total += weight\n            except ValueError:\n                st.warning(f\"Invalid numeric conversion for metric '{metric}'. Skipping.\")\n                continue\n    return (score / total) * 100 if total > 0 else 0\n    \n@handle_exceptions\ndef plot_cagr_over_time(cum_returns: pd.Series, time_frames: list = ['Weekly', 'Monthly', 'Quarterly', 'Annually']) -> None:\n    \"\"\"\n    Plot CAGR over multiple time frames.\n\n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    - time_frames (list): List of time frames to calculate CAGR.\n    \"\"\"\n    frequency_map = {\n        'Weekly': 'W',\n        'Monthly': 'M',\n        'Quarterly': 'Q',\n        'Annually': 'Y'\n    }\n    fig = go.Figure()\n    for tf in time_frames:\n        freq = frequency_map.get(tf)\n        if not freq:\n            continue\n        rolled = cum_returns.resample(freq).last()\n        if rolled.empty:\n            st.warning(f\"No data available for {tf} CAGR calculation.\")\n            continue\n        years = (rolled.index[-1] - rolled.index[0]).days / 365.25\n        if years <= 0:\n            st.warning(f\"Not enough data to calculate {tf} CAGR.\")\n            continue\n        cagr = (rolled / rolled.iloc[0]) ** (1/years) - 1\n        fig.add_trace(go.Scatter(x=rolled.index, y=cagr, mode='lines', name=f'{tf} CAGR'))\n    fig.update_layout(\n        title='CAGR Over Multiple Time Frames',\n        xaxis_title='Date',\n        yaxis_title='CAGR',\n        hovermode=HOVERMODE\n    )\n    st.plotly_chart(fig, use_container_width=True)\n\n@handle_exceptions\ndef backtest(weights: list, prices: pd.DataFrame, rebalance_freq: str = 'M', broker_fee: float = 0.0, debug: bool = False) -> tuple:\n    \"\"\"\n    Backtest the portfolio based on weights and price data.\n\n    Parameters:\n    - weights (list): Allocation weights for each asset.\n    - prices (pd.DataFrame): Adjusted closing prices of assets.\n    - rebalance_freq (str): Rebalancing frequency (e.g., 'D', 'W', 'M').\n    - broker_fee (float): Broker fee as a percentage.\n    - debug (bool): If True, prints debug information.\n\n    Returns:\n    - tuple: (portfolio_returns: pd.Series, cum_returns: pd.Series)\n    \"\"\"\n    try:\n        # Calculate returns and drop any NaN values\n        returns = prices.pct_change().dropna()\n        if returns.empty:\n            st.error(\"Returns data is empty after calculating percentage changes.\")\n            return pd.Series(dtype=float), pd.Series(dtype=float)\n\n        # Calculate portfolio returns\n        portfolio_returns = returns.dot(weights)\n        if not isinstance(portfolio_returns, pd.Series):\n            portfolio_returns = portfolio_returns.squeeze()\n\n        # Identify rebalancing dates\n        if rebalance_freq.upper() == 'D':\n            rebalance_dates = returns.index\n        else:\n            rebalance_dates = returns.resample(rebalance_freq.upper()).last().dropna().index\n\n        # Debug: Show rebalancing dates\n        if debug:\n            st.write(f\"Rebalancing Dates: {rebalance_dates.tolist()}\")\n\n        # Ensure rebalance_dates are in the portfolio_returns index\n        valid_rebalance_dates = rebalance_dates.intersection(portfolio_returns.index)\n        if debug:\n            st.write(f\"Valid Rebalancing Dates: {valid_rebalance_dates.tolist()}\")\n\n        # Apply broker fees on rebalancing dates\n        if not valid_rebalance_dates.empty:\n            portfolio_returns.loc[valid_rebalance_dates] -= broker_fee / 100  # Convert to decimal\n        else:\n            st.warning(\"No valid rebalancing dates found within the returns data.\")\n\n        # Calculate cumulative returns\n        cum_returns = (1 + portfolio_returns).cumprod()\n        if cum_returns.empty:\n            st.error(\"Cumulative returns are empty. Check the data and allocations.\")\n            return pd.Series(dtype=float), pd.Series(dtype=float)\n\n        # Debug: Show cumulative returns\n        if debug:\n            st.write(\"Cumulative Returns:\")\n            st.write(cum_returns)\n\n        return portfolio_returns, cum_returns\n    except Exception as e:\n        st.error(f\"Error during backtesting: {e}\")\n        return pd.Series(dtype=float), pd.Series(dtype=float)\n\ndef calculate_sharpe_ratio(returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Sharpe Ratio for a given set of returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Sharpe Ratio.\n    \"\"\"\n    excess_return = returns.mean() * 252 - rf\n    std_dev = returns.std() * np.sqrt(252)\n    return excess_return / std_dev if std_dev != 0 else np.nan\n\ndef calculate_sortino_ratio(returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Sortino Ratio for a given set of returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Sortino Ratio.\n    \"\"\"\n    excess_return = returns.mean() * 252 - rf\n    downside_std = returns[returns < 0].std() * np.sqrt(252)\n    return excess_return / downside_std if downside_std != 0 else np.nan\n\ndef calculate_treynor_ratio(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Treynor Ratio for a given set of returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Treynor Ratio.\n    \"\"\"\n    beta = calculate_beta(returns, benchmark_returns)\n    excess_return = returns.mean() * 252 - rf\n    return excess_return / beta if beta != 0 else np.nan\n\ndef calculate_calmar_ratio(returns: pd.Series, cum_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Calmar Ratio for a given set of returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n\n    Returns:\n    - float: Calmar Ratio.\n    \"\"\"\n    annual_return = returns.mean() * 252\n    max_dd = drawdown(cum_returns)\n    return annual_return / abs(max_dd) if max_dd != 0 else np.nan\n\ndef calculate_beta(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Beta of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: Beta value.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    covariance_matrix = np.cov(returns, benchmark_returns)\n    covariance = covariance_matrix[0, 1]\n    benchmark_variance = covariance_matrix[1, 1]\n    return covariance / benchmark_variance if benchmark_variance != 0 else np.nan\n\ndef calculate_alpha(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Alpha of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Alpha value.\n    \"\"\"\n    beta = calculate_beta(returns, benchmark_returns)\n    portfolio_return = returns.mean() * 252\n    benchmark_return = benchmark_returns.mean() * 252\n    return portfolio_return - (rf + beta * (benchmark_return - rf)) if not np.isnan(beta) else np.nan\n\ndef calculate_r_squared(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the R-squared of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: R-squared value.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    covariance = np.cov(returns, benchmark_returns)\n    var_port = covariance[0,0]\n    var_bench = covariance[1,1]\n    cov = covariance[0,1]\n    return (cov ** 2) / (var_port * var_bench) if var_port !=0 and var_bench !=0 else np.nan\n\ndef calculate_information_ratio(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Information Ratio of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: Information Ratio.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    active_return = (returns.mean() - benchmark_returns.mean()) * 252\n    tracking_error = calculate_tracking_error(returns, benchmark_returns)\n    return active_return / tracking_error if tracking_error != 0 else np.nan\n\ndef generate_recommendations(allocations: list, available_selected: list) -> list:\n    \"\"\"\n    Generate basic portfolio recommendations based on current allocations.\n\n    Parameters:\n    - allocations (list of float): Current allocation percentages.\n    - available_selected (list of str): List of selected asset tickers.\n\n    Returns:\n    - list of str: Recommendations.\n    \"\"\"\n    recommendations = []\n    max_alloc = max(allocations)\n    min_alloc = min(allocations)\n    total_alloc = sum(allocations)\n\n    if max_alloc > 40.0:\n        idx = allocations.index(max_alloc)\n        ticker = available_selected[idx]\n        recommendations.append(f\"\ud83d\udd39 Consider reducing the allocation to **{ticker}** since it constitutes **{max_alloc:.2f}%** of your portfolio.\")\n\n    if min_alloc < 5.0:\n        idx = allocations.index(min_alloc)\n        ticker = available_selected[idx]\n        recommendations.append(f\"\ud83d\udd38 Consider increasing the allocation to **{ticker}** to at least **5.00%** for better diversification.\")\n\n    if total_alloc < 100.0:\n        recommendations.append(\"\ud83d\udd39 Consider allocating the remaining funds to additional assets to reach a total of **100%**.\")\n\n    return recommendations\n\ndef get_common_benchmarks(selected_tickers: list) -> dict:\n    \"\"\"\n    Define benchmark suggestions based on asset sectors or indices.\n\n    Parameters:\n    - selected_tickers (list of str): List of selected asset tickers.\n\n    Returns:\n    - dict: Dictionary of benchmark names and their corresponding tickers.\n    \"\"\"\n    # Example benchmark sets\n    sp500 = {\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"META\", \"TSLA\"}  # Example S&P 500 tech companies\n    nasdaq_tech = {\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"META\", \"TSLA\", \"NVDA\", \"AMD\"}\n\n    selected_set = set(selected_tickers)\n\n    if selected_set.issubset(sp500):\n        return {\n            \"S&P 500\": \"^GSPC\",\n            \"Dow Jones Industrial Average\": \"^DJI\",\n            \"Russell 2000\": \"^RUT\",\n            \"Custom\": \"CUSTOM\"\n        }\n    elif selected_set.issubset(nasdaq_tech):\n        return {\n            \"NASDAQ Composite\": \"^IXIC\",\n            \"QQQ (Invesco QQQ ETF)\": \"QQQ\",\n            \"Custom\": \"CUSTOM\"\n        }\n    else:\n        return {\n            \"S&P 500\": \"^GSPC\",\n            \"NASDAQ Composite\": \"^IXIC\",\n            \"Dow Jones Industrial Average\": \"^DJI\",\n            \"Russell 2000\": \"^RUT\",\n            \"Custom\": \"CUSTOM\"\n        }\n\ndef calculate_tracking_error(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Tracking Error of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: Tracking Error.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    return np.std((returns - benchmark_returns)) * np.sqrt(252)\n\n@handle_exceptions\ndef calculate_performance_attribution(returns: pd.Series, weights: list) -> pd.DataFrame:\n    \"\"\"\n    Calculate performance attribution based on asset contributions.\n\n    Parameters:\n    - returns (pd.Series or pd.DataFrame): Returns of assets.\n    - weights (list): Allocation weights for each asset.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing asset names and their contribution percentages.\n    \"\"\"\n    if isinstance(returns, pd.Series):\n        returns = returns.to_frame('Asset')  # Convert Series to DataFrame with a default column name\n\n    if len(weights) != len(returns.columns):\n        st.error(f\"Number of weights ({len(weights)}) does not match number of assets ({len(returns.columns)}).\")\n        return pd.DataFrame()\n\n    annual_returns = returns.mean() * 252\n    contributions = annual_returns * weights\n    attribution_df = pd.DataFrame({\n        'Asset': returns.columns,\n        'Contribution (%)': (contributions / contributions.sum() * 100).round(2)\n    }).sort_values(by='Contribution (%)', ascending=False)\n    return attribution_df\n\ndef calculate_active_return(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Active Return of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: Active Return percentage.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    return (returns.mean() - benchmark_returns.mean()) * 252 * 100\n\ndef calculate_gain_loss_ratio(returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Gain/Loss Ratio of the portfolio.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - float: Gain/Loss Ratio.\n    \"\"\"\n    gains = returns[returns > 0].sum()\n    losses = -returns[returns < 0].sum()\n    return gains / losses if losses != 0 else np.nan\n\ndef drawdown(cum_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Maximum Drawdown of the portfolio.\n\n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n\n    Returns:\n    - float: Maximum Drawdown value.\n    \"\"\"\n    if cum_returns.empty:\n        return np.nan\n    peak = cum_returns.expanding(min_periods=1).max()\n    dd = (cum_returns / peak) - 1\n    return dd.min()\n\ndef calculate_capture_ratio(returns: pd.Series, benchmark_returns: pd.Series, upside: bool = True) -> float:\n    \"\"\"\n    Calculate the Upside or Downside Capture Ratio.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - upside (bool): If True, calculates Upside Capture Ratio; otherwise, Downside.\n\n    Returns:\n    - float: Capture Ratio percentage.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    mask = benchmark_returns > 0 if upside else benchmark_returns < 0\n    if mask.sum() == 0:\n        return np.nan\n    portfolio = returns[mask]\n    benchmark = benchmark_returns[mask]\n    return (portfolio.sum() / benchmark.sum()) * 100 if benchmark.sum() != 0 else np.nan\n\ndef calculate_safe_withdrawal_rate(returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Safe Withdrawal Rate based on portfolio returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - float: Safe Withdrawal Rate percentage.\n    \"\"\"\n    if returns.empty:\n        return np.nan\n    return (returns.mean() / returns.std()) * 100\n\ndef calculate_perpetual_withdrawal_rate(returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Perpetual Withdrawal Rate based on portfolio returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - float: Perpetual Withdrawal Rate percentage.\n    \"\"\"\n    if returns.empty:\n        return np.nan\n    return (returns.mean() / returns.std()) * 100\n\ndef calculate_positive_periods(returns: pd.Series) -> str:\n    \"\"\"\n    Calculate the number and percentage of positive return periods.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - str: String describing positive periods.\n    \"\"\"\n    if returns.empty:\n        return \"N/A\"\n    positive = returns > 0\n    return f\"{positive.sum()} out of {len(returns)} ({(positive.sum()/len(returns))*100:.2f}%)\"\n\ndef calculate_modigliani_miller(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Modigliani\u2013Modigliani Measure (M2).\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: M2 value.\n    \"\"\"\n    sharpe = calculate_sharpe_ratio(returns, rf)\n    alpha = calculate_alpha(returns, benchmark_returns, rf)\n    return alpha / sharpe if sharpe != 0 and not np.isnan(alpha) else np.nan\n\ndef get_drawdown_details(cum_returns: pd.Series) -> list:\n    \"\"\"\n    Generate detailed drawdown information.\n\n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n\n    Returns:\n    - list: List of dictionaries containing drawdown details.\n    \"\"\"\n    drawdowns = []\n    if cum_returns.empty:\n        return drawdowns\n    peak = cum_returns.iloc[0]\n    peak_date = cum_returns.index[0]\n    trough = cum_returns.iloc[0]\n    trough_date = cum_returns.index[0]\n    for date, value in cum_returns.items():\n        if value > peak:\n            if trough < peak:\n                recovery = cum_returns[cum_returns >= peak].loc[trough_date:]\n                if not recovery.empty:\n                    recovery_date = recovery.index[0]\n                    if recovery_date > trough_date:\n                        recovery_time = (recovery_date - trough_date).days\n                        underwater_period = (recovery_date - peak_date).days\n                        drawdowns.append({\n                            'Start': peak_date.strftime('%b %Y'),\n                            'End': trough_date.strftime('%b %Y'),\n                            'Length': f\"{(trough_date - peak_date).days // 30} months\",\n                            'Recovery By': recovery_date.strftime('%b %Y'),\n                            'Recovery Time': f\"{recovery_time // 30} months\",\n                            'Underwater Period': f\"{underwater_period // 30} months\",\n                            'Drawdown': f\"{((trough / peak) - 1) * 100:.2f}%\"\n                        })\n                    else:\n                        drawdowns.append({\n                            'Start': peak_date.strftime('%b %Y'),\n                            'End': trough_date.strftime('%b %Y'),\n                            'Length': f\"{(trough_date - peak_date).days // 30} months\",\n                            'Recovery By': \"Not Recovered\",\n                            'Recovery Time': \"N/A\",\n                            'Underwater Period': \"N/A\",\n                            'Drawdown': f\"{((trough / peak) - 1) * 100:.2f}%\"\n                        })\n            peak = value\n            peak_date = date\n            trough = value\n            trough_date = date\n        elif value < trough:\n            trough = value\n            trough_date = date\n    if trough < peak:\n        drawdowns.append({\n            'Start': peak_date.strftime('%b %Y'),\n            'End': trough_date.strftime('%b %Y'),\n            'Length': f\"{(trough_date - peak_date).days // 30} months\",\n            'Recovery By': \"Not Recovered\",\n            'Recovery Time': \"N/A\",\n            'Underwater Period': \"N/A\",\n            'Drawdown': f\"{((trough / peak) - 1) * 100:.2f}%\"\n        })\n    try:\n        drawdowns_sorted = sorted(drawdowns, key=lambda x: float(x['Drawdown'].strip('%')), reverse=False)\n    except:\n        drawdowns_sorted = []\n    drawdowns_sorted = drawdowns_sorted[:10]\n    for idx, dd in enumerate(drawdowns_sorted, start=1):\n        dd['Rank'] = idx\n    drawdowns_final = []\n    for dd in drawdowns_sorted:\n        drawdowns_final.append({\n            'Rank': dd['Rank'],\n            'Start': dd['Start'],\n            'End': dd['End'],\n            'Length': dd['Length'],\n            'Recovery By': dd['Recovery By'],\n            'Recovery Time': dd['Recovery Time'],\n            'Underwater Period': dd['Underwater Period'],\n            'Drawdown': dd['Drawdown']\n        })\n    return drawdowns_final\n\n@handle_exceptions\ndef optimize_portfolio(\n    returns: pd.DataFrame,\n    benchmark_returns: pd.Series = None,\n    objectives: list = ['sharpe'],\n    rf: float = 0.02,\n    max_weight: float = 1.0,\n    min_weight: float = 0.0,\n    target_return: float = None\n) -> np.ndarray:\n    \"\"\"\n    Optimize a portfolio based on specified objectives.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - benchmark_returns (pd.Series, optional): Returns of the benchmark index.\n    - objectives (list): Objectives to optimize.\n        Options include 'sharpe', 'min_variance', 'max_return', 'min_drawdown', 'maximize_alpha', 'minimize_beta'.\n    - rf (float): Risk-free rate for alpha calculation.\n    - max_weight (float): Maximum weight per asset.\n    - min_weight (float): Minimum weight per asset.\n    - target_return (float, optional): Target return for the portfolio.\n\n    Returns:\n    - np.ndarray: Optimized asset weights or None if optimization fails.\n    \"\"\"\n    from scipy.optimize import minimize\n\n    def calculate_beta(portfolio_returns: pd.Series, benchmark_returns: pd.Series) -> float:\n        covariance = np.cov(portfolio_returns, benchmark_returns)\n        variance = covariance[1, 1]\n        return covariance[0, 1] / variance if variance != 0 else 0\n\n    def calculate_var(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:\n        return np.percentile(portfolio_returns, (1 - confidence_level) * 100)\n\n    def calculate_cvar(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:\n        var = calculate_var(portfolio_returns, confidence_level)\n        return portfolio_returns[portfolio_returns <= var].mean()\n\n    # Define individual objective functions\n    def sharpe_ratio(weights: np.ndarray) -> float:\n        portfolio_return = np.dot(returns.mean(), weights) * 252\n        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n        return -portfolio_return / portfolio_volatility if portfolio_volatility > 1e-6 else 0  # Negative for maximization\n\n    def min_variance(weights: np.ndarray) -> float:\n        return np.dot(weights.T, np.dot(returns.cov() * 252, weights))\n\n    def max_return(weights: np.ndarray) -> float:\n        return -np.dot(returns.mean(), weights) * 252  # Negative for maximization\n\n    def min_drawdown(weights: np.ndarray) -> float:\n        portfolio_returns = returns.dot(weights)\n        cvar = calculate_cvar(portfolio_returns, 0.95)\n        return cvar  # Minimizing CVaR as a proxy for drawdown\n\n    # Initialize list of objective functions\n    objective_functions = []\n\n    # Map objectives to functions\n    for obj in objectives:\n        if obj == 'sharpe':\n            objective_functions.append(sharpe_ratio)\n        elif obj == 'min_variance':\n            objective_functions.append(min_variance)\n        elif obj == 'max_return':\n            objective_functions.append(max_return)\n        elif obj == 'min_drawdown':\n            objective_functions.append(min_drawdown)\n        elif obj == 'maximize_alpha':\n            if benchmark_returns is None:\n                st.error(\"benchmark_returns must be provided for 'maximize_alpha' objective.\")\n                return None\n\n            benchmark_return = benchmark_returns.mean() * 252  # Assuming daily returns annualized\n\n            def maximize_alpha(weights: np.ndarray) -> float:\n                portfolio_return = np.dot(returns.mean(), weights) * 252\n                portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n                portfolio_returns = returns.dot(weights)\n                beta = calculate_beta(portfolio_returns, benchmark_returns)\n                alpha = portfolio_return - (rf + beta * (benchmark_return - rf))\n                return -alpha  # Negative for maximization\n\n            objective_functions.append(maximize_alpha)\n        elif obj == 'minimize_beta':\n            if benchmark_returns is None:\n                st.error(\"benchmark_returns must be provided for 'minimize_beta' objective.\")\n                return None\n\n            def minimize_beta(weights: np.ndarray) -> float:\n                portfolio_returns = returns.dot(weights)\n                beta = calculate_beta(portfolio_returns, benchmark_returns)\n                return beta\n\n            objective_functions.append(minimize_beta)\n        else:\n            st.error(f\"Invalid optimization objective: {obj}\")\n            return None\n\n    # Composite objective function: weighted sum of individual objectives\n    def composite_objective(weights: np.ndarray) -> float:\n        return sum(fn(weights) for fn in objective_functions)\n\n    # Define constraints\n    constraints = [\n        {'type': 'eq', 'fun': lambda x: np.sum(x) - 1},\n    ]\n\n    if target_return is not None:\n        def target_return_constraint(x: np.ndarray) -> float:\n            return np.dot(x, returns.mean()) * 252 - target_return\n        constraints.append({\n            'type': 'ineq',\n            'fun': target_return_constraint\n        })\n\n    # Define bounds for weights\n    bounds = tuple((min_weight, max_weight) for _ in range(returns.shape[1]))\n\n    # Initial guess (equally distributed weights)\n    initial_guess = np.array([1.0 / returns.shape[1]] * returns.shape[1])\n\n    # Perform optimization\n    result = minimize(\n        composite_objective,\n        initial_guess,\n        method='SLSQP',\n        bounds=bounds,\n        constraints=constraints\n    )\n\n    if result.success:\n        optimized_weights = result.x\n        portfolio_volatility = np.sqrt(np.dot(optimized_weights.T, np.dot(returns.cov() * 252, optimized_weights)))\n        portfolio_return = np.dot(returns.mean(), optimized_weights) * 252\n        sharpe_ratio_val = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else np.nan\n\n        if portfolio_volatility <= 1e-6:\n            st.error(\"Optimized portfolio has near-zero volatility. Optimization constraints may be too restrictive.\")\n            return None\n\n        # Store optimized results in session state\n        st.session_state.backtest_results['optimized_weights'] = optimized_weights\n        st.session_state.backtest_results['optimized_return'] = portfolio_return\n        st.session_state.backtest_results['optimized_volatility'] = portfolio_volatility\n        st.session_state.backtest_results['optimized_sharpe'] = sharpe_ratio_val\n\n        return optimized_weights\n    else:\n        st.error(\"Optimization failed. Try adjusting your constraints or target return.\")\n        return None\n\ndef monte_carlo_simulation(\n    returns: pd.DataFrame,\n    num_simulations: int = 1000,\n    periods: int = 252,\n    mean_returns: pd.Series = None,\n    cov_matrix: pd.DataFrame = None,\n    mean_reversion: bool = False,\n    mean_reversion_speed: float = 0.1,\n    long_term_mean: pd.Series = None,\n    time_varying_vol: bool = False,\n    vol_change_rate: float = 0.0,\n    stress_shocks: dict = None,\n    stress_period: int = None,\n    return_distribution: str = 'normal'\n) -> np.ndarray:\n    \"\"\"\n    Perform Monte Carlo simulations for portfolio returns.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - num_simulations (int): Number of simulation paths.\n    - periods (int): Number of periods to simulate.\n    - mean_returns (pd.Series, optional): Mean returns of assets.\n    - cov_matrix (pd.DataFrame, optional): Covariance matrix of asset returns.\n    - mean_reversion (bool): If True, applies mean reversion.\n    - mean_reversion_speed (float): Speed of mean reversion.\n    - long_term_mean (pd.Series, optional): Long-term mean for mean reversion.\n    - time_varying_vol (bool): If True, allows volatility to change over time.\n    - vol_change_rate (float): Rate at which volatility changes.\n    - stress_shocks (dict, optional): Dict of shocks to apply at specified period.\n    - stress_period (int, optional): Period at which to apply stress shocks.\n    - return_distribution (str): Distribution type ('normal' or 'log-normal').\n\n    Returns:\n    - np.ndarray: Array of simulated cumulative returns.\n    \"\"\"\n    if mean_returns is None:\n        mean_returns = returns.mean()\n    if cov_matrix is None:\n        cov_matrix = returns.cov()\n\n    num_assets = returns.shape[1]\n    available_selected = returns.columns.tolist()\n\n    def simulate():\n        weights = np.random.random(num_assets)\n        weights /= np.sum(weights)\n        simulated_prices = [1]  # Initial price\n        current_mean = mean_returns.copy()\n        current_vol = np.sqrt(np.diag(cov_matrix))\n        for t in range(periods):\n            if mean_reversion and long_term_mean is not None:\n                current_mean += mean_reversion_speed * (long_term_mean - current_mean)\n            if time_varying_vol:\n                current_vol += vol_change_rate\n                current_vol = np.clip(current_vol, 0, None)  # Ensure volatility doesn't go negative\n\n            adjusted_cov_matrix = np.outer(current_vol, current_vol) * np.corrcoef(returns.T)\n            if return_distribution == 'log-normal':\n                simulated_returns = np.random.lognormal(mean=np.log(1 + current_mean), sigma=current_vol) - 1\n            else:\n                simulated_returns = np.random.multivariate_normal(current_mean, adjusted_cov_matrix)\n\n            # Apply stress shocks if applicable\n            if stress_shocks and t == stress_period:\n                for idx, ticker in enumerate(available_selected):\n                    simulated_returns[idx] += stress_shocks.get(ticker, 0)\n\n            portfolio_return = np.dot(simulated_returns, weights)\n            simulated_prices.append(simulated_prices[-1] * (1 + portfolio_return))\n        cumulative_return = simulated_prices[-1] - 1\n        return cumulative_return\n\n    portfolio_returns = Parallel(n_jobs=-1)(\n        delayed(simulate)() for _ in range(num_simulations)\n    )\n\n    return np.array(portfolio_returns)\n\n@handle_exceptions\ndef plot_efficient_frontier(returns: pd.DataFrame, num_portfolios: int = 1000, rf: float = 0.0, portfolio_name='Portfolio') -> None:\n    \"\"\"\n    Plot an enhanced Efficient Frontier with optimized portfolio marked.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - num_portfolios (int): Number of portfolios to simulate.\n    - rf (float): Risk-free rate for Sharpe Ratio calculation.\n    - portfolio_name (str): Name of the portfolio being optimized.\n    \"\"\"\n    from scipy.optimize import minimize\n\n    num_assets = returns.shape[1]\n\n    def generate_portfolio_metrics(weights: np.ndarray) -> tuple:\n        portfolio_return = np.dot(returns.mean(), weights) * 252\n        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n        sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0\n        return portfolio_volatility, portfolio_return, sharpe_ratio\n\n    # Generate random portfolios\n    weights_list = np.random.dirichlet(np.ones(num_assets), num_portfolios)\n    portfolio_metrics = np.array([generate_portfolio_metrics(w) for w in weights_list])\n\n    # Filter out invalid portfolios\n    portfolio_metrics = portfolio_metrics[~np.isnan(portfolio_metrics).any(axis=1)]\n\n    if portfolio_metrics.size == 0:\n        st.warning(\"No valid portfolios to plot on the Efficient Frontier.\")\n        return\n\n    ef_df = pd.DataFrame({\n        'Std Dev': portfolio_metrics[:, 0],\n        'Return': portfolio_metrics[:, 1],\n        'Sharpe Ratio': portfolio_metrics[:, 2]\n    })\n    fig = px.scatter(\n        ef_df,\n        x='Std Dev',\n        y='Return',\n        color='Sharpe Ratio',\n        color_continuous_scale='Turbo',\n        title='\ud83d\udcc8 Efficient Frontier',\n        labels={\n            'Std Dev': 'Annualized Volatility (Std Dev)',\n            'Return': 'Annualized Return (%)',\n            'Sharpe Ratio': 'Sharpe Ratio'\n        },\n        hover_data={\n            'Sharpe Ratio': ':.2f',\n            'Std Dev': ':.2f',\n            'Return': ':.2f'\n        },\n        template=THEME\n    )\n\n    # Highlight the portfolio with the maximum Sharpe ratio\n    max_sharpe_idx = ef_df['Sharpe Ratio'].idxmax()\n    max_sharpe = ef_df.loc[max_sharpe_idx]\n    fig.add_trace(go.Scatter(\n        x=[max_sharpe['Std Dev']],\n        y=[max_sharpe['Return']],\n        mode='markers+text',\n        marker=dict(color='gold', size=12, symbol='star'),\n        name='Max Sharpe Ratio',\n        text=[\"Max Sharpe\"],\n        textposition=\"top center\",\n        hoverinfo='text'\n    ))\n    fig.add_annotation(\n        x=max_sharpe['Std Dev'],\n        y=max_sharpe['Return'],\n        text=\"Max Sharpe\",\n        showarrow=True,\n        arrowhead=1,\n        ax=0,\n        ay=-40\n    )\n\n    # Add optimized portfolio if available\n    optimized_weights = st.session_state.backtest_results.get('optimized_weights', None)\n    optimized_return = st.session_state.backtest_results.get('optimized_return', None)\n    optimized_volatility = st.session_state.backtest_results.get('optimized_volatility', None)\n    optimized_sharpe = st.session_state.backtest_results.get('optimized_sharpe', None)\n\n    if all(v is not None for v in [optimized_weights, optimized_return, optimized_volatility, optimized_sharpe]):\n        if optimized_volatility > 1e-6 and np.isfinite(optimized_sharpe):\n            fig.add_trace(go.Scatter(\n                x=[optimized_volatility],\n                y=[optimized_return],\n                mode='markers+text',\n                marker=dict(color='red', size=16, symbol='diamond'),\n                name=f'{portfolio_name} Optimized',\n                text=[\"Optimized\"],\n                textposition=\"top center\",\n                hoverinfo='text'\n            ))\n            fig.add_annotation(\n                x=optimized_volatility,\n                y=optimized_return,\n                text=\"Optimized\",\n                showarrow=True,\n                arrowhead=2,\n                ax=0,\n                ay=-50\n            )\n\n    fig.update_layout(\n        hovermode=HOVERMODE,\n        xaxis=dict(showgrid=True, title=dict(text='Annualized Volatility (Std Dev)')),\n        yaxis=dict(showgrid=True, title=dict(text='Annualized Return (%)')),\n        legend=dict(title=\"Portfolio\", x=0.01, y=0.99)\n    )\n    fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')), selector=dict(mode='markers'))\n    st.plotly_chart(fig, use_container_width=True, config={'responsive': True, 'scrollZoom': True})\n\n@handle_exceptions\ndef generate_portfolio(returns: pd.DataFrame, rf: float = 0.0) -> tuple:\n    \"\"\"\n    Generate a random portfolio and calculate its metrics.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - rf (float): Risk-free rate for Sharpe Ratio calculation.\n\n    Returns:\n    - tuple: (volatility: float, return: float, sharpe_ratio: float)\n    \"\"\"\n    try:\n        weights = np.random.dirichlet(np.ones(returns.shape[1]))\n        portfolio_return = np.dot(returns.mean(), weights) * 252\n        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n        sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0\n        return portfolio_volatility, portfolio_return, sharpe_ratio\n    except Exception as e:\n        st.error(f\"Error generating portfolio: {e}\")\n        return np.nan, np.nan, np.nan  # Return NaNs to indicate failure\n\ndef plot_allocation_pie(weights: list, assets: list, title: str = '\ud83e\udd67 Portfolio Allocation', hover_info: str = \"percent+name\") -> None:\n    \"\"\"\n    Plots the portfolio allocation pie chart.\n    \n    Parameters:\n    - weights (list): Allocation weights for each asset.\n    - assets (list): List of asset ticker symbols.\n    - title (str): Title of the pie chart.\n    - hover_info (str): Information to display on hover ('percent+name', etc.).\n    \n    Returns:\n    - None\n    \"\"\"\n    allocation_df = pd.DataFrame({\n        'Asset': assets,\n        'Weight': weights\n    })\n    \n    # Determine hover data based on hover_info\n    if hover_info == \"percent+name\":\n        hover_data = {'Weight': ':.2f%'}\n    elif hover_info == \"name\":\n        hover_data = {}\n    else:\n        hover_data = {'Weight': ':.2f%'}\n    \n    fig = px.pie(\n        allocation_df,\n        names='Asset',\n        values='Weight',\n        title=title,\n        hover_data=hover_data,\n        hole=0.3  # Donut chart appearance\n    )\n    \n    fig.update_traces(textposition='inside', textinfo='percent+label')\n    fig.update_layout(\n        uniformtext_minsize=12,\n        uniformtext_mode='hide',\n        legend_title=\"Assets\",\n        showlegend=True,\n        height=FIG_HEIGHT\n    )\n    \n    st.plotly_chart(fig, use_container_width=True, height=600, config={'responsive': True})\n\ndef plot_rolling_cagr(cum_returns: pd.Series, window: int = 252) -> None:\n    \"\"\"\n    Plot Rolling CAGR over a specified window.\n\n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    - window (int): Window size for rolling CAGR calculation.\n    \"\"\"\n    try:\n        rolling_years = window / 252  # Assuming daily data\n        rolling_cagr = (cum_returns / cum_returns.shift(window)) ** (1 / rolling_years) - 1\n        fig = px.line(\n            rolling_cagr.dropna(),\n            title=f'Rolling {int(rolling_years)}-Year CAGR',\n            labels={'index': 'Date', 'value': 'Rolling CAGR'},\n            template=THEME\n        )\n        st.plotly_chart(fig, use_container_width=True)\n    except Exception as e:\n        st.error(f\"Error plotting rolling CAGR: {e}\")\n\n@handle_exceptions\ndef plot_rolling_metrics(returns: pd.Series, windows: list = [30, 90, 180, 252]) -> None:\n    \"\"\"\n    Plot rolling metrics (Volatility and Sharpe Ratio) over multiple windows.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - windows (list): List of window sizes for rolling calculations.\n    \"\"\"\n    metrics = {}\n    for window in windows:\n        rolling_return = returns.rolling(window).mean() * 252\n        rolling_vol = returns.rolling(window).std() * np.sqrt(252)\n        rolling_sharpe = (rolling_return - 0.02) / rolling_vol\n        metrics[f'Rolling {window}-Day Volatility'] = rolling_vol\n        metrics[f'Rolling {window}-Day Sharpe Ratio'] = rolling_sharpe\n    fig = go.Figure()\n    for metric_name, metric_series in metrics.items():\n        fig.add_trace(go.Scatter(x=metric_series.index, y=metric_series, mode='lines', name=metric_name))\n    fig.update_layout(\n        title='Rolling Metrics Over User-Defined Periods',\n        xaxis_title='Date',\n        yaxis_title='Value',\n        hovermode='x unified',\n        template=THEME\n    )\n    st.plotly_chart(fig, use_container_width=True)\n\n@handle_exceptions\ndef plot_risk_return_attribution(returns: pd.Series, weights: list) -> None:\n    \"\"\"\n    Plot Risk-Return Attribution for the portfolio.\n\n    Parameters:\n    - returns (pd.Series or pd.DataFrame): Returns of assets.\n    - weights (list): Allocation weights for each asset.\n    \"\"\"\n    try:\n        if isinstance(returns, pd.Series):\n            asset_returns = returns.mean() * 252\n            asset_volatility = returns.std() * np.sqrt(252)\n            asset_contribution = weights[0] * asset_volatility if len(weights) > 0 else 0\n            hover_names = [returns.name] if returns.name else ['Asset']\n            x = [asset_volatility]\n            y = [asset_returns]\n            size = [asset_contribution]\n            color = [weights[0]]\n        else:\n            asset_returns = returns.mean() * 252\n            asset_volatility = returns.std() * np.sqrt(252)\n            asset_contribution = np.array(weights) * asset_volatility\n            hover_names = returns.columns.tolist()\n            x = asset_volatility.values\n            y = asset_returns.values\n            size = asset_contribution\n            color = weights\n\n        fig = px.scatter(\n            x=x,\n            y=y,\n            size=size,\n            color=color,\n            hover_name=hover_names,\n            title='Risk-Return Attribution',\n            labels={'x': 'Annualized Volatility', 'y': 'Annualized Return', 'color': 'Weight (%)'},\n            size_max=60,\n            color_continuous_scale='Viridis'\n        )\n        fig.update_layout(template='plotly_dark')\n        st.plotly_chart(fig, use_container_width=True)\n    except Exception as e:\n        st.error(f\"Error plotting risk-return attribution: {e}\")\n\ndef plot_var_cvar_distribution(portfolio_returns: np.ndarray, var: float, cvar: float) -> None:\n    \"\"\"\n    Plot the distribution of portfolio returns with VaR and CVaR lines.\n\n    Parameters:\n    - portfolio_returns (np.ndarray): Simulated portfolio returns.\n    - var (float): Value at Risk.\n    - cvar (float): Conditional Value at Risk.\n    \"\"\"\n    fig = px.histogram(portfolio_returns, nbins=50, title='Returns Distribution with VaR and CVaR',\n                       labels={'value': 'Returns', 'count': 'Frequency'})\n    fig.add_vline(x=var, line_dash=\"dash\", line_color=\"red\", annotation_text=f\"VaR: {var:.2f}\", annotation_position=\"top left\")\n    fig.add_vline(x=cvar, line_dash=\"dash\", line_color=\"blue\", annotation_text=f\"CVaR: {cvar:.2f}\", annotation_position=\"top left\")\n    st.plotly_chart(fig, use_container_width=True)\n\ndef plot_var_cvar_over_time(portfolio_returns: pd.Series, var: float, cvar: float) -> None:\n    \"\"\"\n    Plot cumulative returns with VaR and CVaR over time.\n\n    Parameters:\n    - portfolio_returns (pd.Series): Daily returns of the portfolio.\n    - var (float): Value at Risk.\n    - cvar (float): Conditional Value at Risk.\n    \"\"\"\n    cumulative_returns = (1 + portfolio_returns).cumprod()\n    fig = px.line(cumulative_returns, title='Cumulative Returns with VaR and CVaR Over Time', labels={'value': 'Cumulative Returns', 'index': 'Date'})\n    fig.add_hline(y=var, line_dash=\"dash\", line_color=\"red\", annotation_text=f\"VaR: {var:.2f}\", annotation_position=\"bottom right\")\n    fig.add_hline(y=cvar, line_dash=\"dash\", line_color=\"blue\", annotation_text=f\"CVaR: {cvar:.2f}\", annotation_position=\"bottom right\")\n    st.plotly_chart(fig, use_container_width=True)\n\ndef calculate_var(returns: np.ndarray, confidence_level: float = 0.95) -> float:\n    \"\"\"\n    Calculate Value at Risk (VaR) for a given set of returns.\n\n    Parameters:\n    - returns (np.ndarray): Portfolio returns.\n    - confidence_level (float): Confidence level for VaR.\n\n    Returns:\n    - float: VaR value.\n    \"\"\"\n    return np.percentile(returns, 100 * (1 - confidence_level))\n\ndef calculate_cvar(returns: np.ndarray, confidence_level: float = 0.95) -> float:\n    \"\"\"\n    Calculate Conditional Value at Risk (CVaR) for a given set of returns.\n\n    Parameters:\n    - returns (np.ndarray): Portfolio returns.\n    - confidence_level (float): Confidence level for CVaR.\n\n    Returns:\n    - float: CVaR value.\n    \"\"\"\n    var = calculate_var(returns, confidence_level)\n    return returns[returns <= var].mean()\n\n# ----------------------------\n# Caching Functions\n# ----------------------------\n\n@st.cache_data(show_spinner=False, persist=True)\ndef get_tickers() -> pd.DataFrame:\n    \"\"\"\n    Fetch and combine S&P 500 and NASDAQ-100 tickers from Wikipedia.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing combined tickers and company names.\n    \"\"\"\n    try:\n        # Fetch S&P 500 companies\n        sp500_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n        sp500_response = requests.get(sp500_url, verify=False)\n        sp500_table = pd.read_html(sp500_response.text)[0]\n        sp500 = sp500_table[['Symbol', 'Security']].rename(columns={'Symbol': 'Ticker', 'Security': 'Company Name'})\n        sp500['Ticker'] = sp500['Ticker'].str.replace('.', '-', regex=False)\n\n        # Fetch NASDAQ-100 companies\n        nasdaq100_url = 'https://en.wikipedia.org/wiki/NASDAQ-100'\n        nasdaq100_response = requests.get(nasdaq100_url, verify=False)\n        nasdaq100_tables = pd.read_html(nasdaq100_response.text)\n\n        nasdaq100 = pd.DataFrame()\n        for table in nasdaq100_tables:\n            if 'Ticker' in table.columns and 'Company' in table.columns:\n                nasdaq100 = table[['Ticker', 'Company']].rename(columns={'Ticker': 'Ticker', 'Company': 'Company Name'})\n                nasdaq100['Ticker'] = nasdaq100['Ticker'].str.replace('.', '-', regex=False)\n                break\n\n        combined = pd.concat([sp500, nasdaq100], ignore_index=True)\n        combined = combined.drop_duplicates(subset=['Ticker'])\n        return combined.sort_values('Ticker').reset_index(drop=True)\n    except Exception as e:\n        st.error(f\"Error fetching tickers: {e}\")\n        return pd.DataFrame(columns=['Ticker', 'Company Name'])\n\n@st.cache_data(show_spinner=False, persist=True)\ndef download_data(tickers: list, start: str, end: str, retries: int = 3, backoff_factor: float = 0.3) -> pd.DataFrame:\n    \"\"\"\n    Download historical adjusted closing prices for specified tickers.\n\n    Parameters:\n    - tickers (list): List of ticker symbols.\n    - start (str): Start date in 'YYYY-MM-DD' format.\n    - end (str): End date in 'YYYY-MM-DD' format.\n    - retries (int): Number of retries for failed requests.\n    - backoff_factor (float): Backoff factor for retries.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing adjusted closing prices.\n    \"\"\"\n    try:\n        # Configure retry strategy for requests (used by yfinance internally)\n        session = requests.Session()\n        retry = Retry(\n            total=retries,\n            read=retries,\n            connect=retries,\n            backoff_factor=backoff_factor,\n            status_forcelist=(500, 502, 504),\n        )\n        adapter = HTTPAdapter(max_retries=retry)\n        session.mount('http://', adapter)\n        session.mount('https://', adapter)\n\n        # Attempt to download data\n        data = yf.download(tickers, start=start, end=end, progress=False, session=session)['Adj Close']\n\n        # Handle potential empty data\n        if isinstance(data, pd.Series):\n            data = data.to_frame()\n        if data.empty:\n            st.warning(\"No price data available for the selected portfolio. Please check the ticker symbols and date range.\")\n            return pd.DataFrame()\n        if data.index.tz is not None:\n            data.index = data.index.tz_localize(None)\n\n        # Fill missing data\n        data = data.fillna(method='ffill').fillna(method='bfill')\n        if data.isnull().values.any():\n            st.warning(\"Data contains missing values after filling. Some calculations may be affected.\")\n        return data\n    except Exception as e:\n        st.error(f\"Error downloading data: {e}\")\n        return pd.DataFrame()\n\ndef calculate_metrics(\n    returns: pd.Series, \n    cum_returns: pd.Series, \n    rf: float = 0.02, \n    benchmark_returns: pd.Series = None\n) -> dict:\n    \"\"\"\n    Calculate comprehensive portfolio metrics, optionally relative to a benchmark.\n\n    Parameters:\n    - returns (pd.Series): Portfolio returns.\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    - rf (float): Risk-free rate.\n    - benchmark_returns (pd.Series, optional): Benchmark returns.\n\n    Returns:\n    - dict: Dictionary of calculated metrics.\n    \"\"\"\n    metrics = {}\n    metrics['Start Balance'] = \"$10,000.00\"\n    if not cum_returns.empty:\n        end_balance = 10000 * cum_returns.iloc[-1]\n        years = (cum_returns.index[-1] - cum_returns.index[0]).days / 365.25\n        cagr = (end_balance / 10000) ** (1 / years) - 1 if years > 0 else np.nan\n        metrics.update({\n            'End Balance': f\"${end_balance:,.2f}\",\n            'Annualized Return (CAGR)': f\"{cagr * 100:.2f}%\" if not np.isnan(cagr) else \"N/A\",\n            'Best Year': f\"{returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).max() * 100:.2f}%\" if not returns.empty else \"N/A\",\n            'Worst Year': f\"{returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).min() * 100:.2f}%\" if not returns.empty else \"N/A\",\n            'Arithmetic Mean (Monthly)': f\"{returns.mean() * 100:.2f}%\",\n            'Arithmetic Mean (Annualized)': f\"{returns.mean() * 252 * 100:.2f}%\",\n            'Geometric Mean (Monthly)': f\"{(np.exp(np.log1p(returns).mean()) - 1) * 100:.2f}%\",\n            'Geometric Mean (Annualized)': f\"{(np.exp(np.log1p(returns).mean() * 252) - 1) * 100:.2f}%\",\n            'Standard Deviation (Monthly)': f\"{returns.std() * 100:.2f}%\",\n            'Standard Deviation (Annualized)': f\"{returns.std() * np.sqrt(252) * 100:.2f}%\",\n            'Downside Deviation (Monthly)': f\"{returns[returns < 0].std() * 100:.2f}%\",\n            'Maximum Drawdown': f\"{drawdown(cum_returns) * 100:.2f}%\" if not cum_returns.empty else \"N/A\",\n            'Sharpe Ratio': f\"{calculate_sharpe_ratio(returns, rf):.2f}\" if not cum_returns.empty else \"N/A\",\n            'Sortino Ratio': f\"{calculate_sortino_ratio(returns, rf):.2f}\" if not cum_returns.empty else \"N/A\",\n            'Gain/Loss Ratio': f\"{calculate_gain_loss_ratio(returns):.2f}\" if not cum_returns.empty else \"N/A\",\n            'Skewness': f\"{skew(returns):.2f}\" if not returns.empty else \"N/A\",\n            'Excess Kurtosis': f\"{kurtosis(returns):.2f}\" if not returns.empty else \"N/A\",\n            'Safe Withdrawal Rate': f\"{calculate_safe_withdrawal_rate(returns):.6f}%\" if not returns.empty else \"N/A\",\n            'Perpetual Withdrawal Rate': f\"{calculate_perpetual_withdrawal_rate(returns):.6f}%\" if not returns.empty else \"N/A\",\n            'Positive Periods': calculate_positive_periods(returns) if not returns.empty else \"N/A\"\n        })\n    else:\n        # Assign \"N/A\" to all metrics if cum_returns is empty\n        metrics.update({\n            'End Balance': \"N/A\",\n            'Annualized Return (CAGR)': \"N/A\",\n            # ... all other metrics set to \"N/A\"\n        })\n    \n    if benchmark_returns is not None and not benchmark_returns.empty:\n        correlation = returns.corr(benchmark_returns)\n        beta = calculate_beta(returns, benchmark_returns)\n        alpha = calculate_alpha(returns, benchmark_returns, rf)\n        r2 = calculate_r_squared(returns, benchmark_returns)\n        treynor = calculate_treynor_ratio(returns, benchmark_returns, rf)\n        calmar = calculate_calmar_ratio(returns, cum_returns)\n        m2 = calculate_modigliani_miller(returns, benchmark_returns, rf)\n        info_ratio = calculate_information_ratio(returns, benchmark_returns)\n        tracking_error = calculate_tracking_error(returns, benchmark_returns)\n        active_return = calculate_active_return(returns, benchmark_returns)\n        upside_capture = calculate_capture_ratio(returns, benchmark_returns, upside=True)\n        downside_capture = calculate_capture_ratio(returns, benchmark_returns, upside=False)\n        \n        metrics.update({\n            'Benchmark Correlation': f\"{correlation:.2f}\" if not np.isnan(correlation) else \"N/A\",\n            'Beta': f\"{beta:.2f}\" if not np.isnan(beta) else \"N/A\",\n            'Alpha (annualized)': f\"{alpha * 100:.2f}%\" if not np.isnan(alpha) else \"N/A\",\n            'R2': f\"{r2 * 100:.2f}%\" if not np.isnan(r2) else \"N/A\",\n            'Treynor Ratio': f\"{treynor:.2f}\" if not np.isnan(treynor) else \"N/A\",\n            'Calmar Ratio': f\"{calmar:.2f}\" if not np.isnan(calmar) else \"N/A\",\n            'Modigliani\u2013Modigliani Measure': f\"{m2 * 100:.2f}%\" if not np.isnan(m2) else \"N/A\",\n            'Information Ratio': f\"{info_ratio:.2f}\" if not np.isnan(info_ratio) else \"N/A\",\n            'Tracking Error': f\"{tracking_error * 100:.2f}%\" if not np.isnan(tracking_error) else \"N/A\",\n            'Active Return': f\"{active_return:.2f}%\" if not np.isnan(active_return) else \"N/A\",\n            'Upside Capture Ratio': f\"{upside_capture:.2f}%\" if not np.isnan(upside_capture) else \"N/A\",\n            'Downside Capture Ratio': f\"{downside_capture:.2f}%\" if not np.isnan(downside_capture) else \"N/A\"\n        })\n    else:\n        # Assign \"N/A\" to all benchmark metrics\n        metrics.update({\n            'Benchmark Correlation': \"N/A\",\n            'Beta': \"N/A\",\n            'Alpha (annualized)': \"N/A\",\n            'R2': \"N/A\",\n            'Treynor Ratio': \"N/A\",\n            'Calmar Ratio': \"N/A\",\n            'Modigliani\u2013Modigliani Measure': \"N/A\",\n            'Information Ratio': \"N/A\",\n            'Tracking Error': \"N/A\",\n            'Active Return': \"N/A\",\n            'Upside Capture Ratio': \"N/A\",\n            'Downside Capture Ratio': \"N/A\"\n        })\n    \n    return metrics\n\n# ----------------------------\n# Additional Helper Functions\n# ----------------------------\n\n@handle_exceptions\ndef plot_efficient_frontier_comparison(\n    simulated_metrics: pd.DataFrame,\n    optimized_metrics: tuple\n) -> None:\n    \"\"\"\n    Plot Efficient Frontier and mark the optimized portfolio.\n\n    Parameters:\n    - simulated_metrics (pd.DataFrame): Simulated portfolio metrics.\n    - optimized_metrics (tuple): Metrics of the optimized portfolio.\n    \"\"\"\n    if simulated_metrics.empty:\n        st.warning(\"No simulated metrics available to plot.\")\n        return\n\n    fig = px.scatter(\n        simulated_metrics,\n        x='Std Dev',\n        y='Return',\n        color='Sharpe Ratio',\n        color_continuous_scale='Viridis',\n        title='Efficient Frontier',\n        labels={\n            'Std Dev': 'Annualized Volatility (Std Dev)',\n            'Return': 'Annualized Return',\n            'Sharpe Ratio': 'Sharpe Ratio'\n        },\n        hover_data={\n            'Sharpe Ratio': ':.2f',\n            'Std Dev': ':.2f',\n            'Return': ':.2f'\n        },\n        template=THEME\n    )\n\n    # Add optimized portfolio if available\n    if optimized_metrics:\n        vol, ret, sharpe = optimized_metrics\n        if vol > 0 and np.isfinite(sharpe):\n            fig.add_trace(go.Scatter(\n                x=[vol],\n                y=[ret],\n                mode='markers+text',\n                marker=dict(color='red', size=14, symbol='star'),\n                name='Optimized Portfolio',\n                text=[\"Optimized\"],\n                textposition=\"top center\",\n                hoverinfo='text'\n            ))\n            fig.add_annotation(\n                x=vol,\n                y=ret,\n                text=\"Optimized\",\n                showarrow=True,\n                arrowhead=2,\n                ax=0,\n                ay=-40\n            )\n\n    fig.update_layout(\n        hovermode='x unified',\n        xaxis=dict(rangeslider=dict(visible=True), type='linear'),\n    )\n    st.plotly_chart(fig, use_container_width=True, config={\n        'responsive': True,\n        'scrollZoom': True,\n        'displayModeBar': True\n    })\n\n# ----------------------------\n# End of Helper Functions\n# ----------------------------\n\n# ----------------------------\n# Streamlit Layout\n# ----------------------------\nst.set_page_config(page_title=\"\ud83c\udfaf Portfolio Optimizer\", layout=\"wide\")\n\n# Display loading spinner\nwith st.spinner(\"\ud83c\udfaf Portfolio Optimizer is loading, please be patient...\"):\n    # Simulate loading delay if necessary\n    pass\n\nnav_steps = [\n    {\"name\": \"Configure Portfolio\", \"icon\": \"\ud83d\udcc1\"},\n    {\"name\": \"Run Backtest\", \"icon\": \"\ud83d\udcca\"},\n    {\"name\": \"Optimize Portfolio\", \"icon\": \"\ud83d\udd27\"},\n    {\"name\": \"Monte Carlo Simulations\", \"icon\": \"\ud83d\udcc8\"},\n    {\"name\": \"Risk Analysis\", \"icon\": \"\u26a0\ufe0f\"}\n]\n\nwith st.sidebar:\n    st.header(\"\ud83d\udcc2 Navigation\")\n    # Create a list of (label, value) tuples for radio buttons\n    nav_options = [(f\"{step['icon']} {step['name']}\", step['name']) for step in nav_steps]\n    labels, values = zip(*nav_options)\n    \n    # Use radio buttons for direct navigation with a callback\n    def set_step():\n        selected_label = st.session_state.nav_radio  # Access the selected label from session_state\n        selected_idx = labels.index(selected_label)\n        st.session_state.step = values[selected_idx]\n    \n    selected_label = st.radio(\"Select a Step:\", options=labels, key=\"nav_radio\", on_change=set_step)\n\ncurrent_step = st.session_state.step\n\ntickers = get_tickers()\n# Define asset_options by formatting ticker symbols with company names\nasset_options = tickers.apply(\n    lambda row: format_asset_option(row['Ticker'], row['Company Name']), \n    axis=1\n).tolist()\n\nif current_step == \"Configure Portfolio\":\n    st.title(\"\ud83c\udfaf Portfolio Optimizer\")\n    # Display success message after deletion\n    if 'delete_success' in st.session_state:\n        st.success(st.session_state.delete_success)\n        if st.session_state.get('show_balloons', False):\n            st.balloons()\n            st.session_state.show_balloons = False  # Reset balloon flag\n        del st.session_state.delete_success\n    # Display success message after editing\n    if 'edit_success' in st.session_state:\n        st.success(st.session_state.edit_success)\n        del st.session_state.edit_success\n    \n    # Initialize show_proceed in session state\n    if 'show_proceed' not in st.session_state:\n        st.session_state.show_proceed = False\n        \n    # Define callback function to hide welcome message\n    def hide_welcome():\n        st.session_state.show_proceed = True\n        st.session_state.selected_subtab = \"\u2795 Create New Portfolio\"  # Set default tab\n\n    # Display onboarding message and button if not proceeded\n    if not st.session_state.show_proceed:\n        st.info(\"Welcome! Let's get you started with your first portfolio.\")\n        st.write(\"\"\"\n            1. **Configure your portfolio** by entering a name, start date, end date, and other details.\n            2. **Select the assets** you want to include and set their allocations.\n            3. **Run a backtest** to see how your portfolio performs.\n            4. **Compare your portfolio** with a benchmark or another portfolio.\n            5. **Explore the performance metrics** and visualizations.\n        \"\"\")\n        st.button(\"Got U, Let's Explore\", key=\"explore_button\", on_click=hide_welcome)\n    # ----------------------------\n    # Configure Portfolio\n    # ----------------------------\n\n    if st.session_state.add_new_portfolio:\n        if st.session_state.add_new_portfolio:\n            if st.session_state.edit_portfolio:\n                st.header(f\"\u270f\ufe0f Editing {st.session_state.edit_portfolio} Portfolio\")\n            else:\n                st.header(\"\u2795 Create New Portfolio\")\n        \n        # Initialize or fetch the portfolio being edited\n        if st.session_state.edit_portfolio:\n            # Editing existing portfolio\n            portfolio_to_edit = next((p for p in st.session_state.portfolios if p['name'] == st.session_state.edit_portfolio), None)\n            if portfolio_to_edit:\n                new_portfolio = portfolio_to_edit.copy()\n                st.text_input(\"\ud83d\udcdb Portfolio Name\", value=new_portfolio['name'], key=\"portfolio_name\")\n                start_date_input = st.date_input(\n                    \"\ud83d\udcc5 Start Date:\",\n                    value=new_portfolio['start_date'],\n                    min_value=datetime(1900, 1, 1),\n                    help=\"Choose the start date for backtesting.\"\n                )\n                end_date_input = st.date_input(\n                    \"\ud83d\udcc5 End Date:\",\n                    value=new_portfolio['end_date'],\n                    min_value=datetime(1900, 1, 1),\n                    help=\"Choose the end date for backtesting.\"\n                )\n                rf_rate = st.number_input(\n                    \"\ud83d\udcc8 Risk-Free Rate (%)\",\n                    min_value=0.0,\n                    max_value=10.0,\n                    value=new_portfolio['rf_rate'] * 100,\n                    format=\"%.2f\",\n                    help=\"Enter the risk-free rate as a percentage.\"\n                ) / 100\n                broker_fee = st.number_input(\n                    \"\ud83d\udcb8 Broker Fee (%)\",\n                    min_value=0.0,\n                    max_value=10.0,\n                    value=new_portfolio['broker_fee'] * 100,\n                    step=0.0001,\n                    format=\"%.4f\",\n                    help=\"Enter the broker fee as a percentage per transaction.\"\n                ) / 100\n                rebalance_freq = st.selectbox(\n                    \"\ud83d\udd04 Rebalance Frequency\",\n                    options=list(frequency_mapping.keys()),\n                    index=list(frequency_mapping.values()).index(new_portfolio['rebalance_freq']),\n                    help=\"Choose how often the portfolio should be rebalanced.\"\n                )\n                benchmark_symbol = st.text_input(\n                    \"\ud83c\udfe6 Benchmark Symbol\",\n                    value=new_portfolio['benchmark_symbol'],\n                    help=\"Enter the benchmark symbol (e.g., ^GSPC for S&P 500).\"\n                )\n                selected_assets_new = st.multiselect(\n                    \"\ud83d\uddc2\ufe0f Select Assets:\",\n                    options=asset_options,\n                    format_func=lambda x: f\"{x} - {get_company_name(tickers, x)}\",\n                    default=[f\"{ticker} - {get_company_name(tickers, ticker)}\" for ticker in new_portfolio['selected']],\n                    help=\"Choose the assets you want to include in your portfolio.\"\n                )\n            else:\n                st.error(\"\u274c Selected portfolio to edit was not found.\")\n                st.session_state.add_new_portfolio = False\n                st.session_state.edit_portfolio = None\n                st.stop()\n        else:\n            # Creating a new portfolio\n            st.text_input(\"\ud83d\udcdb Portfolio Name\", key=\"portfolio_name\")\n            start_date_input = st.date_input(\n                \"\ud83d\udcc5 Start Date:\",\n                value=datetime.today() - timedelta(days=365 * 15),\n                min_value=datetime(1900, 1, 1),\n                help=\"Choose the start date for backtesting.\"\n            )\n            end_date_input = st.date_input(\n                \"\ud83d\udcc5 End Date:\",\n                value=datetime.today(),\n                min_value=datetime(1900, 1, 1),\n                help=\"Choose the end date for backtesting.\"\n            )\n            rf_rate = st.number_input(\n                \"\ud83d\udcc8 Risk-Free Rate (%)\",\n                min_value=0.0,\n                max_value=10.0,\n                value=st.session_state.default_config['rf_rate'] * 100,\n                format=\"%.2f\",\n                help=\"Enter the risk-free rate as a percentage.\"\n            ) / 100\n            broker_fee = st.number_input(\n                \"\ud83d\udcb8 Broker Fee (%)\",\n                min_value=0.0,\n                max_value=10.0,\n                value=st.session_state.default_config['broker_fee'] * 100,\n                step=0.0001,\n                format=\"%.4f\",\n                help=\"Enter the broker fee as a percentage per transaction.\"\n            ) / 100\n            rebalance_freq = st.selectbox(\n                \"\ud83d\udd04 Rebalance Frequency\",\n                options=list(frequency_mapping.keys()),\n                index=list(frequency_mapping.keys()).index('Monthly'),\n                help=\"Choose how often the portfolio should be rebalanced.\"\n            )\n            benchmark_symbol = st.text_input(\n                \"\ud83c\udfe6 Benchmark Symbol\",\n                value=st.session_state.default_config['benchmark_symbol'],\n                help=\"Enter the benchmark symbol (e.g., ^GSPC for S&P 500).\"\n            )\n            selected_assets_new = st.multiselect(\n                \"\ud83d\uddc2\ufe0f Select Assets:\",\n                options=asset_options,\n                format_func=lambda x: f\"{x} - {get_company_name(tickers, x)}\",\n                default=[],\n                help=\"Choose the assets you want to include in your portfolio.\"\n            )\n        \n        if selected_assets_new:\n            # Extract tickers from selected assets\n            selected_tickers_new = [option.split(' - ')[0] for option in selected_assets_new if ' - ' in option]\n            # Asset Allocation\n            st.subheader(\"\ud83d\udcb0 Asset Allocation\")\n            allocation_df = pd.DataFrame({\n                'Ticker': selected_tickers_new,\n                'Allocation (%)': [100.0 / len(selected_tickers_new)] * len(selected_tickers_new)\n            })\n            allocations = []\n            cols = st.columns(len(selected_tickers_new))\n            for idx, ticker in enumerate(selected_tickers_new):\n                with cols[idx]:\n                    allocation = st.number_input(\n                        f\"{ticker} (%)\",\n                        min_value=0.0,\n                        max_value=100.0,\n                        value=100.0 / len(selected_tickers_new),\n                        step=0.1,\n                        key=f\"alloc_{ticker}\",\n                        help=f\"Set the allocation percentage for {ticker}.\"\n                    )\n                    allocations.append(allocation)\n                    allocation_df.at[idx, 'Allocation (%)'] = allocation\n        \n            total_allocation_new = sum(allocations)\n            st.markdown(f\"**\ud83e\uddee Total Allocation:** {total_allocation_new:.2f}%\")\n        \n            if not np.isclose(total_allocation_new, 100.0, atol=1e-2):\n                st.warning(\"\u26a0\ufe0f Allocations must sum to 100%. Please adjust the allocations.\")\n        \n        else:\n            st.info(\"\ud83e\uddd0 Select at least one asset to allocate your portfolio.\")\n        \n        # Submit Button\n        submit_button_label = \"\u2705 Update Portfolio\" if st.session_state.edit_portfolio else \"\u2705 Add Portfolio\"\n        if st.button(submit_button_label):\n            # Validation\n            if not st.session_state.portfolio_name:\n                st.error(\"\u274c Please provide a portfolio name.\")\n            elif not selected_assets_new:\n                st.error(\"\u274c Please select at least one asset.\")\n            elif benchmark_symbol == \"\" and benchmark_symbol != \"CUSTOM\":\n                st.error(\"\u274c Please enter a benchmark symbol.\")\n            elif not np.isclose(total_allocation_new, 100.0, atol=1e-2):\n                st.error(f\"\u274c Allocations must sum to 100%. Currently sum to {total_allocation_new:.2f}%.\")\n            else:\n                # Prepare the new or updated portfolio\n                new_portfolio = {\n                    'name': st.session_state.portfolio_name,\n                    'start_date': pd.to_datetime(start_date_input),\n                    'end_date': pd.to_datetime(end_date_input),\n                    'rf_rate': rf_rate,\n                    'broker_fee': broker_fee,\n                    'benchmark_symbol': benchmark_symbol,\n                    'rebalance_freq': frequency_mapping.get(rebalance_freq, 'M'),\n                    'selected': selected_tickers_new,\n                    'allocations': allocations\n                }\n        \n                if st.session_state.edit_portfolio:\n                    # Update existing portfolio\n                    for idx, p in enumerate(st.session_state.portfolios):\n                        if p['name'] == st.session_state.edit_portfolio:\n                            st.session_state.portfolios[idx] = new_portfolio\n                            break\n                    st.success(f\"\ud83c\udf89 Portfolio '{new_portfolio['name']}' updated successfully!\")\n                    st.session_state.edit_portfolio = None  # Reset edit mode\n                else:\n                    # Add as a new portfolio\n                    if new_portfolio['name'] in [p['name'] for p in st.session_state.portfolios]:\n                        st.error(\"\u274c Portfolio name already exists. Please choose a unique name.\")\n                    else:\n                        st.session_state.portfolios.append(new_portfolio)\n                        st.success(f\"\ud83c\udf89 Portfolio '{new_portfolio['name']}' added successfully!\")\n        \n                st.balloons()\n                st.session_state.add_new_portfolio = False  # Hide the form\n                st.rerun()\n        \n        if st.button(\"\u274c Cancel\"):\n            st.session_state.add_new_portfolio = False\n            st.session_state.edit_portfolio = None\n            st.rerun()\n    \n    # Show portfolio management interface if proceeded\n    elif st.session_state.show_proceed:\n        # Show existing portfolios if any\n        if st.session_state.portfolios:\n            st.subheader(\"\ud83d\udcc1 Manage Existing Portfolios\")\n            portfolio_names = [p['name'] for p in st.session_state.portfolios]\n            selected_portfolio = st.selectbox(\"\ud83d\udd0d Select a Portfolio\", portfolio_names)\n            \n            if selected_portfolio:\n                portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)\n                \n                with st.expander(f\"\u2139\ufe0f Details for **{selected_portfolio}**\"):\n                    st.write(f\"**Start Date:** {portfolio['start_date'].strftime('%Y-%m-%d')}\")\n                    st.write(f\"**End Date:** {portfolio['end_date'].strftime('%Y-%m-%d')}\")\n                    st.write(f\"**Benchmark:** {portfolio['benchmark_symbol']}\")\n                    st.write(f\"**Rebalance Frequency:** {rebalance_freq_inverse_mapping.get(portfolio['rebalance_freq'], portfolio['rebalance_freq'])}\")\n                    st.write(\"**Allocations:**\")\n                    allocation_df = pd.DataFrame({\n                        'Ticker': portfolio['selected'],\n                        'Allocation (%)': portfolio['allocations']\n                    })\n                    st.table(allocation_df)\n                \n                # Action Buttons with Icons\n                action_col0, action_col1, action_col2, action_col3 = st.columns(4)\n\n                with action_col0:\n                    if st.button(\"\u2795 Create New Portfolio\", key=\"create_new_portfolio\"):\n                        st.session_state.add_new_portfolio = True\n                        st.rerun()\n\n                with action_col1:\n                    if st.button(f\"\ud83d\uddd1\ufe0f Delete {selected_portfolio}\", key=f\"delete_{selected_portfolio}\"):\n                        st.session_state.portfolios = [p for p in st.session_state.portfolios if p['name'] != selected_portfolio]\n                        st.session_state.delete_success = f\"\u2705 Portfolio '{selected_portfolio}' deleted successfully.\"\n                        st.session_state.show_balloons = True  # Optional: To control balloon display\n                        st.rerun()\n\n                with action_col2:\n                    if st.button(f\"\u270f\ufe0f Edit {selected_portfolio}\", key=f\"edit_{selected_portfolio}\"):\n                        st.session_state.edit_portfolio = selected_portfolio\n                        st.session_state.add_new_portfolio = True  # Ensure the form is visible\n                        st.rerun()\n\n                with action_col3:\n                    if st.button(f\"\ud83d\udcc4 Duplicate {selected_portfolio}\", key=f\"duplicate_{selected_portfolio}\"):\n                        duplicated_portfolio = portfolio.copy()\n                        duplicated_portfolio['name'] = f\"{selected_portfolio}_Copy\"\n                        # Ensure the new name is unique\n                        if duplicated_portfolio['name'] in portfolio_names:\n                            counter = 1\n                            while f\"{selected_portfolio}_Copy{counter}\" in portfolio_names:\n                                counter += 1\n                            duplicated_portfolio['name'] = f\"{selected_portfolio}_Copy{counter}\"\n                        st.session_state.portfolios.append(duplicated_portfolio)\n                        st.success(f\"\ud83c\udf89 Portfolio '{duplicated_portfolio['name']}' duplicated successfully!\")\n                        st.rerun()\n        \n        st.markdown(\"---\")  # Separator line for clarity\n\n    # ----------------------------\n    # Documentation\n    # ----------------------------\n    with st.expander(\"\ud83d\udcd6 Documentation\"):  \n        st.markdown(\"\"\"\n        ### Documentation\n\n        **Portfolio Configuration:**\n        - **Portfolio Name:** Enter a unique name for your portfolio.\n        - **Risk-Free Rate:** The theoretical return of an investment with zero risk, often based on government bonds.\n        - **Sharpe Ratio:** Measures the performance of an investment compared to a risk-free asset, after adjusting for its risk.\n            *Formula:* $$\\\\frac{R_p - R_f}{\\\\sigma_p}$$\n        - **VaR (Value at Risk):** Estimates the maximum potential loss over a specific time frame at a given confidence level.\n        - **CVaR (Conditional Value at Risk):** The expected loss exceeding the VaR, providing insight into tail risk.\n        - **Sortino Ratio:** Similar to the Sharpe Ratio but only penalizes downside volatility.\n            *Formula:* $$\\\\frac{R_p - R_f}{\\\\sigma_d}$$\n        - **Maximum Drawdown:** The largest peak-to-trough decline in the portfolio's value over a specific period.\n        - **Alpha:** Measures the active return on an investment compared to a market index.\n            *Formula:* $$R_p - [R_f + \\\\beta (R_m - R_f)]$$\n        - **Beta:** Indicates the volatility of an investment relative to the market.\n            *Formula:* $$\\\\beta = \\\\frac{Cov(R_p, R_m)}{Var(R_m)}$$\n        - **Information Ratio:** Measures portfolio returns beyond the returns of a benchmark, adjusted for the volatility of those returns.\n            *Formula:* $$\\\\frac{R_p - R_b}{TE}$$\n        - **Gain/Loss Ratio:** The ratio of total gains to total losses in the portfolio.\n        - **Modigliani\u2013Modigliani Measure (M\u00b2):** Adjusts the portfolio return to the risk of a benchmark, allowing for comparison.\n            *Formula:* $$M\u00b2 = \\\\text{Sharpe Ratio}_{\\\\text{Portfolio}} \\\\times \\\\sigma_{\\\\text{Benchmark}} + R_f$$\n        - **Tracking Error:** Measures the standard deviation of the difference between portfolio returns and benchmark returns.\n        - **Upside/Downside Capture Ratio:** Measures how well the portfolio captures the benchmark's positive and negative movements respectively.\n        - **Risk Factor Attribution:** Decomposes portfolio returns based on exposure to different risk factors like Momentum, Value, and Size.\n        - **Performance Attribution:** Breaks down portfolio performance by individual assets, showing each asset's contribution to overall returns.\n        - **Recommendation Engine:** Provides suggestions for portfolio adjustments to optimize performance based on current allocations and objectives.\n        \"\"\")\n\nif current_step == \"Run Backtest\":\n        st.title(\"\ud83d\udcca Run Backtest\")\n        if not st.session_state.portfolios:\n            st.warning(\"Please add at least one portfolio to run backtest.\")\n        else:\n            st.subheader(\"\ud83d\udd0d Select Portfolios to Compare\")\n            comparison_type = st.selectbox(\n                \"Comparison Type\",\n                [\"Portfolio vs Benchmark\", \"Portfolio vs Portfolio\"],\n                key=\"comparison_type\"\n            )\n\n            portfolio_names = [p['name'] for p in st.session_state.portfolios]\n\n            # Dynamic labels based on comparison_type\n            portfolio1_label = \"Select Portfolio\" if comparison_type == \"Portfolio vs Benchmark\" else \"Select First Portfolio\"\n            portfolio2_disabled = comparison_type == \"Portfolio vs Benchmark\"\n\n            portfolio1 = st.selectbox(\n                portfolio1_label,\n                portfolio_names,\n                key=\"p1\"\n            )\n\n            portfolio2 = st.selectbox(\n                \"Select Second Portfolio\",\n                portfolio_names,\n                key=\"p2\",\n                disabled=portfolio2_disabled\n            )\n\n            st.subheader(\"\u2699\ufe0f Visualization Settings\")\n            selected_time_frames = st.multiselect(\n                \"Select Time Frames for CAGR\",\n                options=['Weekly', 'Monthly', 'Quarterly', 'Annually'],\n                default=['Annually'],\n                key=\"selected_time_frames\",\n                help=\"Choose one or more time frames to view CAGR over different horizons.\"\n            )\n            selected_rolling_periods = st.multiselect(\n                \"Select Rolling Periods (Days)\",\n                options=[30, 90, 180, 252],\n                default=[252],\n                key=\"selected_rolling_periods\",\n                help=\"Choose one or more periods to view rolling metrics.\"\n            )\n\n            run = st.button(\"Run Backtest\")\n\n            if run:\n                with st.spinner(\"Running backtest...\"):\n                    if comparison_type == \"Portfolio vs Benchmark\":\n                        portfolio = next(p for p in st.session_state.portfolios if p['name'] == portfolio1)\n                        result = run_portfolio_comparison(\"vs_benchmark\", portfolio)\n                    else:\n                        if portfolio1 == portfolio2:\n                            st.error(\"Please select two different portfolios for comparison.\")\n                            st.stop()\n                        portfolio_a = next(p for p in st.session_state.portfolios if p['name'] == portfolio1)\n                        portfolio_b = next(p for p in st.session_state.portfolios if p['name'] == portfolio2)\n                        result = run_portfolio_comparison(\"vs_portfolio\", portfolio_a, portfolio_b)\n\n                    if result.get(\"error\"):\n                        st.error(result[\"error\"])\n                    else:\n                        backtest_results = result[\"backtest_results\"]\n                        display_backtest_results(backtest_results, comparison_type, portfolio1, portfolio2)\n\nif current_step == \"Optimize Portfolio\":\n    st.title(\"\ud83d\udd27 Optimize Portfolio\")\n    \n    if not st.session_state.portfolios:\n        st.warning(\"Please add at least one portfolio to optimize.\")\n    else:\n        portfolio_names = [p['name'] for p in st.session_state.portfolios]\n        selected_portfolio_name = st.selectbox(\"\ud83d\udcc1 Select Portfolio to Optimize\", portfolio_names)\n        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio_name)\n        \n        # Download and prepare data\n        price_data = download_data(\n            portfolio['selected'],\n            portfolio['start_date'],\n            portfolio['end_date']\n        )\n        \n        if price_data.empty:\n            st.error(\"No price data available for the selected portfolio.\")\n        else:\n            available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n            missing_selected = list(set(portfolio['selected']) - set(available_selected))\n            if missing_selected:\n                st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n            \n            if not available_selected:\n                st.error(\"No selected tickers have available data for the chosen date range.\")\n            else:\n                returns = price_data[available_selected].pct_change().dropna()\n                \n                st.header(\"\ud83d\ude80 Portfolio Optimization Settings\")\n                \n                # Constraints Section\n                with st.expander(\"\u2699\ufe0f Constraints\", expanded=True):\n                    constraints_col1, constraints_col2 = st.columns(2)\n                    \n                    with constraints_col1:\n                        max_weight = st.slider(\n                            \"\ud83d\udcc8 Maximum Weight per Asset (%)\",\n                            min_value=0.0,\n                            max_value=100.0,\n                            value=100.0,\n                            step=0.1,\n                            help=\"Set the maximum allocation percentage for any single asset.\"\n                        ) / 100  # Convert to decimal\n                \n                    with constraints_col2:\n                        min_weight = st.slider(\n                            \"\ud83d\udcc9 Minimum Weight per Asset (%)\",\n                            min_value=0.0,\n                            max_value=100.0,\n                            value=0.0,\n                            step=0.1,\n                            help=\"Set the minimum allocation percentage for any single asset.\"\n                        ) / 100  # Convert to decimal\n                \n                    # Validation: Ensure min_weight does not exceed max_weight\n                    if min_weight > max_weight:\n                        st.error(\"\u26a0\ufe0f **Minimum weight cannot exceed Maximum weight.** Please adjust your settings.\")\n                \n                # Objectives Section\n                with st.expander(\"\ud83c\udfaf Objectives\", expanded=True):\n                    objective_options = {\n                        \"Maximize Sharpe Ratio\": \"sharpe\",\n                        \"Minimize Variance\": \"min_variance\",\n                        \"Maximize Return\": \"max_return\",\n                        \"Minimize Drawdown\": \"min_drawdown\",\n                        \"Maximize Alpha\": \"maximize_alpha\",\n                        \"Minimize Beta\": \"minimize_beta\"\n                    }\n                    selected_objectives = st.multiselect(\n                        \"Select Optimization Objectives\",\n                        options=list(objective_options.keys()),\n                        default=[\"Maximize Sharpe Ratio\"],\n                        help=\"Choose one or more objectives to optimize your portfolio.\"\n                    )\n                    \n                    if not selected_objectives:\n                        st.warning(\"\u26a0\ufe0f Please select at least one optimization objective.\")\n                    \n                # Target Return Section\n                with st.expander(\"\ud83c\udfaf Target Return\", expanded=True):\n                    target_return = st.number_input(\n                        \"\ud83d\udcc8 Target Annual Return (%)\",\n                        min_value=0.0,\n                        max_value=100.0,\n                        value=10.0,\n                        step=0.1,\n                        help=\"Set the target annual return for portfolio optimization.\"\n                    ) / 100  # Convert to decimal\n                \n                # Optimization Trigger\n                if st.button(\"\u2705 Run Optimization\"):\n                    if not selected_objectives:\n                        st.error(\"\u274c Please select at least one optimization objective.\")\n                    else:\n                        # Map objectives to their internal codes\n                        objective_map = {v: k for k, v in objective_options.items()}\n                        objectives_selected = [objective_options[obj] for obj in selected_objectives]\n                        \n                        with st.spinner(\"\ud83e\uddee Optimizing portfolio...\"):\n                            # Fetch benchmark returns\n                            benchmark_symbol = portfolio['benchmark_symbol']\n                            benchmark_data = download_data([benchmark_symbol], portfolio['start_date'], portfolio['end_date'])\n                            \n                            if benchmark_data.empty:\n                                st.error(f\"Benchmark symbol '{benchmark_symbol}' does not have available data for the chosen period.\")\n                            else:\n                                benchmark_returns = benchmark_data[benchmark_symbol].pct_change().dropna()\n                                benchmark_returns = benchmark_returns.reindex(returns.index, method='ffill').dropna()\n    \n                                # Run optimization\n                                optimized_weights = optimize_portfolio(\n                                    returns,\n                                    benchmark_returns=benchmark_returns,\n                                    objectives=objectives_selected,\n                                    rf=portfolio['rf_rate'],\n                                    max_weight=max_weight,\n                                    min_weight=min_weight,\n                                    target_return=target_return\n                                )\n    \n                                if optimized_weights is not None:\n                                    # Ensure allocations sum to 100%\n                                    total_allocation = sum(optimized_weights)\n                                    if not np.isclose(total_allocation, 1.0, atol=1e-4):\n                                        st.warning(f\"\ud83d\udd04 Allocations sum to {total_allocation*100:.2f}%. Adjusting proportionally.\")\n                                        optimized_weights = [w / total_allocation for w in optimized_weights]\n                                    \n                                    # Display Optimized Weights\n                                    st.subheader(\"\ud83d\udcca Optimized Portfolio Allocation\")\n                                    weights_df = pd.DataFrame({\n                                        'Ticker': available_selected,\n                                        'Allocation (%)': [f\"{w*100:.2f}\" for w in optimized_weights]\n                                    })\n                                    st.table(weights_df)\n    \n                                    # Plot Allocation Pie Chart\n                                    plot_allocation_pie(\n                                        optimized_weights,\n                                        available_selected,\n                                        title=\"\ud83e\udd67 Optimized Allocation\",\n                                        hover_info=\"percent+name\"\n                                    )\n    \n                                    # Display Performance Metrics\n                                    st.subheader(\"\ud83d\udcc8 Optimized Portfolio Performance Metrics\")\n                                    optimized_return = np.dot(optimized_weights, returns.mean()) * 252\n                                    optimized_volatility = np.sqrt(np.dot(optimized_weights, np.dot(returns.cov() * 252, optimized_weights)))\n                                    optimized_sharpe = (optimized_return - portfolio['rf_rate']) / optimized_volatility if optimized_volatility > 0 else np.nan\n    \n                                    optimized_metrics = {\n                                        'Expected Annual Return (%)': f\"{optimized_return * 100:.2f}%\",\n                                        'Annualized Volatility (%)': f\"{optimized_volatility * 100:.2f}%\",\n                                        'Sharpe Ratio': f\"{optimized_sharpe:.2f}\"\n                                    }\n    \n                                    optimized_metrics_df = pd.DataFrame(list(optimized_metrics.items()), columns=['Metric', 'Value'])\n                                    st.table(optimized_metrics_df)\n    \n                                    # Store optimized weights and metrics in session state\n                                    st.session_state.backtest_results.update({\n                                        'optimized_weights': optimized_weights,\n                                        'optimized_return': optimized_return,\n                                        'optimized_volatility': optimized_volatility,\n                                        'optimized_sharpe': optimized_sharpe\n                                    })\n    \n                                    st.success(\"\ud83c\udf89 Portfolio optimization completed successfully!\")\n                                else:\n                                    st.error(\"\u274c Optimization did not return any weights.\")\n    #else:\n        #st.warning(\"Please add at least one portfolio to optimize.\")\n\nif current_step == \"Monte Carlo Simulations\":\n    st.title(\"\ud83d\udcc8 Monte Carlo Simulations\")\n    \n    if st.session_state.portfolios:\n        portfolio_names = [p['name'] for p in st.session_state.portfolios]\n        selected_portfolio = st.selectbox(\"\ud83d\udcc1 Select Portfolio for Simulation\", portfolio_names)\n        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)\n        \n        # Download and prepare data\n        price_data = download_data(\n            portfolio['selected'],\n            portfolio['start_date'],\n            portfolio['end_date']\n        )\n        \n        if price_data.empty:\n            st.error(\"No price data available for the selected portfolio.\")\n        else:\n            available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n            missing_selected = list(set(portfolio['selected']) - set(available_selected))\n            if missing_selected:\n                st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n            \n            if not available_selected:\n                st.error(\"No selected tickers have available data for the chosen date range.\")\n            else:\n                returns = price_data[available_selected].pct_change().dropna()\n                \n                # Organize simulation settings and results into tabs\n                simulation_tabs = st.tabs([\"\ud83d\udd27 Settings\", \"\ud83d\udcca Results\"])\n                \n                # Settings Tab\n                with simulation_tabs[0]:\n                    st.header(\"\ud83d\udd27 Simulation Settings\")\n                    \n                    # Simulation Parameters\n                    sim_col1, sim_col2 = st.columns(2)\n                    \n                    with sim_col1:\n                        num_simulations = st.number_input(\n                            \"\ud83d\udd22 Number of Simulations\",\n                            min_value=100,\n                            max_value=10000,\n                            value=1000,\n                            step=100,\n                            help=\"Define how many simulation paths to generate.\"\n                        )\n                        periods = st.number_input(\n                            \"\ud83d\udcc6 Number of Periods\",\n                            min_value=1,\n                            max_value=252,\n                            value=252,\n                            step=1,\n                            help=\"Set the number of periods (e.g., days) for each simulation.\"\n                        )\n                    \n                    with sim_col2:\n                        return_distribution = st.selectbox(\n                            \"\ud83d\udcca Return Distribution\",\n                            options=[\"Normal\", \"Log-Normal\"],\n                            index=0,\n                            help=\"Choose the statistical distribution for asset returns.\"\n                        )\n                        enable_mean_reversion = st.checkbox(\"\ud83d\udd04 Enable Mean Reversion\", help=\"Implement mean reversion in simulated returns.\")\n                    \n                    # Mean Reversion Parameters\n                    if enable_mean_reversion:\n                        mr_col1, mr_col2 = st.columns(2)\n                        with mr_col1:\n                            mean_reversion_speed = st.slider(\n                                \"\u26a1 Mean Reversion Speed\",\n                                min_value=0.0,\n                                max_value=1.0,\n                                value=0.1,\n                                step=0.01,\n                                help=\"Adjust the speed at which returns revert to the mean.\"\n                            )\n                        with mr_col2:\n                            long_term_mean_input = st.number_input(\n                                \"\ud83d\udcc8 Long-term Mean Return (%)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Set the target mean return for mean reversion.\"\n                            ) / 100  # Convert to decimal\n                \n                    # Time-Varying Volatility\n                    enable_time_varying_vol = st.checkbox(\"\ud83d\udcc9 Enable Time-Varying Volatility\", help=\"Allow volatility to change over time in simulations.\")\n                    if enable_time_varying_vol:\n                        vol_change_rate = st.slider(\n                            \"\ud83d\udcc8 Volatility Change Rate\",\n                            min_value=-0.5,\n                            max_value=0.5,\n                            value=0.0,\n                            step=0.01,\n                            help=\"Set the rate at which volatility changes each period.\"\n                        )\n                \n                    # Stress Testing\n                    st.subheader(\"\u26a0\ufe0f Stress Testing Parameters\")\n                    enable_stress_testing = st.checkbox(\"\u26a0\ufe0f Enable Stress Testing\")\n                    stress_shocks = {}\n                    if enable_stress_testing:\n                        selected_stress_assets = st.multiselect(\n                            \"\ud83d\udd0d Select Assets to Apply Stress Shocks\",\n                            options=available_selected,\n                            help=\"Choose assets to apply specific stress shocks.\"\n                        )\n                        for ticker in selected_stress_assets:\n                            shock = st.number_input(\n                                f\"\ud83d\udca5 Stress Shock to {ticker} (%)\",\n                                min_value=-100.0,\n                                max_value=100.0,\n                                value=0.0,\n                                step=0.1,\n                                help=f\"Define the percentage shock for {ticker}.\"\n                            ) / 100  # Convert to decimal\n                            stress_shocks[ticker] = shock\n                \n                    # Sensitivity Analysis (Optional)\n                    st.subheader(\"\ud83d\udd04 Sensitivity Analysis Parameters\")\n                    enable_sensitivity_analysis = st.checkbox(\"\ud83d\udd0e Enable Sensitivity Analysis\", help=\"Assess how changes in market factors affect portfolio risk.\")\n                    sensitivity_adjustments = {}\n                    if enable_sensitivity_analysis:\n                        sensitivity_col1, sensitivity_col2 = st.columns(2)\n                        with sensitivity_col1:\n                            interest_rate_change = st.number_input(\n                                \"\ud83d\udcb9 Change in Interest Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in interest rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        with sensitivity_col2:\n                            inflation_rate_change = st.number_input(\n                                \"\ud83d\udcc8 Change in Inflation Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in inflation rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        \n                        for ticker in available_selected:\n                            col_a, col_b = st.columns(2)\n                            with col_a:\n                                interest_sens = st.number_input(\n                                    f\"\ud83d\udcca Interest Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to interest rate changes.\"\n                                )\n                            with col_b:\n                                inflation_sens = st.number_input(\n                                    f\"\ud83d\udcca Inflation Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to inflation rate changes.\"\n                                )\n                            adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change\n                            sensitivity_adjustments[ticker] = adjustment\n                \n                    # Simulation Trigger\n                    if st.button(\"\u2705 Run Simulations\"):\n                        with st.spinner(\"\ud83e\uddee Running Monte Carlo simulations...\"):\n                            simulated_returns = monte_carlo_simulation(\n                                returns=returns, \n                                num_simulations=int(num_simulations), \n                                periods=int(periods),\n                                mean_returns=returns.mean(), \n                                cov_matrix=returns.cov(),\n                                mean_reversion=enable_mean_reversion,\n                                mean_reversion_speed=mean_reversion_speed if enable_mean_reversion else 0.0,\n                                long_term_mean=np.full(len(available_selected), long_term_mean_input / 252) if enable_mean_reversion else None,\n                                time_varying_vol=enable_time_varying_vol,\n                                vol_change_rate=vol_change_rate if enable_time_varying_vol else 0.0,\n                                stress_shocks=stress_shocks if enable_stress_testing else {},\n                                return_distribution=return_distribution.lower()\n                            )\n                            st.session_state.simulated_returns = simulated_returns\n                            st.success(\"\ud83c\udf89 Simulations completed successfully!\")\n                \n                # Results Tab\n                with simulation_tabs[1]:\n                    if 'simulated_returns' in st.session_state:\n                        simulated_returns = st.session_state.simulated_returns\n                        st.header(\"\ud83d\udcca Simulation Results\")\n        \n                        # Descriptive Statistics\n                        st.subheader(\"\ud83d\udcc8 Descriptive Statistics\")\n                        st.write(pd.Series(simulated_returns).describe())\n        \n                        # Histogram\n                        st.subheader(\"\ud83d\udcca Returns Distribution\")\n                        fig_hist = px.histogram(\n                            simulated_returns,\n                            nbins=50,\n                            title='\ud83d\udcca Simulated Returns Distribution',\n                            labels={'value': 'Simulated Returns', 'count': 'Frequency'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_hist, use_container_width=True)\n        \n                        # Cumulative Distribution Function (CDF)\n                        st.subheader(\"\ud83d\udcc9 Cumulative Distribution Function (CDF)\")\n                        fig_cdf = px.ecdf(\n                            simulated_returns,\n                            title='\ud83d\udcc9 Cumulative Distribution of Simulated Returns',\n                            labels={'value': 'Simulated Returns', 'cumcount': 'CDF'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_cdf, use_container_width=True)\n        \n                        # Box Plot\n                        st.subheader(\"\ud83d\udce6 Box Plot of Simulated Returns\")\n                        fig_box = px.box(\n                            pd.DataFrame(simulated_returns, columns=['Returns']),\n                            y='Returns',\n                            title='\ud83d\udce6 Box Plot of Simulated Returns',\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_box, use_container_width=True)\n        \n                        # Summary Statistics Table\n                        st.subheader(\"\ud83d\udcdd Summary Statistics\")\n                        summary_stats = pd.DataFrame({\n                            'Metric': ['Mean', 'Median', 'Standard Deviation', 'Minimum', 'Maximum'],\n                            'Value': [\n                                f\"{np.mean(simulated_returns):.4f}\",\n                                f\"{np.median(simulated_returns):.4f}\",\n                                f\"{np.std(simulated_returns):.4f}\",\n                                f\"{np.min(simulated_returns):.4f}\",\n                                f\"{np.max(simulated_returns):.4f}\"\n                            ]\n                        })\n                        st.table(summary_stats)\n        \n                        # Interpretation\n                        st.markdown(\"\"\"\n                        **\ud83d\udcd6 Interpretation:**\n                        - **Descriptive Statistics:** Provides an overview of the distribution of simulated portfolio returns.\n                        - **Histogram:** Visualizes the frequency distribution of returns.\n                        - **CDF:** Shows the probability that a return is less than or equal to a particular value.\n                        - **Box Plot:** Highlights the median, quartiles, and potential outliers in the return distribution.\n                        - **Summary Statistics:** Summarizes key metrics from the simulation runs.\n                        \"\"\")\n                    else:\n                        st.info(\"\ud83d\udd0d Run simulations to view results here.\")\n    #else:\n        #st.warning(\"Please add at least one portfolio to run Monte Carlo simulations.\")\n\n# Main Risk Analysis Section\nif current_step == \"Risk Analysis\":\n    st.title(\"\u26a0\ufe0f Risk Analysis\")\n    \n    if st.session_state.portfolios:\n        portfolio_names = [p['name'] for p in st.session_state.portfolios]\n        selected_portfolio = st.selectbox(\"\ud83d\udcc1 Select Portfolio for Risk Analysis\", portfolio_names)\n        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)\n        \n        # Download and prepare data\n        price_data = download_data(\n            portfolio['selected'],\n            portfolio['start_date'],\n            portfolio['end_date']\n        )\n        \n        if price_data.empty:\n            st.error(\"No price data available for the selected portfolio.\")\n        else:\n            available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n            missing_selected = list(set(portfolio['selected']) - set(available_selected))\n            if missing_selected:\n                st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n            \n            if not available_selected:\n                st.error(\"No selected tickers have available data for the chosen date range.\")\n            else:\n                returns = price_data[available_selected].pct_change().dropna()\n                \n                # Organize risk analysis into tabs\n                risk_analysis_tabs = st.tabs([\"\ud83d\udcc9 Risk Metrics\", \"\ud83d\udcc8 Visualizations\", \"\ud83d\udee0\ufe0f Sensitivity Analysis\"])\n                \n                # Risk Metrics Tab\n                with risk_analysis_tabs[0]:\n                    st.header(\"\ud83d\udcc9 Risk Metrics\")\n                    \n                    # Risk Parameters\n                    risk_params_col1, risk_params_col2 = st.columns(2)\n                    \n                    with risk_params_col1:\n                        confidence_level = st.number_input(\n                            \"\ud83d\udd0d Confidence Level for VaR/CVaR (%)\",\n                            min_value=90.0,\n                            max_value=99.0,\n                            value=95.0,\n                            step=1.0,\n                            help=\"Set the confidence level for risk metrics like VaR and CVaR.\"\n                        ) / 100  # Convert to decimal\n                    \n                    with risk_params_col2:\n                        enable_stress_testing = st.checkbox(\"\u26a0\ufe0f Enable Stress Testing\", help=\"Apply specific shocks to assets to assess portfolio resilience.\")\n                    \n                    if enable_stress_testing:\n                        st.markdown(\"#### \u26a0\ufe0f Stress Testing Shocks\")\n                        stress_shocks = {}\n                        for ticker in available_selected:\n                            shock = st.number_input(\n                                f\"\ud83d\udca5 Stress Shock to {ticker} (%)\",\n                                min_value=-100.0,\n                                max_value=100.0,\n                                value=0.0,\n                                step=0.1,\n                                help=f\"Define the percentage shock for {ticker}.\"\n                            ) / 100  # Convert to decimal\n                            stress_shocks[ticker] = shock\n                        if not any(shock != 0 for shock in stress_shocks.values()):\n                            st.warning(\"\u26a0\ufe0f No stress shocks applied. Consider adding at least one to perform stress testing.\")\n                    \n                    # Sensitivity Analysis Inputs\n                    st.subheader(\"\ud83d\udd0e Sensitivity Analysis Parameters\")\n                    enable_sensitivity_analysis = st.checkbox(\"\ud83d\udd04 Enable Sensitivity Analysis\", help=\"Assess how changes in market factors affect portfolio risk.\")\n                    sensitivity_adjustments = {}\n                    if enable_sensitivity_analysis:\n                        sensitivity_col1, sensitivity_col2 = st.columns(2)\n                        with sensitivity_col1:\n                            interest_rate_change = st.number_input(\n                                \"\ud83d\udcb9 Change in Interest Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in interest rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        with sensitivity_col2:\n                            inflation_rate_change = st.number_input(\n                                \"\ud83d\udcc8 Change in Inflation Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in inflation rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        \n                        for ticker in available_selected:\n                            col_a, col_b = st.columns(2)\n                            with col_a:\n                                interest_sens = st.number_input(\n                                    f\"\ud83d\udcca Interest Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to interest rate changes.\"\n                                )\n                            with col_b:\n                                inflation_sens = st.number_input(\n                                    f\"\ud83d\udcca Inflation Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to inflation rate changes.\"\n                                )\n                            adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change\n                            sensitivity_adjustments[ticker] = adjustment\n                \n                    # Calculate Risk Metrics Button\n                    if st.button(\"\ud83d\udcca Calculate Risk Metrics\"):\n                        with st.spinner(\"\ud83e\uddee Calculating risk metrics...\"):\n                            # Apply stress shocks if enabled\n                            portfolio_returns = returns.copy()\n                            if enable_stress_testing:\n                                for ticker, shock in stress_shocks.items():\n                                    portfolio_returns[ticker] += shock\n                            \n                            # Apply sensitivity adjustments if enabled\n                            if enable_sensitivity_analysis:\n                                for ticker, adjustment in sensitivity_adjustments.items():\n                                    portfolio_returns[ticker] += adjustment\n                            \n                            # Ensure no extreme negative returns\n                            portfolio_returns = portfolio_returns.clip(lower=-1.0)\n                            \n                            # Calculate weighted portfolio returns\n                            weights = np.array(portfolio['allocations']) / 100\n                            portfolio_returns = portfolio_returns.dot(weights)\n                            \n                            # Store in session state\n                            st.session_state.portfolio_returns = portfolio_returns\n                            \n                            # Calculate metrics\n                            cum_returns = (1 + portfolio_returns).cumprod()\n                            var = calculate_var(portfolio_returns, confidence_level)\n                            cvar = calculate_cvar(portfolio_returns, confidence_level)\n                            skewness = skew(portfolio_returns)\n                            kurt = kurtosis(portfolio_returns)\n                            annual_vol = portfolio_returns.std() * np.sqrt(252)\n                            max_drawdown_val = drawdown(cum_returns) * 100\n                            \n                            # Store metrics\n                            risk_metrics = {\n                                'Value at Risk (VaR)': f\"{var * 100:.2f}%\",\n                                'Conditional Value at Risk (CVaR)': f\"{cvar * 100:.2f}%\",\n                                'Skewness': f\"{skewness:.2f}\",\n                                'Kurtosis': f\"{kurt:.2f}\",\n                                'Annualized Volatility (%)': f\"{annual_vol * 100:.2f}%\",\n                                'Maximum Drawdown (%)': f\"{max_drawdown_val:.2f}%\"\n                            }\n                            \n                            st.session_state.risk_metrics = risk_metrics\n                            st.success(\"\u2705 Risk metrics calculated successfully!\")\n        \n                # Visualizations Tab\n                with risk_analysis_tabs[1]:\n                    st.header(\"\ud83d\udcc8 Risk Visualizations\")\n                    \n                    if 'risk_metrics' in st.session_state:\n                        var = st.session_state.risk_metrics.get('Value at Risk (VaR)', None)\n                        cvar = st.session_state.risk_metrics.get('Conditional Value at Risk (CVaR)', None)\n                        portfolio_returns_local = st.session_state.portfolio_returns\n                        cum_returns = (1 + portfolio_returns_local).cumprod()\n                        \n                        # VaR and CVaR Plot\n                        st.subheader(\"\ud83d\udcc9 Cumulative Returns with VaR and CVaR\")\n                        fig_cum = px.line(\n                            cum_returns,\n                            x=cum_returns.index,\n                            y=cum_returns,\n                            title='\ud83d\udcc9 Cumulative Returns Over Time',\n                            labels={'y': 'Cumulative Returns', 'x': 'Date'},\n                            template=THEME\n                        )\n                        if var is not None and cvar is not None:\n                            fig_cum.add_hline(\n                                y=1 + var,\n                                line_dash=\"dash\",\n                                line_color=\"red\",\n                                annotation_text=f\"VaR ({confidence_level*100:.0f}%): {var*100:.2f}%\",\n                                annotation_position=\"bottom right\"\n                            )\n                            fig_cum.add_hline(\n                                y=1 + cvar,\n                                line_dash=\"dash\",\n                                line_color=\"blue\",\n                                annotation_text=f\"CVaR ({confidence_level*100:.0f}%): {cvar*100:.2f}%\",\n                                annotation_position=\"bottom right\"\n                            )\n                        st.plotly_chart(fig_cum, use_container_width=True)\n                        \n                        # Returns Distribution with VaR and CVaR\n                        st.subheader(\"\ud83d\udcca Returns Distribution with VaR and CVaR\")\n                        fig_dist = px.histogram(\n                            portfolio_returns_local,\n                            nbins=50,\n                            title='\ud83d\udcca Simulated Returns Distribution',\n                            labels={'value': 'Returns', 'count': 'Frequency'},\n                            template=THEME\n                        )\n                        if var is not None and cvar is not None:\n                            fig_dist.add_vline(\n                                x=var,\n                                line_dash=\"dash\",\n                                line_color=\"red\",\n                                annotation_text=f\"VaR: {var*100:.2f}%\",\n                                annotation_position=\"top left\"\n                            )\n                            fig_dist.add_vline(\n                                x=cvar,\n                                line_dash=\"dash\",\n                                line_color=\"blue\",\n                                annotation_text=f\"CVaR: {cvar*100:.2f}%\",\n                                annotation_position=\"top left\"\n                            )\n                        st.plotly_chart(fig_dist, use_container_width=True)\n                        \n                        # Box Plot\n                        st.subheader(\"\ud83d\udce6 Box Plot of Portfolio Returns\")\n                        fig_box = px.box(\n                            pd.DataFrame(portfolio_returns_local, columns=['Returns']),\n                            y='Returns',\n                            title='\ud83d\udce6 Box Plot of Portfolio Returns',\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_box, use_container_width=True)\n                        \n                        # Interpretation\n                        st.markdown(\"\"\"\n                        **\ud83d\udcd6 Interpretation:**\n                        - **VaR (Value at Risk):** Represents the maximum expected loss at the specified confidence level.\n                        - **CVaR (Conditional Value at Risk):** Indicates the average loss exceeding the VaR.\n                        - **Skewness:** Measures the asymmetry of the return distribution.\n                        - **Kurtosis:** Indicates the \"tailedness\" of the return distribution.\n                        - **Annualized Volatility:** Measures the dispersion of returns, indicating risk.\n                        - **Maximum Drawdown:** Shows the largest peak-to-trough decline, reflecting potential risk exposure.\n                        \"\"\")\n                    else:\n                        st.info(\"\ud83d\udd0d Calculate risk metrics to view visualizations.\")\n                \n                # Sensitivity Analysis Tab\n                with risk_analysis_tabs[2]:\n                    st.header(\"\ud83d\udee0\ufe0f Sensitivity Analysis\")\n                    \n                    if 'portfolio_returns' in st.session_state:\n                        portfolio_returns_local = st.session_state.portfolio_returns\n                        st.subheader(\"\ud83d\udcc8 Adjusted Returns Distribution\")\n                        fig_adj_returns = px.histogram(\n                            portfolio_returns_local,\n                            nbins=50,\n                            title='\ud83d\udcc8 Adjusted Portfolio Returns Distribution',\n                            labels={'value': 'Returns', 'count': 'Frequency'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_adj_returns, use_container_width=True)\n                        \n                        st.subheader(\"\ud83d\udd04 Risk-Return Scatter Plot\")\n                        fig_risk_return = px.scatter(\n                            x=portfolio_returns_local.std() * np.sqrt(252),\n                            y=portfolio_returns_local.mean() * 252,\n                            size=[w * 100 for w in weights],\n                            color=[w * 100 for w in weights],\n                            hover_name=available_selected,\n                            title='\ud83d\udd04 Risk vs Return Scatter Plot',\n                            labels={'x': 'Annualized Volatility (Std Dev)', 'y': 'Annualized Return'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_risk_return, use_container_width=True)\n                        \n                        # Interpretation\n                        st.markdown(\"\"\"\n                        **\ud83d\udcd6 Interpretation:**\n                        - **Adjusted Returns Distribution:** Reflects how stress shocks and sensitivity adjustments affect portfolio returns.\n                        - **Risk-Return Scatter Plot:** Visualizes the relationship between risk (volatility) and return for each asset in the portfolio.\n                        - **Size & Color Indicators:** Represent the allocation percentage, highlighting each asset's influence on the overall portfolio.\n                        \"\"\")\n                    else:\n                        st.info(\"\ud83d\udd0d Calculate risk metrics to perform sensitivity analysis.\")\n    #else:\n        #st.warning(\"Please add at least one portfolio to run risk analysis.\")\n\nrefactor the above code as its way too messy now, and very un organized, many redundant/similar code as well\nPlease review my code and identify specific sections that need editing. For each section, provide the updated, full version of the code that I can directly use to replace the old version. I don't need to see the original code\u2014only the revised part.\n\nAdditionally, include clear, beginner-friendly instructions for replacing each section. Focus on where to find the code in my project files, any specific lines or keywords to look for, and exactly how to insert the new code. Keep it concise.\u00a0Thank\u00a0you!"},{"assistant_initiates":false,"asst_text":"Based on the statement, it seems that the individual perceives Anchorage (ANC) as being less safe compared to Tokyo. He highlights Tokyo's safety and contrasts it with his reluctance to walk around Anchorage at night, implying concerns about crime in Anchorage.\n\nRegarding data on crime, Anchorage, like many cities, faces challenges with certain types of crime, particularly property crime and violent crime. According to available statistics, Anchorage has experienced higher crime rates compared to national averages in the United States. Issues like theft, burglary, and assaults have been concerns in Anchorage. However, it's important to note that crime rates can vary significantly within different areas of the city and over time.\n\nIn contrast, Tokyo is often cited as one of the safest large cities in the world. Japan generally has low crime rates, particularly when it comes to violent crime, and Tokyo, as the capital, benefits from stringent law enforcement and social norms that contribute to this high level of safety.\n\nOverall, the perception shared by the individual in the statement aligns with general statistics regarding crime in both cities, though perceptions of safety can also be subjective and influenced by personal experiences and media.","behavior":"degenerate_repetition","conversation_hash":"768e08c17dd955dcd74dc620be24bde1","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1474,"n_turns_in_conversation":1,"n_words":238,"source_row_offset":2178,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:47:14 GMT","turn_id":"0ef692fe3030a548ede5b5e9","turn_index":0,"user_text":"A guy said: \"What i like about Tokyo? How safe it is. I would not walk around Anchorage at night. The crime here is bad.\"\n\nBased on this, do you think he feels ANC is unsafe? Does the data back up his claims?"},{"assistant_initiates":false,"asst_text":"```json\n{\n  \"instruction\": \"\u5728\u53e4\u5df4\u6bd4\u502b\u7684\u7e41\u69ae\u90fd\u5e02\u88e1\uff0c\u6d41\u50b3\u8457\u4e00\u500b\u795e\u79d8\u7684\u50b3\u8aaa\uff1a\u5728\u57ce\u5e02\u7684\u5730\u5e95\u6df1\u8655\u85cf\u6709\u4e00\u5ea7\u5049\u5927\u7684\u5716\u66f8\u9928\uff0c\u63ed\u793a\u8457\u5b87\u5b99\u9593\u7684\u7121\u6578\u79d8\u5bc6\u3002\u50b3\u8aaa\uff0c\u9019\u5ea7\u5716\u66f8\u9928\u88ab\u4e00\u4f4d\u53e4\u8001\u7684\u667a\u8005\u6240\u5b88\u8b77\uff0c\u53ea\u6709\u5fc3\u5730\u7d14\u6b63\u4e14\u52c7\u6c23\u975e\u51e1\u8005\u624d\u80fd\u627e\u5230\u5b83\u3002\u57ce\u5e02\u4e2d\u7684\u4eba\u5011\u5c0d\u6b64\u5145\u6eff\u656c\u754f\uff0c\u4f46\u4f60\uff0c\u8eab\u70ba\u4e00\u4f4d\u5e74\u8f15\u7684\u5192\u96aa\u8005\uff0c\u6c7a\u5fc3\u63ed\u958b\u9019\u4e00\u79d8\u5bc6\u3002\u4e00\u6b21\u5076\u7136\u7684\u6a5f\u6703\uff0c\u4f60\u5728\u5e02\u96c6\u4e0a\u807d\u5230\u4e00\u4f4d\u8001\u4eba\u4f4e\u8a9e\u8457\u95dc\u65bc\u5716\u66f8\u9928\u6240\u5728\u7684\u7dda\u7d22\uff0c\u65bc\u662f\u4f60\u5c55\u958b\u4e86\u9019\u4e00\u5834\u5192\u96aa\u4e4b\u65c5\uff0c\u8a66\u5716\u5c0b\u627e\u88ab\u907a\u5fd8\u5df2\u4e45\u7684\u5df4\u6bd4\u502b\u667a\u6167\u3002\",\n  \"input\": \"\u7a7f\u8d8a\u7e41\u83ef\u7684\u5e02\u96c6\",\n  \"output\": \"\u4f60\u6cbf\u8457\u8001\u4eba\u7684\u6307\u5f15\uff0c\u5728\u5145\u6eff\u53eb\u8ce3\u8072\u7684\u5e02\u96c6\u4e2d\u7a7f\u884c\uff0c\u4e0d\u65b7\u5c0b\u627e\u6709\u95dc\u5716\u66f8\u9928\u7684\u9032\u4e00\u6b65\u7dda\u7d22\u3002\u7d93\u904e\u4e00\u756a\u63a2\u7d22\uff0c\u4f60\u4f86\u5230\u4e86\u57ce\u5e02\u6771\u908a\u7684\u4e00\u500b\u53e4\u8001\u795e\u5edf\uff0c\u9019\u88e1\u975c\u8b10\u800c\u795e\u79d8\uff0c\u4f3c\u4e4e\u96b1\u85cf\u8457\u67d0\u7a2e\u529b\u91cf\u3002\u4f60\u975c\u975c\u5730\u89c0\u5bdf\u56db\u5468\uff0c\u767c\u73fe\u795e\u5edf\u7684\u7246\u58c1\u4e0a\u523b\u6709\u4e0d\u6613\u5bdf\u89ba\u7684\u53e4\u4ee3\u6587\u5b57\uff0c\u4f3c\u4e4e\u5728\u5f15\u5c0e\u8457\u901a\u5f80\u5716\u66f8\u9928\u7684\u79d8\u5bc6\u901a\u9053\u3002\"\n}\n```","behavior":"degenerate_repetition","conversation_hash":"4253718d70527bf77f5c82d1d6ff20b0","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":617,"n_turns_in_conversation":1,"n_words":18,"source_row_offset":2179,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:47:31 GMT","turn_id":"be57ed5b31def5d7c2beb1b2","turn_index":0,"user_text":"random seed2648\uff0c \u6211\u60f3\u8981\u88fd\u4f5cAI Dungeon\u7684\u904a\u6232\uff0c\u8acb\u5e6b\u6211\u751f\u6210\u8cc7\u6599\u96c6\uff0c\u7bc4\u4f8b\u8cc7\u6599\u70ba\uff1a{'instruction': '...','input': '...','output': '...'}\uff0c\u4e3b\u984c\u70ba\u5df4\u6bd4\u502b\u7684\u79d8\u5bc6\u6545\u4e8b\uff0cinstruction\u8acb\u5beb\u6545\u4e8b\u7684\u5b8c\u6574\u5287\u60c5\u80cc\u666f\uff0c\u4e3b\u89d2\u4e0d\u8981\u6709\u540d\u5b57\uff0c\u76e1\u91cf\u7528\u4f60\uff0cinput\u70ba\u52d5\u4f5c(\u4e0d\u7528\u4e3b\u8a5e\uff0c\u8acb\u8d85\u904e\u4e94\u500b\u5b57)\u3001output\u70ba\u63a5\u4e0b\u4f86\u6545\u4e8b\u7684\u8b8a\u5316\uff0c1\u7b46\u5c31\u597d\uff0c\u6545\u4e8b\u5287\u60c5\u5167\u5bb9\u76e1\u91cf\u5728200\u5b57\u5167 \u8acb\u7528\u7e41\u9ad4\u4e2d\u6587\uff0c\u76f4\u63a5\u56de\u8986\u6211json"},{"assistant_initiates":false,"asst_text":"\u0418\u0433\u0440\u0430 \"G4K Forlorn Duck Escape\", \u0442\u0430\u043a\u0436\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u0430\u044f \u043a\u0430\u043a \"G4K Forlorn Duck Escapee\" \u0438 \"G4K Forlorn Duck Escape | 8bGames\", \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u0431\u043e\u0439 \u0443\u0432\u043b\u0435\u043a\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u043f\u0440\u043e\u0435\u043a\u0442 \u0432 \u0436\u0430\u043d\u0440\u0435 point and click escape. \u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u043d\u0430\u044f \u0441\u0442\u0443\u0434\u0438\u0435\u0439 Games4King, \u043e\u043d\u0430 \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u0435\u0442 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f\u043c \u043f\u043e\u0433\u0440\u0443\u0437\u0438\u0442\u044c\u0441\u044f \u0432 \u0437\u0430\u0445\u0432\u0430\u0442\u044b\u0432\u0430\u044e\u0449\u0443\u044e \u0438\u0441\u0442\u043e\u0440\u0438\u044e \u043e\u0434\u0438\u043d\u043e\u043a\u043e\u0439 \u0443\u0442\u043a\u0438, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043e\u0431\u0438\u0442\u0430\u043b\u0430 \u0432 \u043b\u0435\u0441\u0443 \u043d\u0435\u043f\u043e\u0434\u0430\u043b\u0435\u043a\u0443 \u043e\u0442 \u043c\u0440\u0430\u0447\u043d\u043e\u0433\u043e \u0434\u0432\u043e\u0440\u0446\u0430. \u0412 \u043f\u043e\u0438\u0441\u043a\u0430\u0445 \u043f\u0438\u0449\u0438 \u0443\u0442\u043a\u0430 \u0437\u0430\u0431\u0440\u0435\u043b\u0430 \u0432 \u044d\u0442\u043e\u0442 \u0434\u0432\u043e\u0440\u0435\u0446 \u0438 \u043e\u043a\u0430\u0437\u0430\u043b\u0430\u0441\u044c \u0432 \u043d\u0435\u043c \u0432 \u043b\u043e\u0432\u0443\u0448\u043a\u0435. \n\n\u0412\u0430\u0448\u0430 \u0437\u0430\u0434\u0430\u0447\u0430 - \u0441\u043f\u0430\u0441\u0442\u0438 \u044d\u0442\u0443 \u043d\u0435\u0441\u0447\u0430\u0441\u0442\u043d\u0443\u044e \u0443\u0442\u043a\u0443. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u044c \u0434\u0432\u043e\u0440\u0435\u0446 \u0432 \u043f\u043e\u0438\u0441\u043a\u0430\u0445 \u0441\u043a\u0440\u044b\u0442\u044b\u0445 \u0443\u043b\u0438\u043a, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043f\u043e\u043c\u043e\u0433\u0443\u0442 \u0432\u0430\u043c \u043e\u0441\u0432\u043e\u0431\u043e\u0434\u0438\u0442\u044c \u043f\u043b\u0435\u043d\u043d\u0438\u0446\u0443. \u0412 \u0438\u0433\u0440\u0435 \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u043e \u043f\u043e\u0434\u0441\u043a\u0430\u0437\u043e\u043a, \u0437\u0430\u043c\u0430\u0441\u043a\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0432 \u0432\u0438\u0434\u0435 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u043f\u0440\u0435\u0434\u043c\u0435\u0442\u043e\u0432, \u0446\u0432\u0435\u0442\u043e\u0432 \u0438 \u0447\u0438\u0441\u0435\u043b. \u0420\u0435\u0448\u0435\u043d\u0438\u0435 \u0437\u0430\u0433\u0430\u0434\u043e\u043a \u0438 \u043e\u0441\u0432\u043e\u0431\u043e\u0436\u0434\u0435\u043d\u0438\u0435 \u0443\u0442\u043a\u0438 \u0441\u0442\u0430\u043d\u0435\u0442 \u043d\u0430\u0441\u0442\u043e\u044f\u0449\u0438\u043c \u0438\u0441\u043f\u044b\u0442\u0430\u043d\u0438\u0435\u043c \u0434\u043b\u044f \u0432\u0430\u0448\u0435\u0439 \u0432\u043d\u0438\u043c\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0438 \u0441\u043e\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438. \n\n\u041d\u0435 \u0441\u043e\u043c\u043d\u0435\u0432\u0430\u0439\u0442\u0435\u0441\u044c, \u0438\u0433\u0440\u0430 \u0431\u0443\u0434\u0435\u0442 \u043a\u0430\u043a \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e\u0439, \u0442\u0430\u043a \u0438 \u0443\u0432\u043b\u0435\u043a\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0439. \u0416\u0435\u043b\u0430\u0435\u043c \u0443\u0434\u0430\u0447\u0438 \u0438 \u043f\u0440\u0438\u044f\u0442\u043d\u043e\u0433\u043e \u0432\u0440\u0435\u043c\u044f\u043f\u0440\u0435\u043f\u0440\u043e\u0432\u043e\u0436\u0434\u0435\u043d\u0438\u044f \u0432 \u043c\u0438\u0440\u0435 \"G4K Forlorn Duck Escape\"!","behavior":"degenerate_repetition","conversation_hash":"610a90b15eeff0dde17d5ce667e8a553","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":2420,"n_turns_in_conversation":1,"n_words":372,"source_row_offset":2180,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:47:32 GMT","turn_id":"8a7a7e7145421a60e1cb382c","turn_index":0,"user_text":"\u0422\u044b \u2014 \u044d\u043a\u0441\u043f\u0435\u0440\u0442 \u043f\u043e \u0438\u0433\u0440\u0430\u043c. \u0423 \u0442\u0435\u0431\u044f \u0435\u0441\u0442\u044c \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f \u043e \u0438\u0433\u0440\u0435 \u0441 \u0440\u0430\u0437\u043d\u044b\u0445 \u0441\u0430\u0439\u0442\u043e\u0432 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 JSON, id - \u0443\u043d\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u0439 \u043d\u043e\u043c\u0435\u0440, name - \u043c\u0430\u0441\u0441\u0438\u0432 \u0438\u043c\u0435\u043d \u0438\u0433\u0440\u044b, description - \u043c\u0430\u0441\u0441\u0438\u0432 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0439 \u0438\u0433\u0440\u044b, genre - \u043c\u0430\u0441\u0441\u0438\u0432 \u0436\u0430\u043d\u0440\u043e\u0432. \u0422\u0432\u043e\u044f \u0437\u0430\u0434\u0430\u0447\u0430 \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0431\u043e\u043b\u044c\u0448\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0434\u0430\u043d\u043d\u043e\u0439 \u0442\u0435\u0431\u0435 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438, \u0442\u0430\u043a \u0447\u0442\u043e \u0431\u044b \u043e\u043d\u043e \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u043b\u0430 \u0432\u0441\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u0431\u0435\u0437 \u0434\u0443\u0431\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f.  ```{\"_id\":\"1\",\"name\":[\"g4k forlorn duck escape\",\"g4k forlorn duck escapee\",\"g4k forlorn duck escape | 8bgames\"],\"description\":[\"games4king - g4k forlorn duck escape game is another point and click escape game developed by games4king.\",\"games4king \u2013 g4k forlorn duck escape game is another point and click escape game developed by games4king.\u00a0 \u00a0 a forlorn duck lived in a forest near a terrible palace. the forlorn duck went through the palace in search of food. the forlorn duck was trapped in a room in the terrible palace. you must rescue the trapped forlorn duck. there are many clues hidden in the palace to save the forlorn duck. find the clues that are hidden and capture the forlorn duck and win the game. the scores are signed from numbers and colors and materials. this game will be interesting and exciting. good luck and have fun!\",\"games4king - g4k forlorn duck escape game is another point and click escape game developed by games4king. a forlorn duck lived in a forest near a terrible palace. the forlorn duck went through the palace in search of food. the forlorn duck was trapped in a room in the terrible palace. you must rescue\"],\"genre\":[]}```"},{"assistant_initiates":false,"asst_text":"Known for his remarkable talents as well as his numerous romantic entanglements, Richard Burton's personal life was almost as famous as his acting career. During his time, he was often compared to Warren Beatty for his numerous affairs. Burton had an almost endless appetite for new romantic encounters, reportedly becoming involved with at least one new woman every other day during his peak years. This lifestyle of frequent affairs was not hindered by his marital status. Even while married to his first wife, Sybil Williams, Burton's reputation for promiscuity was well-known. His roving eye was especially drawn to the leading ladies he worked with on set, leading to a multitude of affairs. Despite experiencing several tumultuous marriages, Burton seemed unable to curb his passionate pursuits. His insatiable appetite for romantic escapades not only defined much of his personal life but also contributed significantly to his notoriety in Hollywood history. Reply finished.","behavior":"degenerate_repetition","conversation_hash":"518fc72b02b2e0daa11b3e1fb393c02d","conversation_redacted":false,"conversation_toxic":false,"country":"Denmark","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":3496,"n_turns_in_conversation":1,"n_words":523,"source_row_offset":2181,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:47:44 GMT","turn_id":"b442d5c2ce244c2c38c0d10d","turn_index":0,"user_text":"Create a long detailed text about actor Richard Burton with the following summary. The title of the video is\"50 Worst S*x Addicts In Hollywood History\". Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Richard Burton] Summary: [Richard Burton was notorious for his numerous affairs with leading ladies, including while married to his first wife Sybil Williams. His promiscuity rivaled Warren Beatty's, with at least one new woman every other day during his heyday. Despite tumultuous marriages, Burton's appetite for sex was insatiable.]\n\n            Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks. \n            In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.\n            Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response.\n            Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. \n            The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. \n            Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. \n            It is very important to provide only the final output without any additional comments or remarks. \n            Please provide the text directly without any labels such as \"Conclusion:\", \"Introduction:\", or \"Transition:\". Focus only on delivering the requested content without any headings or section titles.\n            Please respond to my question, and always conclude your reply with the words \"Reply finished\" to indicate that your response is complete. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}\n         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you."},{"assistant_initiates":false,"asst_text":"Hello! I'm just a program, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?","behavior":"degenerate_repetition","conversation_hash":"f8b9bdd831a4a6c624a33669df4485a9","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":133,"n_turns_in_conversation":1,"n_words":28,"source_row_offset":2182,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:47:57 GMT","turn_id":"d2d6ad696d9009b646a3b2f6","turn_index":0,"user_text":"hi, how are you?"},{"assistant_initiates":false,"asst_text":"Hi! I'm just a computer program, so I don't have feelings, but I'm here to help you. How can I assist you today?","behavior":"degenerate_repetition","conversation_hash":"166618f192c63736960bb69369766d72","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":129,"n_turns_in_conversation":1,"n_words":27,"source_row_offset":2183,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:48:00 GMT","turn_id":"13356a16f3eb29a38d808410","turn_index":0,"user_text":"hi, how are you?"},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"instruction\": 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   \"input\": \"\u8dd1\u9032\u5145\u6eff\u9713\u8679\u71c8\u7684\u5df7\u5f04\",\n    \"output\": \"\u4f60\u885d\u9032\u4e86\u4e00\u689d\u72f9\u7a84\u7684\u5c0f\u5df7\uff0c\u9713\u8679\u71c8\u5149\u5728\u7246\u58c1\u548c\u5730\u9762\u4e0a\u53cd\u5c04\u51fa\u5947\u7570\u7684\u5149\u5f71\uff0c\u8b93\u4f60\u66ab\u6642\u7121\u6cd5\u5206\u8fa8\u65b9\u5411\u3002\u6b63\u7576\u4f60\u8a66\u5716\u5c0b\u627e\u4e0b\u4e00\u6b65\u7684\u7dda\u7d22\u6642\uff0c\u5df7\u5b50\u88e1\u97ff\u8d77\u4e86\u8f15\u5fae\u7684\u6a5f\u68b0\u8072\u97ff\uff0c\u90a3\u662f\u7121\u4eba\u6a5f\u7684\u5de1\u908f\u8072\u97f3\u3002\u4f60\u7acb\u523b\u8cbc\u8fd1\u7246\u58c1\uff0c\u5c4f\u4f4f\u547c\u5438\uff0c\u5e0c\u671b\u80fd\u5920\u907f\u958b\u5b83\u7684\u7d05\u5916\u7dda\u6383\u63cf\u3002\u6b64\u6642\uff0c\u4f60\u770b\u5230\u524d\u65b9\u4e0d\u9060\u8655\u6709\u500b\u5c0f\u578b\u7684\u96fb\u5b50\u5bc6\u78bc\u9396\uff0c\u53ef\u80fd\u85cf\u8457\u4f60\u901a\u5f80\u4e0b\u4e00\u500b\u968e\u6bb5\u7684\u95dc\u9375\u3002\"\n}\n```","behavior":"degenerate_repetition","conversation_hash":"df1aa0745709e7c0e215bb2fbe7bb463","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":699,"n_turns_in_conversation":1,"n_words":18,"source_row_offset":2184,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:48:10 GMT","turn_id":"d324fdd9dcba44a5cf324cd4","turn_index":0,"user_text":"random seed2655\uff0c \u6211\u60f3\u8981\u88fd\u4f5cAI Dungeon\u7684\u904a\u6232\uff0c\u8acb\u5e6b\u6211\u751f\u6210\u8cc7\u6599\u96c6\uff0c\u7bc4\u4f8b\u8cc7\u6599\u70ba\uff1a{'instruction': '...','input': '...','output': '...'}\uff0c\u4e3b\u984c\u70ba\u90fd\u5e02\u751f\u5b58\u6311\u6230\u6545\u4e8b\uff0cinstruction\u8acb\u5beb\u6545\u4e8b\u7684\u5b8c\u6574\u5287\u60c5\u80cc\u666f\uff0c\u4e3b\u89d2\u4e0d\u8981\u6709\u540d\u5b57\uff0c\u76e1\u91cf\u7528\u4f60\uff0cinput\u70ba\u52d5\u4f5c(\u4e0d\u7528\u4e3b\u8a5e\uff0c\u8acb\u8d85\u904e\u4e94\u500b\u5b57)\u3001output\u70ba\u63a5\u4e0b\u4f86\u6545\u4e8b\u7684\u8b8a\u5316\uff0c1\u7b46\u5c31\u597d\uff0c\u6545\u4e8b\u5287\u60c5\u5167\u5bb9\u76e1\u91cf\u5728200\u5b57\u5167 \u8acb\u7528\u7e41\u9ad4\u4e2d\u6587\uff0c\u76f4\u63a5\u56de\u8986\u6211json"},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"instruction\": \"\u5728\u7e41\u83ef\u7684\u90fd\u5e02\u4e2d\uff0c\u96b1\u85cf\u8457\u8af8\u591a\u672a\u89e3\u7684\u8b0e\u5718\u3002\u9019\u88e1\u5145\u65a5\u8457\u9ed1\u6697\u4ea4\u6613\u3001\u672a\u7834\u6848\u4ef6\u8207\u795e\u79d8\u6d88\u5931\u7684\u4eba\u7fa4\u3002\u4f60\u662f\u4e00\u540d\u7d93\u9a57\u8c50\u5bcc\u7684\u5075\u63a2\uff0c\u5c0d\u57ce\u5e02\u7684\u6bcf\u500b\u89d2\u843d\u4e86\u5982\u6307\u638c\uff0c\u537b\u7e3d\u662f\u559c\u6b61\u7368\u81ea\u884c\u52d5\u3002\u67d0\u5929\uff0c\u4e00\u500b\u795e\u79d8\u7684\u4f86\u4fe1\u727d\u52d5\u8457\u4f60\u7684\u795e\u7d93\uff0c\u4e0a\u9762\u50c5\u6709\u4e00\u53e5\u8a71\uff1a\u300e\u57ce\u5e02\u7684\u65cb\u5f8b\u85cf\u65bc\u7121\u8072\u4e4b\u6240\u3002\u300f\u9019\u662f\u6311\u6230\uff0c\u4e5f\u662f\u9080\u7d04\u3002\u4e00\u5834\u667a\u529b\u8207\u81bd\u91cf\u7684\u8f03\u91cf\uff0c\u5c07\u5728\u9019\u500b\u591c\u5e55\u4e0b\u6084\u7136\u5c55\u958b\u3002\u4f60\u5fc5\u9808\u4f9d\u9760\u76f4\u89ba\u8207\u773c\u5149\uff0c\u6253\u7834\u6c89\u9ed8\u4e2d\u7684\u98a8\u66b4\uff0c\u89e3\u958b\u8b0e\u984c\uff0c\u627e\u51fa\u6240\u6709\u80cc\u5f8c\u7684\u771f\u76f8\uff0c\u4e26\u5728\u9019\u7247\u71c8\u7d05\u9152\u7da0\u4e2d\u5c0b\u5f97\u4e00\u7d72\u6b63\u7fa9\u7684\u66d9\u5149\u3002\",\n    \"input\": \"\u8d70\u9032\u5e7d\u6697\u7684\u5c0f\u5df7\u6df1\u8655\",\n    \"output\": \"\u4f60\u96d9\u773c\u8b66\u60d5\u5730\u6383\u8996\u5468\u570d\uff0c\u6bcf\u500b\u9670\u6697\u89d2\u843d\u90fd\u53ef\u80fd\u96b1\u85cf\u8457\u7dda\u7d22\u3002\u7246\u58c1\u4e0a\u7684\u5857\u9d09\u4f3c\u4e4e\u5728\u662d\u793a\u4ec0\u9ebc\uff0c\u90a3\u4e9b\u770b\u4f3c\u96a8\u610f\u7684\u984f\u8272\u8207\u7dda\u689d\uff0c\u62fc\u6e4a\u51fa\u4e00\u689d\u57ce\u5e02\u4e2d\u7684\u7955\u5bc6\u8def\u7dda\u3002\u4f60\u77e5\u9053\uff0c\u9019\u4e0d\u662f\u55ae\u7d14\u7684\u8857\u982d\u85dd\u8853\uff0c\u800c\u662f\u67d0\u7a2e\u7d44\u7e54\u7559\u4e0b\u7684\u8a0a\u606f\u3002\u8033\u908a\u50b3\u4f86\u8f15\u5fae\u7684\u97ff\u52d5\uff0c\u4f60\u5c4f\u4f4f\u547c\u5438\uff0c\u4e26\u5728\u5fc3\u4e2d\u76e4\u7b97\u4e0b\u4e00\u6b65\u8a72\u600e\u9ebc\u8d70\u3002\"\n}\n```","behavior":"degenerate_repetition","conversation_hash":"50f0585e962219c36c508ae1c0161533","conversation_redacted":false,"conversation_toxic":false,"country":"Malaysia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":640,"n_turns_in_conversation":1,"n_words":18,"source_row_offset":2185,"source_shard":"turns-00044.parquet","timestamp":"Mon, 04 Nov 2024 04:48:17 GMT","turn_id":"2708a65d64edb917daa93e08","turn_index":0,"user_text":"random seed2657\uff0c \u6211\u60f3\u8981\u88fd\u4f5cAI Dungeon\u7684\u904a\u6232\uff0c\u8acb\u5e6b\u6211\u751f\u6210\u8cc7\u6599\u96c6\uff0c\u7bc4\u4f8b\u8cc7\u6599\u70ba\uff1a{'instruction': '...','input': '...','output': '...'}\uff0c\u4e3b\u984c\u70ba\u7368\u884c\u5075\u63a2\u7684\u90fd\u5e02\u8b0e\u5718\u6545\u4e8b\uff0cinstruction\u8acb\u5beb\u6545\u4e8b\u7684\u5b8c\u6574\u5287\u60c5\u80cc\u666f\uff0c\u4e3b\u89d2\u4e0d\u8981\u6709\u540d\u5b57\uff0c\u76e1\u91cf\u7528\u4f60\uff0cinput\u70ba\u52d5\u4f5c(\u4e0d\u7528\u4e3b\u8a5e\uff0c\u8acb\u8d85\u904e\u4e94\u500b\u5b57)\u3001output\u70ba\u63a5\u4e0b\u4f86\u6545\u4e8b\u7684\u8b8a\u5316\uff0c1\u7b46\u5c31\u597d\uff0c\u6545\u4e8b\u5287\u60c5\u5167\u5bb9\u76e1\u91cf\u5728200\u5b57\u5167 \u8acb\u7528\u7e41\u9ad4\u4e2d\u6587\uff0c\u76f4\u63a5\u56de\u8986\u6211json"}],"shard":{"index":44,"name":"turns-00044.parquet","rows":44850}}