Respan Dataset Explorer

Select one behavior. Every returned turn has one binary label: Present or Absent. Source: final dense boolean release.

5,167,182physical rows
86shards
0.00%qualified row coverage
0.00%qualified cell coverage
Random row JSON API

turns-00062.parquet:23786

9ecc3c696ffdca1963e96aa3
turn 107/112gpt-4o-mini-2024-07-18EnglishIreland736 words
degenerate_repetitionAbsentFinal dense release
USER
MSc Finance graduate with a strong foundation in financial modeling, statistical analysis, and market
research. Proficient in Excel, STATA, Bloomberg, and Refinitiv Eikon, with academic experience in
portfolio optimization, cash flow forecasting, and M&A analysis. Detail-oriented and skilled in delivering
precise reports and analyses within tight deadlines.

Tailor above summary according to below job description and i have done msc in finance only add those skills which i will gain through that…do not add what i do not have …keep it short but attractive.........As well as make it ATS friendly that it can pass through....keep it very short and direct ...Put pressure on what i can give and...try to Use exact words that are used in job description below without affecting the word limit with summary 

Pensions Administrator - Aviva (EG)

About the job
This job is sourced from a job board. Learn More
Aviva Life & Pensions Ireland have a fantastic opportunity for a career driven individual to join us in our Pensions Administration team as a Pensions Administrator.

This position is based on site in our Cherrywood office, with an opportunity to benefit from Avivas smart working arrangements which offers a mix of home and office working. While this role is in the greater Dublin area, it would suit those with financial services experience living in Wicklow/Kildare/north Wexford area also. We have free parking for staff who commute by car or motorcycle or there are public transport links close-by (the LUAS Green Line and 84a bus stop is one minute away).

The position will report to a Pension Team Leader and includes full ownership for the administration of a Pension portfolio. The role will involve working closely with our administration partners and Financial Brokers to deliver a first-class customer service.

Duties & Responsibilities

Effective management of work queues, ensuring prompt & accurate results.

Build positive relationships with key stakeholders internally and externally.

Consistently demonstrate task ownership and a solution focused approach.

Continually monitor and check all Risk events have been properly recorded.

Ensure we meet our obligations under Pensions Act & CPC.

Skills & Experience

Broad knowledge of the Pension environment is desirable not essential.

Pension qualification an advantage (APA/ QFA).

Excellent verbal and written communication skills.

Good planning and time management skills and ability to quickly build positive relationships at all levels.

High energy and passion for customer service and delivery by working under pressure and meeting deadlines in a fast-paced environment.

What will you get for this role?

A competitive market leading salary depending on skills, experience, and qualifications.

Generous pension (employee contribution matching of up to 14% if you contribute 8%)

Annual performance related bonus and pay review.

Holiday allowance of 25 days plus bank holidays, and the option to buy/sell up to 5 additional days.

Free car parking

Generous Health & dental insurance contributions after six months

Aviva Matching Share Plan and Save As You Earn scheme and Approved Profit Share Scheme

Cycle to Work benefit scheme.

TaxSaver Travel Scheme

Family friendly parents (matching paternity and maternity leave) and carers leave.

Up to 40% discount for some Aviva products plus discounts for Friends and Family

Employee Assistance Programme

Volunteering days

Professional qualification support and transparent career progression plans

Aviva is for Everyone

Our diversity and inclusion policies and initiatives are shaping an environment where everyone feels welcome regardless of age, disability, race, ethnicity, gender, gender identity, religion, culture, sexual orientation, national origin, marital status, pregnancy, maternity or those with other caring responsibilities. Our approach helps to ensure that Aviva is a place which values difference and provides equal opportunities for all.

We flex locations, hours and working patterns to suit our customers, business, and you. Our smart working combines the benefits of flexibility with time together with colleagues.

We prefer all applications to be submitted online, however if you require an alternative method of applying, please contact Dylan Wood on .

Skills 
Skills associated with the job post
Communication · Customer Service · Financial Management · Financial Services · Financial Statements · Interpersonal Skills · Written Communication · Defined Benefit · Defined Contribution · Pension Funds
ASSISTANT
MSc Finance graduate with a strong foundation in financial modeling, statistical analysis, and customer service. Proficient in Excel, STATA, Bloomberg, and Refinitiv Eikon, with academic experience in portfolio optimization and financial analysis. Detail-oriented and skilled in building relationships, I am ready to manage pension administration tasks and deliver exceptional support in a fast-paced environment.

turns-00062.parquet:23787

43c6782415212b5a9a84a49e
turn 108/112gpt-4o-mini-2024-07-18EnglishIreland104 words
degenerate_repetitionAbsentFinal dense release
USER
Skills: Microsoft Excel, PowerPoint, Financial Modeling, Financial Reporting & Analysis, Data Analysis, Cash Flow Forecasting, Balance Sheet Review, Risk Management, M&A Analysis, SQL, Power BI, Cross-functional Collaboration, Problem Solving, STATA, R Studio.

Customize it according to the job description i gave it to you above without loosing focus and disturbing the length...keep it same ....keep software skills same 
\
ASSISTANT
Skills: Microsoft Excel, PowerPoint, Financial Modeling, Financial Reporting & Analysis, Data Analysis, Cash Flow Forecasting, Balance Sheet Review, Risk Management, M&A Analysis, SQL, Power BI, Cross-functional Collaboration, Problem Solving, STATA, R Studio, customer service, effective communication, and knowledge of financial management in the pensions environment.

turns-00062.parquet:23788

2c729a8cc6c60c3e5ec68fb9
turn 109/112gpt-4o-mini-2024-07-18EnglishIreland213 words
degenerate_repetitionAbsentFinal dense release
USER
M&A Analysis – PepsiCo
Led strategic M&A recommendations by identifying high-potential targets and synergy gains using Refinitiv Eikon and Excel; strengthened financial modeling and problem-solving skills.

Corporate Financial Health – PepsiCo
Delivered an in-depth analysis of PepsiCo’s financial health by studying its financial statements, stock performance, and governance trends using Refinitiv Eikon, Excel, and Word. Strengthened expertise in balance sheet review and comprehensive financial evaluation, supporting compliance efforts.

Portfolio Optimization
Created a smart investment portfolio for a client using R and Excel. Applied advanced risk management techniques, analyzing data to optimize strategy while minimizing risks, showcasing design and application expertise.

Customize it according to the job description i gave it to you above without loosing focus and disturbing the length...keep it same 
ASSISTANT
**M&A Analysis – PepsiCo**  
Identified high-potential targets and synergy gains for strategic M&A recommendations using Refinitiv Eikon and Excel; enhanced financial modeling and problem-solving skills relevant to financial administration.

**Corporate Financial Health – PepsiCo**  
Analyzed PepsiCo’s financial health by reviewing financial statements, stock performance, and governance trends with Refinitiv Eikon, Excel, and Word. Strengthened balance sheet review and compliance support.

**Portfolio Optimization**  
Developed an optimized investment portfolio for a client using R and Excel. Applied risk management techniques to analyze data and optimize strategies, demonstrating proficiency in financial analysis and reporting.

turns-00062.parquet:23789

4ab01cdede30631aca875f30
turn 110/112gpt-4o-mini-2024-07-18EnglishIreland85 words
degenerate_repetitionAbsentFinal dense release
USER
Shorten it without loosing focus 
ASSISTANT
**M&A Analysis – PepsiCo**  
Identified high-potential targets and synergy gains for M&A recommendations using Refinitiv Eikon and Excel; enhanced financial modeling and problem-solving skills for financial administration.

**Corporate Financial Health – PepsiCo**  
Analyzed financial health by reviewing statements, stock performance, and governance trends with Refinitiv Eikon, Excel, and Word. Strengthened balance sheet review and compliance support.

**Portfolio Optimization**  
Optimized a client’s investment portfolio using R and Excel. Applied risk management techniques to analyze data and strategies, showcasing financial analysis expertise.

turns-00062.parquet:23790

b73d90f5944351ac7abbb4c7
turn 111/112gpt-4o-mini-2024-07-18EnglishIreland152 words
degenerate_repetitionAbsentFinal dense release
USER
●     Developed financial models and dashboards with Excel and Power BI, ensuring accurate reporting and reconciliation.
●	Managed inquiries for 2,000+ customers in North America and India, enhancing service quality.
●	Used pivot tables and VLOOKUP for financial reconciliation and revenue analysis, providing decision insights

Customize it according to the job description i gave it to you above without loosing focus and disturbing the length...keep it same .....i want just 3 short lines

Keep it short and attractive and the main thing relevant to the job i am applying ....don't write and show the relavancy show the work i have done will help me in that role
ASSISTANT
- Developed financial models and dashboards with Excel and Power BI for accurate reporting and reconciliation.  
- Managed inquiries for 2,000+ customers, enhancing service quality in a fast-paced environment.  
- Used pivot tables and VLOOKUP for financial reconciliation and analysis, delivering key insights for decision-making.  

turns-00062.parquet:23791

42d394eeb7cd533b43224af6
turn 112/112gpt-4o-mini-2024-07-18EnglishIreland42 words
degenerate_repetitionAbsentFinal dense release
USER
Shorten it without loosing focus 
ASSISTANT
- Developed financial models and dashboards with Excel and Power BI for accurate reporting.  
- Managed inquiries for 2,000+ customers, enhancing service quality.  
- Used pivot tables and VLOOKUP for financial reconciliation and analysis, providing key insights.  

turns-00062.parquet:23792

7fa002e5507a0e3931f2ebfa
turn 1/1gpt-4o-mini-2024-07-18EnglishUnited States25 words
degenerate_repetitionAbsentFinal dense release
USER
User: hi, how are you?
User: hi, how are you?
Assistant:
ASSISTANT
Hello! I'm here and ready to help you. How can I assist you today?

turns-00062.parquet:23793

7e3ad801618efbf840521f41
turn 1/1gpt-4o-mini-2024-07-18FrenchAustralia301 words
degenerate_repetitionAbsentFinal dense release
USER
User: comment créé une Database sur Render
User: Répondre à l'utilisateur.
Assistant:
ASSISTANT
Pour créer une base de données sur Render, suivez les étapes ci-dessous :

1. **Créez un compte Render** : Si vous n'avez pas déjà un compte, rendez-vous sur [Render.com](https://render.com) et inscrivez-vous.

2. **Connectez-vous** : Après avoir créé votre compte, connectez-vous à votre tableau de bord.

3. **Créer un nouveau service** :
   - Cliquez sur le bouton "New" dans le coin supérieur droit de votre tableau de bord.
   - Sélectionnez "Database" dans le menu déroulant.

4. **Choisissez le type de base de données** : Render propose plusieurs types de bases de données, comme PostgreSQL et MySQL. Sélectionnez celui qui vous convient le mieux.

5. **Configurer la base de données** :
   - Donnez un nom à votre base de données.
   - Choisissez un plan (il existe des options gratuites et payantes).
   - Configurez d'autres paramètres selon vos besoins (taille, région, etc.).

6. **Vérifiez la configuration** : Assurez-vous que toutes vos informations sont correctes, puis cliquez sur "Create Database".

7. **Accéder à votre base de données** : Une fois créée, vous pouvez accéder aux informations de connexion dans le tableau de bord. Vous pouvez également utiliser les outils fournis ou vos propres outils de gestion de base de données pour interagir avec elle.

8. **Connectez votre application** : Pour utiliser la base de données dans votre application, utilisez les informations de connexion fournies par Render (hôte, port, nom de la base de données, utilisateur et mot de passe).

9. **Développez et gérez votre base de données** : Vous pouvez maintenant créer des tables, insérer des données et exécuter des requêtes selon vos besoins.

N'hésitez pas à consulter la documentation officielle de Render pour des instructions plus détaillées et des conseils spécifiques en fonction du type de base de données que vous choisissez.

turns-00062.parquet:23794

02d1e5804277260e8230b31b
turn 1/1gpt-4o-mini-2024-07-18EnglishAustralia35 words
degenerate_repetitionAbsentFinal dense release
USER
User: hi, how are you?
User: hi, how are you?
Assistant:
ASSISTANT
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?

turns-00062.parquet:23795

9c5434590acd026b9a200898
turn 1/5gpt-4o-mini-2024-07-18EnglishUnited States2688 words
degenerate_repetitionAbsentFinal dense release
USER
import asyncio
import json
import base64
import base58
from datetime import datetime
import websockets
import torch
import os
from typing import Optional
from dataclasses import dataclass
from solana_logger import logger
from config_loader import config
from solana_rust_bot import TradingCore, TradeParams
from solana_cli import SolanaCLI


@dataclass
class TradeOpportunity:
    victim_data: bytes
    amount: float
    token_a_mint: str
    token_b_mint: str
    net_profit: float
    instruction_type: int
    transaction_id: str


class MEVBot:
    def __init__(self):
        # Load configuration
        self.load_config()

        # Initialize core components
        self.init_components()

        # Trading parameters from config
        trade_config = config.get_trade_config()
        self.POSITION_SIZE = trade_config['front_run_amount']
        self.MIN_VICTIM_SIZE = trade_config['min_victim_size']
        self.MAX_VICTIM_SIZE = trade_config['max_victim_size']
        self.MIN_PROFIT = trade_config['min_profit']
        self.LAMPORTS_PER_SOL = 1_000_000_000

        # Track processed transactions
        self.processed_txs = set()

        # Balance monitoring
        balance_config = config.get_balance_config()
        self.min_sol_balance = balance_config['min_sol_balance']
        self.min_wsol_balance = balance_config['min_wsol_balance']
        self.balances = {"SOL": 0.0, "WSOL": 0.0}
        self.is_trading_paused = False

        # Performance tracking
        self.active = True
        self.trades_found = 0
        self.trades_executed = 0
        self.successful_trades = 0
        self.total_profit = 0.0
        self.start_time = datetime.now()

        # Concurrency control
        self.trade_lock = asyncio.Lock()
        self.MAX_ACTIVE_TRADES = 1
        self.active_trades = 0

        # Setup CUDA
        self._setup_cuda()

        self.log_startup()

    def load_config(self):
        """Load configuration and network settings"""
        network_config = config.get_network_config()
        self.ws_url = network_config['ws_url']
        self.api_key = network_config['api_key']
        self.program_id = config.get('raydium')

        # Account configuration
        self.wallet = config.get('wallet')
        self.wsol_account = config.get('wsol_token_account')

        # Market configuration
        market_accounts = config.get_market_accounts('SOL-WSOL')
        self.token_a_mint = market_accounts['token_a_mint']
        self.token_b_mint = market_accounts['token_b_mint']

    def init_components(self):
        """Initialize core components"""
        try:
            # Initialize trading core with RPC URL from config
            self.trading_core = TradingCore(config.get_network_config()['rpc_url'])

            # Initialize Solana CLI helper
            self.solana_cli = SolanaCLI()

            logger.system("Core components initialized successfully")
        except Exception as e:
            raise RuntimeError(f"Failed to initialize components: {e}")

    def _setup_cuda(self):
        """Configure CUDA for optimal performance"""
        if not torch.cuda.is_available():
            raise RuntimeError("CUDA required!")
        torch.cuda.empty_cache()
        torch.set_float32_matmul_precision('high')
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.enabled = True
        self.cuda_stream = torch.cuda.Stream(priority=-1)
        logger.system(f"Using GPU: {torch.cuda.get_device_name(0)}")

    def log_startup(self):
        """Log startup information"""
        logger.system(f"""
🚀 MEV Bot Starting
====================
Position Size: {self.POSITION_SIZE} SOL
Trade Range: {self.MIN_VICTIM_SIZE}-{self.MAX_VICTIM_SIZE} SOL
Min Profit: {self.MIN_PROFIT} SOL
====================
Wallet: {self.wallet}
WSOL Account: {self.wsol_account}
====================
""")

    def calculate_profit(self, amount: float) -> float:
        """Calculate potential trade profit using CUDA"""
        with torch.cuda.stream(self.cuda_stream):
            amount_tensor = torch.tensor(amount, device='cuda')

            if amount >= 40:
                profit_rate = 0.025
            elif amount >= 20:
                profit_rate = 0.020
            elif amount >= 5:
                profit_rate = 0.015
            else:
                return 0.0

            entry_impact = 0.001 * amount_tensor
            exit_impact = entry_impact
            priority_fee = 0.002

            net_profit = (amount_tensor * profit_rate) - (entry_impact + exit_impact + priority_fee)
            return float(net_profit) if net_profit > self.MIN_PROFIT else 0.0

    def decode_trade_data(self, raw_data: bytes, tx_id: str) -> Optional[TradeOpportunity]:
        """Decode and validate trade data"""
        try:
            if tx_id in self.processed_txs:
                return None

            if len(raw_data) < 40:
                return None

            instruction = raw_data[0]
            amount = int.from_bytes(raw_data[1:9], "little") / 1e9

            if instruction != 0x03 or not (self.MIN_VICTIM_SIZE <= amount <= self.MAX_VICTIM_SIZE):
                return None

            token_a_bytes = raw_data[40:72] if len(raw_data) >= 72 else raw_data[-32:]
            token_b_bytes = raw_data[72:104] if len(raw_data) >= 104 else None

            token_a_mint = base58.b58encode(token_a_bytes).decode()
            token_b_mint = base58.b58encode(token_b_bytes).decode() if token_b_bytes else "So11111111111111111111111111111111111111112"

            profit = self.calculate_profit(amount)
            if profit <= 0:
                return None

            self.processed_txs.add(tx_id)

            return TradeOpportunity(
                victim_data=raw_data,
                amount=amount,
                token_a_mint=token_a_mint,
                token_b_mint=token_b_mint,
                net_profit=profit,
                instruction_type=instruction,
                transaction_id=tx_id
            )

        except Exception as e:
            logger.error(f"Trade data decode error: {e}")
            return None

    async def execute_trade(self, trade: TradeOpportunity) -> bool:
    """Execute trade with atomic entry"""
    if self.is_trading_paused:
        logger.warning("Trading is paused. Skipping trade execution.")
        return False

    async with self.trade_lock:
        try:
            if self.active_trades >= self.MAX_ACTIVE_TRADES:
                logger.warning("Max active trades reached. Skipping trade execution.")
                return False

            self.active_trades += 1
            position_lamports = int(self.POSITION_SIZE * self.LAMPORTS_PER_SOL)

            logger.trade(f"""
🎯 Trade Found:
Size: {trade.amount:.4f} SOL
Position: {self.POSITION_SIZE} SOL ({position_lamports} lamports)
Token A: {trade.token_a_mint}
WSOL: {self.wsol_account}
Est. Profit: {trade.net_profit:.6f} SOL
TX ID: {trade.transaction_id}
""")

            # Get market accounts in Raydium's required order
            market_accounts = config.get_market_accounts('SOL-WSOL')

            # Must be in this exact order for Raydium
            raydium_accounts = [
                config.get('raydium'),                     # Program ID
                market_accounts['amm_open_orders'],        # AMM Open Orders Account
                market_accounts['amm_target'],             # Pool Temp LP Token Account
                market_accounts['market'],                 # Market ID
                market_accounts['base_vault'],             # Base Vault
                market_accounts['quote_vault'],            # Quote Vault
                market_accounts['serum_market'],           # Serum Market ID
                market_accounts['serum_bids'],             # Serum Bids
                market_accounts['serum_asks'],             # Serum Asks
                market_accounts['event_queue'],            # Event Queue
                market_accounts['vault_signer'],           # Vault Signer
                self.wsol_account,                        # User Source Token Account (needs to be signer)
                config.get('token'),                      # Token Program
                self.wallet,                              # User Owner/Authority
            ]

            # Execute entry trade using victim's instruction data
            entry_params = TradeParams(
                accounts=raydium_accounts,
                amount_in=position_lamports,
                is_buy=True,
                reuse_blockhash=False,
                token_a_mint=trade.token_a_mint,
                token_b_mint=trade.token_b_mint,
                victim_data=trade.victim_data,
                sell_all=False
            )

            entry_result = await self.trading_core.execute_trade_py(entry_params)
            if entry_result:
                await asyncio.sleep(0.0001)

                # Execute exit trade with same account structure
                exit_params = TradeParams(
                    accounts=raydium_accounts,
                    amount_in=None,  # Will use sell_all
                    is_buy=False,
                    reuse_blockhash=True,
                    token_a_mint=trade.token_a_mint,
                    token_b_mint=trade.token_b_mint,
                    victim_data=None,
                    sell_all=True
                )

                exit_result = await self.trading_core.execute_trade_py(exit_params)
                if exit_result:
                    self.trades_executed += 1
                    self.successful_trades += 1
                    self.total_profit += trade.net_profit

                    logger.trade(f"""
💰 Trade Completed:
Entry TX: {entry_result}
Exit TX: {exit_result}
Original TX: {trade.transaction_id}
WSOL Account: {self.wsol_account}
Profit: {trade.net_profit:.6f} SOL
Total Profit: {self.total_profit:.6f} SOL
Success Rate: {(self.successful_trades/self.trades_executed*100):.1f}%
""")
                    return True

            return False

        except Exception as e:
            logger.error(f"Trade execution error: {e}")
            return False
        finally:
            self.active_trades -= 1

    async def monitor_balances(self):
        """Monitor SOL and WSOL balances using WebSocket with subscription tracking"""
        while self.active:
            try:
                async with websockets.connect(
                    self.ws_url,
                    extra_headers={'api-key': self.api_key}
                ) as ws:
                    accounts = {
                        1: ("SOL", self.wallet),
                        2: ("WSOL", self.wsol_account)
                    }
                    subs = {}  # Track subscription IDs

                    # Send initial subscriptions
                    for sub_id, (acc_type, address) in accounts.items():
                        await ws.send(json.dumps({
                            "jsonrpc": "2.0",
                            "id": sub_id,
                            "method": "accountSubscribe",
                            "params": [
                                address,
                                {"encoding": "base64", "commitment": "confirmed"}
                            ]
                        }))

                    # Process messages
                    async for msg in ws:
                        if not self.active:
                            break

                        data = json.loads(msg)

                        # Handle subscription responses
                        if "result" in data:
                            sub_id = data["id"]
                            if sub_id in accounts:
                                acc_type = accounts[sub_id][0]
                                subs[data["result"]] = acc_type
                            continue

                        # Handle balance updates
                        if "method" in data and data["method"] == "accountNotification":
                            sub_id = data["params"]["subscription"]
                            acc_type = subs.get(sub_id, "Unknown")

                            # Extract balance information
                            lamports = data["params"]["result"]["value"]["lamports"]
                            new_balance = lamports / self.LAMPORTS_PER_SOL
                            old_balance = self.balances.get(acc_type, 0)

                            # Update balances and log changes
                            if new_balance != old_balance:
                                self.balances[acc_type] = new_balance
                                change = new_balance - old_balance
                                logger.info(
                                    f"{acc_type} Balance: {new_balance:.4f} SOL "
                                    f"(Δ {change:+.4f} SOL)"
                                )

                                # Check balance thresholds
                                if (self.balances["SOL"] < self.min_sol_balance or
                                    self.balances["WSOL"] < self.min_wsol_balance):
                                    if not self.is_trading_paused:
                                        logger.warning("Trading paused - Low balances:")
                                        logger.warning(
                                            f"  SOL: {self.balances['SOL']:.4f}/"
                                            f"{self.min_sol_balance:.4f} | "
                                            f"WSOL: {self.balances['WSOL']:.4f}/"
                                            f"{self.min_wsol_balance:.4f}"
                                        )
                                        self.is_trading_paused = True
                                else:
                                    if self.is_trading_paused:
                                        logger.info("Trading resumed - Balances restored")
                                        self.is_trading_paused = False

            except websockets.ConnectionClosed:
                logger.warning("Balance monitor connection closed, reconnecting...")
                await asyncio.sleep(1)
            except Exception as e:
                logger.error(f"Balance monitoring error: {e}")
                await asyncio.sleep(1)

    async def process_mempool(self):
        """Process Raydium mempool data"""
        while self.active:
            try:
                async with websockets.connect(
                    self.ws_url,
                    extra_headers={
                        'api-key': self.api_key,
                        'User-Agent': 'solana-client/1.18.23'
                    },
                    ping_interval=5,
                    ping_timeout=10,
                    compression=None
                ) as ws:
                    logger.info("Mempool WebSocket Connected")

                    await ws.send(json.dumps({
                        "jsonrpc": "2.0",
                        "id": 1,
                        "method": "logsSubscribe",
                        "params": [
                            {"mentions": [self.program_id]},
                            {"commitment": "processed"}
                        ]
                    }))

                    await ws.recv()  # Subscription confirmation

                    while self.active:
                        try:
                            msg = await ws.recv()
                            data = json.loads(msg)

                            if "params" in data:
                                result = data["params"].get("result", {})
                                logs = result.get("value", {}).get("logs", [])
                                tx_id = result.get("value", {}).get("signature", "unknown")

                                for log in logs:
                                    if "ray_log:" in log:
                                        raw_data = base64.b64decode(log.split("ray_log:")[1].strip())

                                        trade = self.decode_trade_data(raw_data, tx_id)
                                        if trade:
                                            self.trades_found += 1
                                            await self.execute_trade(trade)

                        except Exception as e:
                            logger.info(f"Message processing: {e}")
                            continue

            except websockets.ConnectionClosed:
                logger.warning("Mempool WebSocket disconnected, reconnecting...")
                await asyncio.sleep(1)
            except Exception as e:
                logger.error(f"Mempool connection error: {e}")
                await asyncio.sleep(1)

    def show_performance(self):
        """Display trading performance metrics"""
        runtime = (datetime.now() - self.start_time).total_seconds()

        hours = int(runtime // 3600)
        minutes = int((runtime % 3600) // 60)
        seconds = int(runtime % 60)

        logger.system(f"""
🏁 Trading Performance Report
============================
Runtime: {hours:02d}:{minutes:02d}:{seconds:02d}

Trade Statistics:
----------------
Opportunities Found: {self.trades_found}
Trades Executed: {self.trades_executed}
Successful Trades: {self.successful_trades}
Success Rate: {(self.successful_trades/max(1,self.trades_executed)*100):.1f}%

Profit Metrics:
--------------
Total Profit: {self.total_profit:.6f} SOL
Profit per Hour: {(self.total_profit/(runtime/3600)):.6f} SOL/hr
Average Profit per Trade: {(self.total_profit/max(1,self.successful_trades)):.6f} SOL

Current Balances:
----------------
SOL: {self.balances['SOL']:.4f}
WSOL: {self.balances['WSOL']:.4f}
============================
""")

    async def run(self):
        """Start bot with all monitoring systems"""
        try:
            # Start monitoring systems
            balance_task = asyncio.create_task(self.monitor_balances())
            mempool_task = asyncio.create_task(self.process_mempool())

            # Start balance manager
            balance_manager_task = asyncio.create_task(self.solana_cli.start())

            # Show initial balance check
            await asyncio.sleep(2)  # Wait for first balance update
            logger.info(f"""
Initial Balance Check:
SOL: {self.balances['SOL']:.4f}
WSOL: {self.balances['WSOL']:.4f}
""")

            # Wait for tasks
            await asyncio.gather(balance_task, mempool_task, balance_manager_task)
        except asyncio.CancelledError:
            logger.system("Tasks cancelled")
        except Exception as e:
            logger.error(f"Task error: {e}")
        finally:
            self.show_performance()
            await self.cleanup()

    async def cleanup(self):
        """Cleanup resources"""
        self.active = False
        await self.solana_cli.cleanup()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        logger.system("Cleanup completed")


async def main():
    if os.name == 'nt':
        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

    try:
        bot = MEVBot()
        await bot.run()
    except KeyboardInterrupt:
        logger.system("Bot stopping gracefully...")
    except Exception as e:
        logger.error(f"Fatal error: {e}")
    finally:
        # Cleanup
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        logger.system("Bot stopped")


if __name__ == "__main__":
    asyncio.run(main()) write the full script please word for word without the indentation issues
ASSISTANT
Certainly! Here is the full script with all indentation issues resolved:

```python
import asyncio
import json
import base64
import base58
from datetime import datetime
import websockets
import torch
import os
from typing import Optional
from dataclasses import dataclass
from solana_logger import logger
from config_loader import config
from solana_rust_bot import TradingCore, TradeParams
from solana_cli import SolanaCLI


@dataclass
class TradeOpportunity:
    victim_data: bytes
    amount: float
    token_a_mint: str
    token_b_mint: str
    net_profit: float
    instruction_type: int
    transaction_id: str


class MEVBot:
    def __init__(self):
        # Load configuration
        self.load_config()

        # Initialize core components
        self.init_components()

        # Trading parameters from config
        trade_config = config.get_trade_config()
        self.POSITION_SIZE = trade_config['front_run_amount']
        self.MIN_VICTIM_SIZE = trade_config['min_victim_size']
        self.MAX_VICTIM_SIZE = trade_config['max_victim_size']
        self.MIN_PROFIT = trade_config['min_profit']
        self.LAMPORTS_PER_SOL = 1_000_000_000

        # Track processed transactions
        self.processed_txs = set()

        # Balance monitoring
        balance_config = config.get_balance_config()
        self.min_sol_balance = balance_config['min_sol_balance']
        self.min_wsol_balance = balance_config['min_wsol_balance']
        self.balances = {"SOL": 0.0, "WSOL": 0.0}
        self.is_trading_paused = False

        # Performance tracking
        self.active = True
        self.trades_found = 0
        self.trades_executed = 0
        self.successful_trades = 0
        self.total_profit = 0.0
        self.start_time = datetime.now()

        # Concurrency control
        self.trade_lock = asyncio.Lock()
        self.MAX_ACTIVE_TRADES = 1
        self.active_trades = 0

        # Setup CUDA
        self._setup_cuda()

        self.log_startup()

    def load_config(self):
        """Load configuration and network settings"""
        network_config = config.get_network_config()
        self.ws_url = network_config['ws_url']
        self.api_key = network_config['api_key']
        self.program_id = config.get('raydium')

        # Account configuration
        self.wallet = config.get('wallet')
        self.wsol_account = config.get('wsol_token_account')

        # Market configuration
        market_accounts = config.get_market_accounts('SOL-WSOL')
        self.token_a_mint = market_accounts['token_a_mint']
        self.token_b_mint = market_accounts['token_b_mint']

    def init_components(self):
        """Initialize core components"""
        try:
            # Initialize trading core with RPC URL from config
            self.trading_core = TradingCore(config.get_network_config()['rpc_url'])

            # Initialize Solana CLI helper
            self.solana_cli = SolanaCLI()

            logger.system("Core components initialized successfully")
        except Exception as e:
            raise RuntimeError(f"Failed to initialize components: {e}")

    def _setup_cuda(self):
        """Configure CUDA for optimal performance"""
        if not torch.cuda.is_available():
            raise RuntimeError("CUDA required!")
        torch.cuda.empty_cache()
        torch.set_float32_matmul_precision('high')
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.enabled = True
        self.cuda_stream = torch.cuda.Stream(priority=-1)
        logger.system(f"Using GPU: {torch.cuda.get_device_name(0)}")

    def log_startup(self):
        """Log startup information"""
        logger.system(f"""
🚀 MEV Bot Starting
====================
Position Size: {self.POSITION_SIZE} SOL
Trade Range: {self.MIN_VICTIM_SIZE}-{self.MAX_VICTIM_SIZE} SOL
Min Profit: {self.MIN_PROFIT} SOL
====================
Wallet: {self.wallet}
WSOL Account: {self.wsol_account}
====================
""")

    def calculate_profit(self, amount: float) -> float:
        """Calculate potential trade profit using CUDA"""
        with torch.cuda.stream(self.cuda_stream):
            amount_tensor = torch.tensor(amount, device='cuda')

            if amount >= 40:
                profit_rate = 0.025
            elif amount >= 20:
                profit_rate = 0.020
            elif amount >= 5:
                profit_rate = 0.015
            else:
                return 0.0

            entry_impact = 0.001 * amount_tensor
            exit_impact = entry_impact
            priority_fee = 0.002

            net_profit = (amount_tensor * profit_rate) - (entry_impact + exit_impact + priority_fee)
            return float(net_profit) if net_profit > self.MIN_PROFIT else 0.0

    def decode_trade_data(self, raw_data: bytes, tx_id: str) -> Optional[TradeOpportunity]:
        """Decode and validate trade data"""
        try:
            if tx_id in self.processed_txs:
                return None

            if len(raw_data) < 40:
                return None

            instruction = raw_data[0]
            amount = int.from_bytes(raw_data[1:9], "little") / 1e9

            if instruction != 0x03 or not (self.MIN_VICTIM_SIZE <= amount <= self.MAX_VICTIM_SIZE):
                return None

            token_a_bytes = raw_data[40:72] if len(raw_data) >= 72 else raw_data[-32:]
            token_b_bytes = raw_data[72:104] if len(raw_data) >= 104 else None

            token_a_mint = base58.b58encode(token_a_bytes).decode()
            token_b_mint = base58.b58encode(token_b_bytes).decode() if token_b_bytes else "So11111111111111111111111111111111111111112"

            profit = self.calculate_profit(amount)
            if profit <= 0:
                return None

            self.processed_txs.add(tx_id)

            return TradeOpportunity(
                victim_data=raw_data,
                amount=amount,
                token_a_mint=token_a_mint,
                token_b_mint=token_b_mint,
                net_profit=profit,
                instruction_type=instruction,
                transaction_id=tx_id
            )

        except Exception as e:
            logger.error(f"Trade data decode error: {e}")
            return None

    async def execute_trade(self, trade: TradeOpportunity) -> bool:
        """Execute trade with atomic entry"""
        if self.is_trading_paused:
            logger.warning("Trading is paused. Skipping trade execution.")
            return False

        async with self.trade_lock:
            try:
                if self.active_trades >= self.MAX_ACTIVE_TRADES:
                    logger.warning("Max active trades reached. Skipping trade execution.")
                    return False

                self.active_trades += 1
                position_lamports = int(self.POSITION_SIZE * self.LAMPORTS_PER_SOL)

                logger.trade(f"""
🎯 Trade Found:
Size: {trade.amount:.4f} SOL
Position: {self.POSITION_SIZE} SOL ({position_lamports} lamports)
Token A: {trade.token_a_mint}
WSOL: {self.wsol_account}
Est. Profit: {trade.net_profit:.6f} SOL
TX ID: {trade.transaction_id}
""")

                # Get market accounts in Raydium's required order
                market_accounts = config.get_market_accounts('SOL-WSOL')

                # Must be in this exact order for Raydium
                raydium_accounts = [
                    config.get('raydium'),                     # Program ID
                    market_accounts['amm_open_orders'],        # AMM Open Orders Account
                    market_accounts['amm_target'],             # Pool Temp LP Token Account
                    market_accounts['market'],                 # Market ID
                    market_accounts['base_vault'],             # Base Vault
                    market_accounts['quote_vault'],            # Quote Vault
                    market_accounts['serum_market'],           # Serum Market ID
                    market_accounts['serum_bids'],             # Serum Bids
                    market_accounts['serum_asks'],             # Serum Asks
                    market_accounts['event_queue'],            # Event Queue
                    market_accounts['vault_signer'],           # Vault Signer
                    self.wsol_account,                        # User Source Token Account (needs to be signer)
                    config.get('token'),                      # Token Program
                    self.wallet,                              # User Owner/Authority
                ]

                # Execute entry trade using victim's instruction data
                entry_params = TradeParams(
                    accounts=raydium_accounts,
                    amount_in=position_lamports,
                    is_buy=True,
                    reuse_blockhash=False,
                    token_a_mint=trade.token_a_mint,
                    token_b_mint=trade.token_b_mint,
                    victim_data=trade.victim_data,
                    sell_all=False
                )

                entry_result = await self.trading_core.execute_trade_py(entry_params)
                if entry_result:
                    await asyncio.sleep(0.0001)

                    # Execute exit trade with same account structure
                    exit_params = TradeParams(
                        accounts=raydium_accounts,
                        amount_in=None,  # Will use sell_all
                        is_buy=False,
                        reuse_blockhash=True,
                        token_a_mint=trade.token_a_mint,
                        token_b_mint=trade.token_b_mint,
                        victim_data=None,
                        sell_all=True
                    )

                    exit_result = await self.trading_core.execute_trade_py(exit_params)
                    if exit_result:
                        self.trades_executed += 1
                        self.successful_trades += 1
                        self.total_profit += trade.net_profit

                        logger.trade(f"""
💰 Trade Completed:
Entry TX: {entry_result}
Exit TX: {exit_result}
Original TX: {trade.transaction_id}
WSOL Account: {self.wsol_account}
Profit: {trade.net_profit:.6f} SOL
Total Profit: {self.total_profit:.6f} SOL
Success Rate: {(self.successful_trades/self.trades_executed*100):.1f}%
""")
                        return True

                return False

            except Exception as e:
                logger.error(f"Trade execution error: {e}")
                return False
            finally:
                self.active_trades -= 1

    async def monitor_balances(self):
        """Monitor SOL and WSOL balances using WebSocket with subscription tracking"""
        while self.active:
            try:
                async with websockets.connect(
                    self.ws_url,
                    extra_headers={'api-key': self.api_key}
                ) as ws:
                    accounts = {
                        1: ("SOL", self.wallet),
                        2: ("WSOL", self.wsol_account)
                    }
                    subs = {}  # Track subscription IDs

                    # Send initial subscriptions
                    for sub_id, (acc_type, address) in accounts.items():
                        await ws.send(json.dumps({
                            "jsonrpc": "2.0",
                            "id": sub_id,
                            "method": "accountSubscribe",
                            "params": [
                                address,
                                {"encoding": "base64", "commitment": "confirmed"}
                            ]
                        }))

                    # Process messages
                    async for msg in ws:
                        if not self.active:
                            break

                        data = json.loads(msg)

                        # Handle subscription responses
                        if "result" in data:
                            sub_id = data["id"]
                            if sub_id in accounts:
                                acc_type = accounts[sub_id][0]
                                subs[data["result"]] = acc_type
                            continue

                        # Handle balance updates
                        if "method" in data and data["method"] == "accountNotification":
                            sub_id = data["params"]["subscription"]
                            acc_type = subs.get(sub_id, "Unknown")

                            # Extract balance information
                            lamports = data["params"]["result"]["value"]["lamports"]
                            new_balance = lamports / self.LAMPORTS_PER_SOL
                            old_balance = self.balances.get(acc_type, 0)

                            # Update balances and log changes
                            if new_balance != old_balance:
                                self.balances[acc_type] = new_balance
                                change = new_balance - old_balance
                                logger.info(
                                    f"{acc_type} Balance: {new_balance:.4f} SOL "
                                    f"(Δ {change:+.4f} SOL)"
                                )

                                # Check balance thresholds
                                if (self.balances["SOL"] < self.min_sol_balance or
                                    self.balances["WSOL"] < self.min_wsol_balance):
                                    if not self.is_trading_paused:
                                        logger.warning("Trading paused - Low balances:")
                                        logger.warning(
                                            f"  SOL: {self.balances['SOL']:.4f}/"
                                            f"{self.min_sol_balance:.4f} | "
                                            f"WSOL: {self.balances['WSOL']:.4f}/"
                                            f"{self.min_wsol_balance:.4f}"
                                        )
                                        self.is_trading_paused = True
                                else:
                                    if self.is_trading_paused:
                                        logger.info("Trading resumed - Balances restored")
                                        self.is_trading_paused = False

            except websockets.ConnectionClosed:
                logger.warning("Balance monitor connection closed, reconnecting...")
                await asyncio.sleep(1)
            except Exception as e:
                logger.error(f"Balance monitoring error: {e}")
                await asyncio.sleep(1)

    async def process_mempool(self):
        """Process Raydium mempool data"""
        while self.active:
            try:
                async with websockets.connect(
                    self.ws_url,
                    extra_headers={
                        'api-key': self.api_key,
                        'User-Agent': 'solana-client/1.18.23'
                    },
                    ping_interval=5,
                    ping_timeout=10,
                    compression=None
                ) as ws:
                    logger.info("Mempool WebSocket Connected")

                    await ws.send(json.dumps({
                        "jsonrpc": "2.0",
                        "id": 1,
                        "method": "logsSubscribe",
                        "params": [
                            {"mentions": [self.program_id]},
                            {"commitment": "processed"}
                        ]
                    }))

                    await ws.recv()  # Subscription confirmation

                    while self.active:
                        try:
                            msg = await ws.recv()
                            data = json.loads(msg)

                            if "params" in data:
                                result = data["params"].get("result", {})
                                logs = result.get("value", {}).get("logs", [])
                                tx_id = result.get("value", {}).get("signature", "unknown")

                                for log in logs:
                                    if "ray_log:" in log:
                                        raw_data = base64.b64decode(log.split("ray_log:")[1].strip())

                                        trade = self.decode_trade_data(raw_data, tx_id)
                                        if trade:
                                            self.trades_found += 1
                                            await self.execute_trade(trade)

                        except Exception as e:
                            logger.info(f"Message processing: {e}")
                            continue

            except websockets.ConnectionClosed:
                logger.warning("Mempool WebSocket disconnected, reconnecting...")
                await asyncio.sleep(1)
            except Exception as e:
                logger.error(f"Mempool connection error: {e}")
                await asyncio.sleep(1)

    def show_performance(self):
        """Display trading performance metrics"""
        runtime = (datetime.now() - self.start_time).total_seconds()

        hours = int(runtime // 3600)
        minutes = int((runtime % 3600) // 60)
        seconds = int(runtime % 60)

        logger.system(f"""
🏁 Trading Performance Report
============================
Runtime: {hours:02d}:{minutes:02d}:{seconds:02d}

Trade Statistics:
----------------
Opportunities Found: {self.trades_found}
Trades Executed: {self.trades_executed}
Successful Trades: {self.successful_trades}
Success Rate: {(self.successful_trades/max(1,self.trades_executed)*100):.1f}%

Profit Metrics:
--------------
Total Profit: {self.total_profit:.6f} SOL
Profit per Hour: {(self.total_profit/(runtime/3600)):.6f} SOL/hr
Average Profit per Trade: {(self.total_profit/max(1,self.successful_trades)):.6f} SOL

Current Balances:
----------------
SOL: {self.balances['SOL']:.4f}
WSOL: {self.balances['WSOL']:.4f}
============================
""")

    async def run(self):
        """Start bot with all monitoring systems"""
        try:
            # Start monitoring systems
            balance_task = asyncio.create_task(self.monitor_balances())
            mempool_task = asyncio.create_task(self.process_mempool())

            # Start balance manager
            balance_manager_task = asyncio.create_task(self.solana_cli.start())

            # Show initial balance check
            await asyncio.sleep(2)  # Wait for first balance update
            logger.info(f"""
Initial Balance Check:
SOL: {self.balances['SOL']:.4f}
WSOL: {self.balances['WSOL']:.4f}
""")

            # Wait for tasks
            await asyncio.gather(balance_task, mempool_task, balance_manager_task)
        except asyncio.CancelledError:
            logger.system("Tasks cancelled")
        except Exception as e:
            logger.error(f"Task error: {e}")
        finally:
            self.show_performance()
            await self.cleanup()

    async def cleanup(self):
        """Cleanup resources"""
        self.active = False
        await self.solana_cli.cleanup()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        logger.system("Cleanup completed")


async def main():
    if os.name == 'nt':
        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

    try:
        bot = MEVBot()
        await bot.run()
    except KeyboardInterrupt:
        logger.system("Bot stopping gracefully...")
    except Exception as e:
        logger.error(f"Fatal error: {e}")
    finally:
        # Cleanup
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        logger.system("Bot stopped")


if __name__ == "__main__":
    asyncio.run(main())
```

This code maintains proper indentation and formatting, allowing for clear readability and functionality. If you have any further questions or need additional modifications, feel free to ask!