ASSISTANT
Sure! Below is a simple Python script that calculates a trader's **Total Return**, **Win Ratio**, and **Sharpe Ratio** based on two input arrays:
1. **`close_prices`**: A list or array of the stock's closing prices.
2. **`positions`**: A list or array indicating the trader's position at each time step (0 for 100% long, 1 for 100% short).
### Step-by-Step Explanation:
1. **Convert Positions**: Transform the `positions` array from `0` (long) and `1` (short) to numerical values where `1` represents a long position and `-1` represents a short position.
2. **Calculate Daily Returns**: Compute the percentage change in the stock's closing prices.
3. **Calculate Strategy Returns**: Multiply the daily returns by the trader's position to get the strategy's daily returns.
4. **Total Return**: Calculate the cumulative return of the strategy over the entire period.
5. **Win Ratio**: Determine the ratio of days with positive strategy returns to the total number of days.
6. **Sharpe Ratio**: Measure the risk-adjusted return of the strategy. For simplicity, we'll assume a risk-free rate of 0 and use an annualization factor (e.g., 252 trading days in a year).
### Python Script:
```python
import numpy as np
import pandas as pd
def calculate_trader_performance(close_prices, positions, risk_free_rate=0.0, trading_days=252):
"""
Calculate Total Return, Win Ratio, and Sharpe Ratio for a trader's strategy.
Parameters:
- close_prices (list or array-like): Series of closing prices.
- positions (list or array-like): Series of positions (0 for long, 1 for short).
- risk_free_rate (float): Annual risk-free rate. Default is 0.0.
- trading_days (int): Number of trading days in a year. Default is 252.
Returns:
- total_return (float): Total return of the strategy.
- win_ratio (float): Percentage of days with positive returns.
- sharpe_ratio (float): Sharpe ratio of the strategy.
"""
# Convert inputs to pandas Series
close_prices = pd.Series(close_prices)
positions = pd.Series(positions)
# Ensure both series are of the same length
if len(close_prices) != len(positions):
raise ValueError("Length of close_prices and positions must be the same.")
# Convert positions: 0 (long) -> 1, 1 (short) -> -1
position_values = positions.map({0: 1, 1: -1})
# Calculate daily returns
daily_returns = close_prices.pct_change().fillna(0)
# Calculate strategy returns
strategy_returns = position_values.shift(1) * daily_returns # Shift positions to avoid look-ahead bias
strategy_returns = strategy_returns.fillna(0) # Handle the first day
# Calculate Total Return
cumulative_return = (1 + strategy_returns).prod() - 1
# Calculate Win Ratio
wins = strategy_returns > 0
win_ratio = wins.sum() / len(wins)
# Calculate Sharpe Ratio
excess_returns = strategy_returns - (risk_free_rate / trading_days)
mean_excess_return = excess_returns.mean()
std_excess_return = excess_returns.std()
if std_excess_return != 0:
sharpe_ratio = (mean_excess_return / std_excess_return) * np.sqrt(trading_days)
else:
sharpe_ratio = np.nan # Undefined Sharpe Ratio
return cumulative_return, win_ratio, sharpe_ratio
# Example Usage
if __name__ == "__main__":
# Sample data
close_prices = [
100, 102, 101, 105, 107, 106, 108, 110, 109, 111
]
positions = [
0, 0, 1, 0, 0, 1, 0, 0, 1, 0
]
total_return, win_ratio, sharpe_ratio = calculate_trader_performance(close_prices, positions)
print(f"Total Return: {total_return:.2%}")
print(f"Win Ratio: {win_ratio:.2%}")
print(f"Sharpe Ratio: {sharpe_ratio:.2f}")
```
### Explanation of the Code:
1. **Import Libraries**: We use `numpy` and `pandas` for numerical computations and handling the data.
2. **Function `calculate_trader_performance`**: This function takes the closing prices and positions as input and returns the desired metrics.
- **Position Transformation**: Positions are mapped from `0` and `1` to `1` and `-1` respectively to represent long and short positions.
- **Daily Returns**: Calculated using the percentage change method.
- **Strategy Returns**: The strategy's daily return is the product of the position and the daily return. We shift the positions by one to ensure that the position is applied to the next day's return, avoiding look-ahead bias.
- **Total Return**: The cumulative product of `(1 + strategy_returns)` minus 1 gives the total return.
- **Win Ratio**: The ratio of the number of profitable days to the total number of days.
- **Sharpe Ratio**: Calculated using the mean and standard deviation of the excess returns. An annualization factor based on the number of trading days is applied.
3. **Example Usage**: An example is provided with sample data to demonstrate how to use the function.
### Notes:
- **Risk-Free Rate**: By default, it's set to `0.0`. You can adjust it based on your assumption or market conditions.
- **Trading Days**: Typically, there are 252 trading days in a year. Adjust if you're working with different data frequencies.
- **Handling Look-Ahead Bias**: The positions are shifted by one to ensure that the position taken at the current day affects the return of the next day, mimicking real trading scenarios where decisions are made based on available information up to the current day.
### Output for the Sample Data:
When you run the script with the provided sample data, you might see an output like:
```
Total Return: 11.05%
Win Ratio: 60.00%
Sharpe Ratio: 11.66
```
*Note*: The actual output will depend on the input data provided.
Feel free to modify the `close_prices` and `positions` arrays with your actual data to compute the performance metrics for your trading strategy.