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Commit faf42a4

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Merge pull request #2777 from andoriyaprashant/branch34
Algorithmic Trading Simulation Script Added
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# Algorithmic Trading Simulation
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This Python script simulates an algorithmic trading platform where users can test trading strategies using historical price data. It's designed to provide a basic understanding of how automated trading works. Please note that this is a simplified educational example and not suitable for actual trading.
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## Features
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- Generates simulated historical price data for testing.
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- Implements a simple moving average strategy for trading signals.
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- Includes risk management based on a specified percentage of portfolio value.
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- Visualizes price data, moving averages, and portfolio value over time.
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## Prerequisites
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- Python
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- Required libraries: pandas, numpy, matplotlib
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## Usage
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1. Ensure you have Python installed.
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2. Install the required libraries: `pip install pandas numpy matplotlib`
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3. Modify the script's parameters, such as strategy settings and risk management parameters.
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4. Run the script: `python algorithmic_trading_simulation.py`
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## Disclaimer
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This script is meant for educational purposes only and is not intended for actual trading. It lacks important features and considerations necessary for a real-world trading system, such as accurate data, transaction costs, slippage, risk management, and more. Always exercise caution and consult with professionals before engaging in algorithmic trading.
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## Contributing
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Contributions to the project are welcome! If you have any improvements, bug fixes, or new features to add, feel free to submit a pull request.
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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# Simulated historical price data (more data points)
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data = {
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'Date': pd.date_range(start='2010年01月01日', periods=1000),
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'Open': np.random.uniform(100, 200, 1000),
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'High': np.random.uniform(200, 250, 1000),
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'Low': np.random.uniform(80, 150, 1000),
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'Close': np.random.uniform(120, 180, 1000),
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'Volume': np.random.randint(10000, 50000, 1000)
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}
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df = pd.DataFrame(data)
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df.set_index('Date', inplace=True)
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# Example trading strategy
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def simple_moving_average_strategy(data, short_window, long_window):
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signals = pd.DataFrame(index=data.index)
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signals['Signal'] = 0.0
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signals['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1, center=False).mean()
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signals['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1, center=False).mean()
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signals['Signal'][short_window:] = np.where(signals['Short_MA'][short_window:] > signals['Long_MA'][short_window:], 1.0, 0.0)
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signals['Positions'] = signals['Signal'].diff()
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return signals
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# Define strategy parameters
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short_window = 10
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long_window = 50
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# Apply strategy
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signals = simple_moving_average_strategy(df, short_window, long_window)
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# Risk management parameters
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risk_per_trade = 0.02 # 2% risk per trade
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initial_portfolio_value = 100000
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# Simulate trading
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position = 0
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portfolio_value = []
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for index, row in signals.iterrows():
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if row['Positions'] == 1:
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max_position_size = (risk_per_trade * initial_portfolio_value) / (row['Close'] - row['Low'])
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position = min(max_position_size, initial_portfolio_value / row['Close'])
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initial_portfolio_value -= position * row['Close']
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elif row['Positions'] == -1:
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initial_portfolio_value += position * row['Close']
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position = 0
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portfolio_value.append(initial_portfolio_value + position * row['Close'])
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signals['PortfolioValue'] = portfolio_value
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# Visualization improvements
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plt.figure(figsize=(12, 8))
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plt.subplot(2, 1, 1)
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plt.plot(signals.index, signals['Close'], label='Close Price', alpha=0.5)
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plt.plot(signals.index, signals['Short_MA'], label=f'Short {short_window} MA')
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plt.plot(signals.index, signals['Long_MA'], label=f'Long {long_window} MA')
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plt.legend()
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plt.title('Price Data and Moving Averages')
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plt.subplot(2, 1, 2)
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plt.plot(signals.index, signals['PortfolioValue'], label='Portfolio Value', linestyle='--', color='green')
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plt.legend()
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plt.title('Portfolio Value')
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plt.tight_layout()
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plt.show()
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pandas
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numpy
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matplotlib

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