Algorithmic Trading Simulation Script Added

Author: andoriyaprashant

Algorithmic Trading Simulation/README.md

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+# Algorithmic Trading Simulation
+
+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.
+
+## Features
+
+- Generates simulated historical price data for testing.
+- Implements a simple moving average strategy for trading signals.
+- Includes risk management based on a specified percentage of portfolio value.
+- Visualizes price data, moving averages, and portfolio value over time.
+
+## Prerequisites
+
+- Python 
+- Required libraries: pandas, numpy, matplotlib
+
+## Usage
+
+1. Ensure you have Python installed.
+2. Install the required libraries: `pip install pandas numpy matplotlib`
+3. Modify the script's parameters, such as strategy settings and risk management parameters.
+4. Run the script: `python algorithmic_trading_simulation.py`
+
+## Disclaimer
+
+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.
+
+## Contributing
+
+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.

Algorithmic Trading Simulation/algorithmic_trading_simulation.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Simulated historical price data (more data points)
+data = {
+    'Date': pd.date_range(start='2010-01-01', periods=1000),
+    'Open': np.random.uniform(100, 200, 1000),
+    'High': np.random.uniform(200, 250, 1000),
+    'Low': np.random.uniform(80, 150, 1000),
+    'Close': np.random.uniform(120, 180, 1000),
+    'Volume': np.random.randint(10000, 50000, 1000)
+}
+
+df = pd.DataFrame(data)
+df.set_index('Date', inplace=True)
+
+# Example trading strategy
+def simple_moving_average_strategy(data, short_window, long_window):
+    signals = pd.DataFrame(index=data.index)
+    signals['Signal'] = 0.0
+    
+    signals['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1, center=False).mean()
+    signals['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1, center=False).mean()
+    
+    signals['Signal'][short_window:] = np.where(signals['Short_MA'][short_window:] > signals['Long_MA'][short_window:], 1.0, 0.0)
+    
+    signals['Positions'] = signals['Signal'].diff()
+    
+    return signals
+
+# Define strategy parameters
+short_window = 10
+long_window = 50
+
+# Apply strategy
+signals = simple_moving_average_strategy(df, short_window, long_window)
+
+# Risk management parameters
+risk_per_trade = 0.02  # 2% risk per trade
+initial_portfolio_value = 100000
+
+# Simulate trading
+position = 0
+portfolio_value = []
+for index, row in signals.iterrows():
+    if row['Positions'] == 1:
+        max_position_size = (risk_per_trade * initial_portfolio_value) / (row['Close'] - row['Low'])
+        position = min(max_position_size, initial_portfolio_value / row['Close'])
+        initial_portfolio_value -= position * row['Close']
+    elif row['Positions'] == -1:
+        initial_portfolio_value += position * row['Close']
+        position = 0
+    portfolio_value.append(initial_portfolio_value + position * row['Close'])
+
+signals['PortfolioValue'] = portfolio_value
+
+# Visualization improvements
+plt.figure(figsize=(12, 8))
+
+plt.subplot(2, 1, 1)
+plt.plot(signals.index, signals['Close'], label='Close Price', alpha=0.5)
+plt.plot(signals.index, signals['Short_MA'], label=f'Short {short_window} MA')
+plt.plot(signals.index, signals['Long_MA'], label=f'Long {long_window} MA')
+plt.legend()
+plt.title('Price Data and Moving Averages')
+
+plt.subplot(2, 1, 2)
+plt.plot(signals.index, signals['PortfolioValue'], label='Portfolio Value', linestyle='--', color='green')
+plt.legend()
+plt.title('Portfolio Value')
+
+plt.tight_layout()
+plt.show()

Algorithmic Trading Simulation/requirements.txt

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+pandas
+numpy
+matplotlib