📈 Trader — Simple Backtesting Engine for Long/Short Trading Strategies

Trader is a lightweight, pure-Python backtesting engine designed for testing long and short trading strategies with realistic commission modeling (including MOEX-style fixed + percentage fees). It tracks your account balance, position size, unrealized PnL, and trade history — all in a clean, testable class-based interface.

Perfect for prototyping algorithmic strategies, educational purposes, or integrating into larger quant pipelines.

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✨ Features

• Long & Short support: Open, close, and manage both long and short positions.
• Realistic commissions: Configurable percentage + fixed fee (e.g., 0.1% + 0 RUB).
• Account tracking: Automatically logs My_money, Stock_Cash, and total Account_money over time.
• Profit calculation: Built-in methods for current PnL and total cash result.
• Test-driven: Comes with a full unittest suite for reliability.
• No external dependencies beyond pandas and numpy.

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🚀 Quick Start

1. Install dependencies

pip install pandas numpy

2. Basic usage example

from Trader import Trader

# Initialize trader with 100,000 RUB, 0.1% commission + 0 fixed fee
trader = Trader(money=100_000, comis=[0.1, 0], printBool=True)

# Buy 5,000 units at price 10.0
trader.buy(orderVolume=5000, price=10.0)

# Sell 5,000 units at price 20.0 (close long)
trader.sell(orderVolume=5000, price=20.0)

# Get total profit
print("Total PnL:", trader.getCashResultFast(20.0))

3. Log account state over time

trader.quant_money(price=15.0, date_now="2023-06-01")
trader.quant_money(price=18.0, date_now="2023-06-02")

# View full account history
print(trader.table)

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🧪 Testing

The project includes a comprehensive test suite:

python -m pytest TestTrader.py
# or
python TestTrader.py

Tests cover:

• Commission calculation
• Long/short entry & exit
• Account value consistency
• Profit tracking accuracy

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📂 Project Structure

.
├── Trader.py          # Core Trader class (rename to Trader.py in real use)
├── TestTrader.py      # Unit tests (rename to TestTrader.py)
└── CNY000000TOD_22-6-2022_18-7-2023_Test.txt  # Sample market data (CSV format expected)

💡 Note: The .txt extensions are likely placeholders. In a real repo, rename to .py and .csv accordingly.

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💡 How It Works

• my_money: Cash not invested.
• posVolume: Current position (positive = long, negative = short).
• Account_money: Total equity = cash + value of open positions (adjusted for short logic).
• moneyOnStartDeal: Snapshot of account value at position entry — used to compute trade-level PnL.
• Commissions: Calculated as
  price * volume / 100 * comis[0] + comis[1]
  (e.g., 0.1% + 0 RUB per trade).

Short positions are modeled using a "borrowed asset" approach: selling short reduces cash (you must post collateral), and profits come from buying back cheaper.

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🛠️ Customization

• Adjust commission structure via the comis=[percentage, fixed] parameter.
• Disable verbose output with printBool=False.
• Use getCashResultFast() for real-time PnL without logging.
• Extend the class to integrate with live brokers or strategy logic.

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📜 License

MIT License — feel free to use, modify, and distribute.

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🙌 Author

Created by gekator — for traders, by a trader.

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🔍 Tip: Pair this engine with historical OHLC data (e.g., from MOEX or Binance) to backtest your strategies end-to-end!