simple-trade 0.3.0

Compute technical indicators and build trade strategies in a simple way

simple-trade

A Python library that allows you to compute technical indicators and build trade strategies in a simple way.

Features

• Data Fetching: Easily download historical stock data using yfinance.
• Technical Indicators: Compute a variety of technical indicators:
  • Trend (e.g., Moving Averages, MACD, ADX)
  • Momentum (e.g., RSI, Stochastics)
  • Volatility (e.g., Bollinger Bands, ATR)
  • Volume (e.g., On-Balance Volume)
• Trading Strategies: Implement custom trading strategies or select from premade trading strategies.
• Backtesting: Evaluate the performance of your trading strategies on historical data.
• Optimization: Optimize strategy parameters using techniques like grid search.
• Plotting: Visualize data, indicators, and backtest results using matplotlib.
• Combining: Combine different strategies to create a more complex strategies.

Installation

1. Clone the repository:

   git clone <repository_url> # Replace with your repo URL
   cd simple-trade
   

2. Create and activate a virtual environment (recommended):

   python -m venv myenv
   # On Windows
   myenv\Scripts\activate
   # On macOS/Linux
   source myenv/bin/activate
   

3. Install the package and dependencies:

   pip install .
   

   Alternatively, installed with PyPI:

   pip install simple-trade
   

Dependencies

• Python >= 3.10
• yfinance
• pandas
• numpy
• joblib
• matplotlib

These will be installed automatically when you install simple-trade using pip.

Basic Usage

Calculate Indicators

Here's a quick example of how to download data and compute a technical indicator:

# Load Packages and Functions
from simple_trade import compute_indicator, download_data
from simple_trade import list_indicators

# Step 1: Download data
symbol = 'TSLA'
start = '2024-01-01'
end = '2025-01-01'
interval = '1d'
print(f"\nDownloading data for {symbol}...")
data = download_data(symbol, start, end, interval=interval)

# Step 2: Calculate indicator
parameters = dict()
columns = dict()
parameters["window"] = 14
data, columns, fig = compute_indicator(
    data=data,
    indicator='adx',
    parameters=parameters
)

# Step 3: Display the plot
fig.show()

Plot of Results

To see a list of all indicators, use list_indicators() function.

Backtesting Strategies

Use the run_premade_trade function to select from premade strategies or create your custom strategies using run_cross_trade/run_band_trade functions.

# Example for backtesting a premade strategy

# Load Packages and Functions
from simple_trade import download_data
from simple_trade import run_premade_trade
from simple_trade import list_premade_strategies
from simple_trade import print_results

# Step 1: Download data
symbol = 'AAPL'
start_date = '2020-01-01'
end_date = '2022-12-31'
interval = '1d'
data = download_data(symbol, start_date, end_date, interval=interval)

# Step 2: Set Global Parameters
global_parameters = {
    'initial_cash': 10000,
    'commission_long': 0.001,
    'commission_short': 0.001,
    'short_borrow_fee_inc_rate': 0.0,
    'long_borrow_fee_inc_rate': 0.0,
    'trading_type': 'long',
    'day1_position': 'none',
    'risk_free_rate': 0.0,
}

# Step 3: Set Strategy Parameters
strategy_name = 'sma'
specific_parameters = {
    'short_window': 25,
    'long_window': 75,
    'fig_control': 1,
}

# Step 4: Run Backtest
parameters = {**global_parameters, **specific_parameters}
results, portfolio, fig = run_premade_trade(data, strategy_name, parameters)
print_results(results)

Plot of Results

============================================================

🗓️ BACKTEST PERIOD: • Period: 2020-04-20 to 2022-12-30 • Duration: 984 days • Trading Periods: 682

📊 BASIC METRICS: • Initial Investment: $10,000.00 • Final Portfolio Value: $13,199.32 • Total Return: 31.99% • Annualized Return: 10.80% • Number of Trades: 16 • Total Commissions: $237.12

📈 BENCHMARK COMPARISON: • Benchmark Return: 87.48% • Benchmark Final Value: $18,748.45 • Strategy vs Benchmark: -55.49%

📉 RISK METRICS: • Sharpe Ratio: 0.530 • Sortino Ratio: 0.500 • Maximum Drawdown: -32.50% • Average Drawdown: -14.25% • Max Drawdown Duration: 360 days • Avg Drawdown Duration: 43.43 days • Annualized Volatility: 25.89%

============================================================

To see a list of all premade strategies, use list_premade_strategies() function.

Optimizing Strategies

Use the premade_optimizer function to find the best parameters for your premade strategies or optimize your custom strategies using custom_optimizer function.

# Example for optimizing a premade strategy

# Load Packages and Functions
from simple_trade import download_data
from simple_trade import premade_optimizer

# Step 1: Load Data
ticker = "AAPL"
start_date = "2020-01-01"
end_date = "2023-12-31"

data = download_data(ticker, start_date, end_date)

# Step 2: Load Optimization Parameters
# Define the parameter grid to search
param_grid = {
    'short_window': [10, 20, 30],
    'long_window': [50, 100, 150],
}

# Step 3: Set Base Parameters
base_params = {
    'initial_cash': 100000.0,
    'commission_long': 0.001,         # 0.1% commission
    'commission_short': 0.001,
    'trading_type': 'long',           # Only long trades
    'day1_position': 'none',
    'risk_free_rate': 0.02,
    'metric': 'total_return_pct',     # Metric to optimize
    'maximize': True,                 # Maximize the metric
    'parallel': False,                # Sequential execution for this example
    'fig_control': 0                  # No plotting during optimization
}

# Step 4: Run Optimization
best_results, best_params, all_results = premade_optimizer(
    data=data,
    strategy_name='sma',
    parameters=base_params,
    param_grid=param_grid
)

# Show top 3 parameter combinations
print("\nTop 3 SMA Parameter Combinations:")
sorted_results = sorted(all_results, key=lambda x: x['score'], reverse=True)
for i, result in enumerate(sorted_results[:3]):
    print(f"  {i+1}. {result['params']} -> {result['score']:.2f}%")

Output of Results

Top 3 SMA Parameter Combinations:
  1. {'short_window': 10, 'long_window': 50} -> 99.87%
  2. {'short_window': 20, 'long_window': 50} -> 85.69%
  3. {'short_window': 30, 'long_window': 50} -> 67.08%

Combining Strategies

Use the run_combined_trade function to combine multiple strategies.

# Example for combining premade strategies

# Load Packages and Functions
from simple_trade import download_data
from simple_trade import run_premade_trade
from simple_trade import run_combined_trade


# Step 1: Download data
print("Downloading stock data...")
symbol = 'AAPL'
start_date = '2020-01-01'
end_date = '2022-12-31'
interval = '1d'
data = download_data(symbol, start_date, end_date, interval=interval)

# Step 2: Set Global Parameters
global_parameters = {
    'initial_cash': 10000,
    'commission_long': 0.001,
    'commission_short': 0.001,
    'short_borrow_fee_inc_rate': 0.0,
    'long_borrow_fee_inc_rate': 0.0,
    'trading_type': 'long',
    'day1_position': 'none',
    'risk_free_rate': 0.0,
}

# Step 3: Compute RSI Strategy
rsi_params = {
    'window': 14,
    'upper': 70,
    'lower': 30,
    'fig_control': 0
}

rsi_params = {**global_parameters, **rsi_params}
rsi_results, rsi_portfolio, _ = run_premade_trade(data, "rsi", rsi_params)

# Step 3: Compute SMA Strategy 
sma_params = {
    'short_window': 20,
    'long_window': 50,
    'fig_control': 0
}

sma_params = {**global_parameters, **sma_params}
sma_results, sma_portfolio, _ = run_premade_trade(data, "sma", sma_params)

# Step 4: Combine RSI and SMA Strategies 
strategies = {
    'RSI': {'results': rsi_results, 'portfolio': rsi_portfolio},
    'SMA': {'results': sma_results, 'portfolio': sma_portfolio}
}

combined_results, combined_portfolio, _ = run_combined_trade(
    portfolio_dfs=[rsi_portfolio, sma_portfolio],
    price_data=data,
    price_col='Close',
    combination_logic='majority',
    trading_type='long',
    fig_control=0,
    strategies=strategies,
    strategy_name='Majority',
    initial_cash=200,
    commission_long=0.001,
    commission_short=0.001
)

print(f"2 Trading Strategy Combination - Final Value: ${combined_results['final_value']:.2f}")
print(f"2 Trading Strategy Combination - Total Return: {combined_results['total_return_pct']}%")
print(f"2 Trading Strategy Combination - Number of Trades: {combined_results['num_trades']}")
print(f"2 Trading Strategy Combination - Sharpe Ratio: {combined_results['sharpe_ratio']:.3f}")

Output of Results

2 Trading Strategy Combination - Final Value: $318.11
2 Trading Strategy Combination - Total Return: 59.16%
2 Trading Strategy Combination - Number of Trades: 13
2 Trading Strategy Combination - Sharpe Ratio: 0.780

Examples

For more detailed examples, please refer to the Jupyter notebooks in the /examples directory:

• /examples/indicators: Demonstrations of various technical indicators.
• /examples/backtest: Examples of backtesting different strategies.
• /examples/optimize: Examples of optimizing strategy parameters.
• /examples/combine_trade: Examples of combining different strategies.
• /examples/lists: Examples of listing functions.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue. (Further details can be added here if needed).

License

This project is licensed under the AGPL-3.0 License - see the LICENSE file for details.