simple-trade 0.1.2

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 and backtest common trading strategies:
  • Cross Trade Strategies (cross_trade)
  • Band Trading Strategies (band_trade)
• 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.

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

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

# Load Packages and Functions
import pandas as pd
from simple_trade import compute_indicator, download_data
from simple_trade import IndicatorPlotter

# 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
columns["high_col"] = 'High'
columns["low_col"] = 'Low'
columns["close_col"] = 'Close'
data = compute_indicator(
    data=data,
    indicator='adx',
    parameters=parameters,
    columns=columns
)

# Step 3: Plot the indicator
plotter = IndicatorPlotter()
window = parameters["window"]
columns = [f'ADX_{window}', f'+DI_{window}', f'-DI_{window}']
fig = plotter.plot_results(
        data,
        price_col='Close',
        column_names=columns,
        plot_on_subplot=True, 
        title=f"{symbol} with ADX({window})"
    )

# Step 4: Display the plot
fig.show()

Plot of Results

Advanced Usage

Backtesting Strategies

Use the backtesting module to simulate strategies like moving average crossovers (cross_trade) or Bollinger Band breakouts (band_trade).

# Load Packages and Functions
import pandas as pd
from simple_trade import download_data, compute_indicator
from simple_trade import CrossTradeBacktester
from simple_trade import BacktestPlotter

# 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: Download indicators
short_window = 25
long_window = 75
data = compute_indicator(data, indicator='sma', window=short_window)
data = compute_indicator(data, indicator='sma', window=long_window)

# Step 3: Initialize strategy
initial_cash = 10000.0
commission = 0.01
backtester = CrossTradeBacktester(initial_cash=initial_cash, commission_long=commission)

results, portfolio = backtester.run_cross_trade(
    data=data,
    short_window_indicator="SMA_25",
    long_window_indicator="SMA_75",
    price_col='Close',
)

# Step 4: Produce results
backtester.print_results(results)

# Step 5: Plot results
plotter = BacktestPlotter()
indicator_cols_to_plot = [f'SMA_{short_window}', f'SMA_{long_window}']
fig = plotter.plot_results(
    data_df=data,
    history_df=portfolio,
    price_col='Close',
    indicator_cols=indicator_cols_to_plot, 
    title=f"Cross Trade (Long Only) (SMA-{short_window} vs SMA-{long_window})"
)

# Step 6: Display the plot
plt.show()

Output of Results

============================================================
              ✨ Cross Trade (SMA_25/SMA_75) ✨               
============================================================

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

📊 BASIC METRICS:
  • Initial Investment: $10,000.00
  • Final Portfolio Value: $11,400.77
  • Total Return: 14.01%
  • Annualized Return: 4.96%
  • Number of Trades: 16
  • Total Commissions: $1,936.74

📈 BENCHMARK COMPARISON:
  • Benchmark Return: 71.32%
  • Benchmark Final Value: $17,132.49
  • Strategy vs Benchmark: -57.31%

📉 RISK METRICS:
  • Sharpe Ratio: 0.320
  • Sortino Ratio: 0.260
  • Maximum Drawdown: -33.59%
  • Average Drawdown: -15.36%
  • Max Drawdown Duration: 849 days
  • Avg Drawdown Duration: 61.33 days
  • Annualized Volatility: 23.75%

Plot of Results

Optimizing Strategies

The optimizer module allows you to find the best parameters for your strategy (e.g., optimal moving average windows).

# Load Packages and Functions
from simple_trade import download_data, compute_indicator
from simple_trade import CrossTradeBacktester
from simple_trade import 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],
}
# Define constant parameters for the backtester
initial_capital = 100000
commission_fee = 0.001 # 0.1%
constant_params = {
    'initial_cash': initial_capital, 
    'commission_long': commission_fee,
    'price_col': 'Close'
}
# Define the metric to optimize and whether to maximize or minimize
metric_to_optimize = 'total_return_pct'
maximize_metric = True

# Step 3: Define the wrapper function
def run_cross_trade_with_windows(data, short_window, long_window, **kwargs):
    # Work on a copy of the data
    df = data.copy()
    
    # Compute the SMA indicators
    df = compute_indicator(df, indicator='sma', parameters={'window': short_window}, columns={'close_col': 'Close'})
    df = compute_indicator(df, indicator='sma', parameters={'window': long_window}, columns={'close_col': 'Close'})
     
    # Get the indicator column names
    short_window_indicator = f"SMA_{short_window}"
    long_window_indicator = f"SMA_{long_window}"
    
    # Create a backtester instance
    backtester = CrossTradeBacktester(
        initial_cash=kwargs.pop('initial_cash', 10000),
        commission_long=kwargs.pop('commission_long', 0.001),
    )
    
    # Run the backtest
    return backtester.run_cross_trade(
        data=df,
        short_window_indicator=short_window_indicator,
        long_window_indicator=long_window_indicator,
        **kwargs
    )

# Step 4: Instantiate and Run Optimizer
print("Initializing Optimizer...")
optimizer = Optimizer(
    data=data,
    backtest_func=run_cross_trade_with_windows,  # Use our wrapper function
    param_grid=param_grid,
    metric_to_optimize=metric_to_optimize,
    maximize_metric=maximize_metric,
    constant_params=constant_params
)

print("\nRunning Optimization (Parallel)...")
# Run optimization with parallel processing (adjust n_jobs as needed)
results = optimizer.optimize(parallel=True, n_jobs=-1) # n_jobs=-1 uses all available cores

# --- Display Results ---
print("\n--- Optimization Results ---")

# Unpack results
best_params, best_metric_value, all_results = results

print("\n--- Top 5 Parameter Combinations ---")
# Sort results for display
sorted_results = sorted(all_results, key=lambda x: x[1], reverse=maximize_metric)
for i, (params, metric_val) in enumerate(sorted_results[:5]):
    print(f"{i+1}. Params: {params}, Metric: {metric_val:.4f}")

Output of Results

print("\nRunning Optimization (Parallel)...")
# Run optimization with parallel processing (adjust n_jobs as needed)
results = optimizer.optimize(parallel=True, n_jobs=-1) # n_jobs=-1 uses all available cores

# --- Display Results ---
print("\n--- Optimization Results ---")

# Unpack results
best_params, best_metric_value, all_results = results

print("\n--- Top 5 Parameter Combinations ---")
# Sort results for display
sorted_results = sorted(all_results, key=lambda x: x[1], reverse=maximize_metric)
for i, (params, metric_val) in enumerate(sorted_results[:5]):
    print(f"{i+1}. Params: {params}, Metric: {metric_val:.4f}")

Initializing Optimizer...
Generated 9 parameter combinations.

Running Optimization (Parallel)...
Starting optimization for 9 combinations...
Metric: total_return_pct (Maximize) | Parallel: True (n_jobs=-1)
Using 16 parallel jobs.
[Parallel(n_jobs=16)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done   2 out of   9 | elapsed:    9.0s remaining:   31.6s
[Parallel(n_jobs=16)]: Done   4 out of   9 | elapsed:    9.4s remaining:   11.7s
[Parallel(n_jobs=16)]: Done   6 out of   9 | elapsed:    9.5s remaining:    4.7s
Optimization finished in 9.86 seconds.
Best Parameters found: {'short_window': 10, 'long_window': 50}
Best Metric Value (total_return_pct): 89.0500

--- Optimization Results ---

--- Top 5 Parameter Combinations ---
1. Params: {'short_window': 10, 'long_window': 50}, Metric: 89.0500
2. Params: {'short_window': 20, 'long_window': 50}, Metric: 76.6100
3. Params: {'short_window': 30, 'long_window': 50}, Metric: 60.6400
4. Params: {'short_window': 10, 'long_window': 150}, Metric: 19.4100
5. Params: {'short_window': 20, 'long_window': 100}, Metric: 10.9600
[Parallel(n_jobs=16)]: Done   9 out of   9 | elapsed:    9.7s finished

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.

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.