stocksimpy 0.1.1

A beginner-friendly library for stock market data, backtesting, and strategies.

stocksimpy

Easy Python backtesting for stocks — fast and simple

stocksimpy lets you prototype trading ideas with minimal boilerplate. Everything is explicit — load data, define rules, run a backtest. No configuration sprawl, no hidden state, and no magic methods.. Perfect for beginners and anyone who wants results fast.

(Disclaimer: This is an early alpha, API might change in the future updates)

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🎯 Quick Features

• Load stock prices from yfinance or your own database in seconds.
• Run fixed or dynamic backtests in just a few lines of code.
• Built-in example strategies like SMA/EMA crossover, RSI, and price action.
• Clear and simple design — no hidden magic, everything is easy to read.
• Beginner-friendly: start experimenting immediately.

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⚡ Quick Start

1. Install stocksimpy (and yfinance for convenient data input)

pip install stocksimpy yfinance

2. Imports

from stocksimpy import StockData, Backtester, Visualize, Performance

3. Load Data

# If you want to load your own data from .csv, .sqlite or similar, use appropriate functions built into `StockData()`
data = StockData().from_yfinance([your_stock_symbol], your_starting_date, your_end_date)

4. Define Your Strategy

def sma_crossover(prices):

    # Don't do any trades until long range
    if(len(prices) < 50):
        return "hold"
    
    short = prices["Close"].rolling(20).mean()
    long = prices["Close"].rolling(50).mean()

    # Buy when short crosses above long, sell when it crosses below
    if short > long:
        return "buy"
    else:
        return "sell"

5. Run Backtest

# You can use your own strategy or one of the built-in ones for testing (e.g. Strategy.rsi_momentum_fixed())
bt = Backtester('AAPL', data, sma_crossover)

# Depending on your strategy you may need to run bt.run_backtest_dynamic(), see more on the documentation
bt.run_backtest_fixed()

6. Evaluate the Results

# View performance metrics like max drawdown and sharpe ratio
perf = Performance(bt)
print(perf.generate_risk_report())

7. Visualize

# Visualize the graph
graph = Visualize(bt)
graph.visualize_backtest().show() # You can use .savefig(...) instead of .show() to record the graph in disk

And thats it, you just ran your first backtest 🚀

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🤝 Contributions

Ideas, strategies, or improvements? We welcome all contributions! Check out CONTRIBUTING.md for a more detailed information about contributing

📃 Docs

More in depth explanation on how to use this library can be found in ReadTheDocs.