Getting Started with Algorithmic Trading in Python

Algorithmic trading uses computer programs to execute trades based on predefined rules. In this post, we’ll walk through the fundamentals of building a simple trading system in Python.

Setting Up Your Environment

First, install the required packages:

pip install pandas numpy matplotlib

Loading Market Data

We’ll start by loading historical price data into a pandas DataFrame:

import pandas as pd
import numpy as np

# Load historical data
df = pd.read_csv("prices.csv", parse_dates=["date"], index_col="date")
print(df.head())

A Simple Moving Average Crossover

One of the most common starter strategies is the moving average crossover:

def moving_average_crossover(df: pd.DataFrame, short: int = 20, long: int = 50):
    """Generate signals based on moving average crossover."""
    df["sma_short"] = df["close"].rolling(window=short).mean()
    df["sma_long"] = df["close"].rolling(window=long).mean()

    df["signal"] = 0
    df.loc[df["sma_short"] > df["sma_long"], "signal"] = 1
    df.loc[df["sma_short"] < df["sma_long"], "signal"] = -1

    return df

When the short-term moving average crosses above the long-term average, we go long. When it crosses below, we go short.

Backtesting the Strategy

Here’s a minimal backtest to evaluate performance:

def backtest(df: pd.DataFrame) -> dict:
    """Run a simple backtest on signal column."""
    df["returns"] = df["close"].pct_change()
    df["strategy_returns"] = df["signal"].shift(1) * df["returns"]

    total_return = (1 + df["strategy_returns"]).cumprod().iloc[-1] - 1
    sharpe = df["strategy_returns"].mean() / df["strategy_returns"].std() * np.sqrt(252)

    return {"total_return": total_return, "sharpe_ratio": sharpe}

What’s Next

This is a starting point. In future posts we’ll cover:

• Risk management and position sizing
• More sophisticated signal generation with machine learning
• Live execution with broker APIs
• Tokenized market microstructure