Algorithmic Trading

1. Trend Following: Buy assets in uptrends, sell in downtrends using moving averages, momentum indicators, or breakouts. No prediction required — just follow what's happening.

2. Mean Reversion: When a price deviates too far from its average, bet on it returning. Requires statistical definition of "too far" — typically using Bollinger Bands, Z-score, or RSI extremes.

3. Momentum: Buy yesterday's winners, sell yesterday's losers. Strong short-term persistence of returns has been documented across decades and asset classes.

4. Statistical Arbitrage: Exploit pricing inefficiencies between related assets — pairs trading, ETF vs. basket arbitrage, cross-exchange differences.

5. Market Making: Continuously quote bids and offers to profit from the spread. Requires speed, large capital, and sophisticated inventory management.

6. High-Frequency Trading (HFT): Sub-millisecond execution exploiting fleeting inefficiencies. Requires co-location, direct market access, and custom hardware.

7. Machine Learning / AI: Train models on historical data to predict price direction, classify setups, or optimize parameters dynamically.

8. Event-Driven: Trade around earnings, economic releases, M&A news, or SEC filings. Requires fast data feeds and pre-built reaction logic.

Getting started with algo trading does not require a Ph.D. or millions in capital. Here is a practical roadmap:

Step 1 — Learn Python: Python is the dominant language for retail algo trading. You need basic proficiency with pandas (data manipulation), numpy (numerical computing), and matplotlib (charting). Most broker APIs have Python SDKs.

Step 2 — Get market data: Free sources include Yahoo Finance (via yfinance library), Alpha Vantage, and FRED (economic data). Paid sources like Polygon.io provide real-time and historical data with millisecond granularity. Start with free data for backtesting, upgrade when you go live.

Step 3 — Choose a broker with an API: Alpaca (commission-free, Python SDK), Tradier (low-cost options), and Interactive Brokers (widest market access) all offer programmatic order submission. Paper trading (simulated orders) is available on all three — always start in paper mode.

Step 4 — Backtest your strategy: Use historical data to simulate how your strategy would have performed. Libraries like backtrader, vectorbt, and zipline handle backtesting. Watch for overfitting — a strategy that looks perfect on historical data may fail live because it was tuned to past noise rather than genuine patterns.

Step 5 — Paper trade: Run your algorithm on live market data but submit simulated orders. Paper trading validates that your code handles real-world conditions: partial fills, slippage, market hours, and data delays. Paper trade for at least 2-4 weeks before risking real capital.

Step 6 — Go live with small size: Start with the minimum position size your broker allows. Scale up gradually as you verify that live performance matches backtest expectations. Most successful algo traders spend months in paper and small-size mode before trading meaningful capital.

Most algorithmic trading failures come from the same set of well-known pitfalls:

Overfitting: The most dangerous mistake. Adding more parameters to a strategy improves backtest performance but degrades live performance. A strategy with 15 optimized parameters is almost certainly overfit to historical noise. The best algo strategies have 3-5 parameters and work across multiple time periods and markets.

Ignoring transaction costs: A strategy that produces 0.1% profit per trade looks great in a frictionless backtest. Add commissions ($0.005/share), spread costs ($0.01-$0.05), and slippage ($0.01-$0.10 depending on size and liquidity), and the strategy may be unprofitable. Always include realistic transaction cost models in backtests.

Survivorship bias: If your backtest data only includes stocks that exist today, you are missing all the companies that went bankrupt or were delisted. Strategies backtested on survivorship-biased data overestimate returns because they never bought the stocks that went to zero.

Look-ahead bias: Using information in your backtest that would not have been available at the time of the trade. Common examples: using end-of-day price data for signals that should be calculated intraday, or using fundamental data before its actual release date.

Lack of risk management: An algorithm that generates great returns without stop-losses or position limits will eventually blow up. Every live algorithm needs: maximum position size, daily loss limit (circuit breaker), maximum drawdown threshold, and correlation limits (not all positions in the same sector).

Neglecting regime changes: A strategy optimized for the 2020-2024 bull market may fail catastrophically in a bear market. Test across multiple market regimes (bull, bear, choppy, crisis) before deploying live.