What is Algorithmic Trading?
Algorithmic trading uses computer programs to execute trades automatically based on predefined rules. Unlike discretionary trading, algo systems operate without emotion, execute faster than humans, and can monitor hundreds of instruments simultaneously. From simple moving average crossovers to complex machine learning models, all systematic approaches fall under algo trading.
Why Algorithmic Trading?
- Remove emotions: Algorithm follows rules exactly, no fear or greed
- Speed: Execute in milliseconds, impossible for humans
- Consistency: Same logic applied every time, no "gut feelings"
- Scalability: Monitor unlimited markets simultaneously
- Backtestability: Precise rules enable accurate historical testing
- 24/7 operation: Trade crypto/forex while you sleep
Algorithmic trading isn't just for hedge funds. Retail traders use algos for automated swing trading, rebalancing portfolios, and systematic options strategies. The key: rigorous backtesting to ensure your algorithm works before risking real capital.
Algorithmic Trading Strategies
Trend Following
Most popular algo strategy. Follow momentum, ride trends until they reverse. Simple rules, effective in trending markets.
Examples:
- • Moving average crossover (50/200 MA)
- • Breakout systems (new highs/lows)
- • Donchian channel breakouts
- • ADX + directional filters
Characteristics:
- • Win rate: 35-45%
- • R:R ratio: 2:1 to 4:1
- • Works best: Commodities, forex
- • Fails in: Choppy, range-bound markets
Mean Reversion
Bet on prices returning to average. Buy oversold, sell overbought. Works in range-bound markets.
Examples:
- • RSI extremes (RSI < 30 or > 70)
- • Bollinger Band bounces
- • Statistical pairs trading
- • Mean reversion to VWAP
Characteristics:
- • Win rate: 55-70%
- • R:R ratio: 1:1 to 1.5:1
- • Works best: Stocks, indices
- • Fails in: Strong trending markets
Statistical Arbitrage
Exploit temporary price inefficiencies between correlated instruments. Pairs trading, triangular arbitrage, index arbitrage.
Pairs Trading Example:
Trade AAPL vs MSFT when spread diverges from historical mean.
1. Calculate z-score of price ratio over 60 days
2. When z > +2: Short AAPL, long MSFT
3. When z < -2: Long AAPL, short MSFT
4. Exit when z returns to 0 (mean)
Challenges: Correlations break down, execution slippage, transaction costs eat thin margins. Requires sophisticated infrastructure.
Market Making
Provide liquidity by simultaneously posting buy and sell orders. Profit from bid-ask spread, earn rebates.
Strategy:
- • Place limit orders on both sides
- • Adjust quotes based on inventory
- • Manage adverse selection risk
- • Hedge overnight positions
Requirements:
- • Ultra-low latency (< 1ms)
- • Co-location near exchange
- • Risk management systems
- • Significant capital ($100K+)
Machine Learning Strategies
Use ML models to predict price direction, volatility, or optimal execution. Neural networks, random forests, gradient boosting.
- Classification: Predict up/down/sideways movement
- Regression: Predict next price or return magnitude
- Reinforcement learning: Optimize execution or portfolio allocation
- Warning: Extremely easy to overfit. Requires extensive validation and large datasets
Execution Simulation
The difference between backtest and live performance often comes down to execution quality. Naive backtests assume instant fills at mid-price. Reality is messy.
Key Metrics to Track
| Metric | Good | Red Flag |
|---|---|---|
| Win rate | 35-55% (trend), 55-70% (mean reversion) | >70% with tiny R:R |
| Profit factor | 1.4-2.0 | >3.0 with few trades |
| Sharpe ratio | 1.0-2.0 (retail infra) | >3.0 without institutional costs |
| Max drawdown | <20% | >30% or unrecovered |
| Turnover / day | Sized to pay <0.1% slippage+fees per round trip | Costs ignored; PF collapses after costs |
| Latency impact | Modeled per timeframe (1-5s mid-freq, 10-100ms HFT) | Assumed zero latency |
Slippage Modeling
Slippage is the difference between expected and actual execution price. Critical to model accurately:
- • Fixed slippage: Simple but unrealistic. "Always 0.05% per trade"
- • Percentage of spread: Better. Pay half spread on limit orders, full spread on market orders
- • Volume-based: Most accurate. Larger orders relative to volume = more slippage
- • Volatility-adjusted: Higher volatility = wider spreads = more slippage
Realistic Slippage Estimates:
Liquid stocks (AAPL, SPY): 0.02-0.05% per side
Mid-cap stocks: 0.05-0.15% per side
Small-cap, illiquid: 0.2-0.5%+ per side
Crypto: 0.1-0.3% on major pairs, 0.5-2% on altcoins
Latency Effects
Time between signal generation and order arrival at exchange. In fast markets, price can move significantly.
- • Low frequency (daily): Latency irrelevant, use close prices
- • Medium frequency (hourly/15-min): Model 1-5 second delay
- • High frequency (second/tick): Model 10-100ms delay, critical for accuracy
- • Reality check: If your strategy needs < 100ms latency, you need co-location and dedicated infrastructure
Order Types
Different order types have different fill characteristics. Model appropriately:
- Market orders: Guaranteed fill, pay spread + slippage. Use in backtest if speed critical
- Limit orders: Better price, but may not fill. Model 50-80% fill rate depending on limit price
- Stop orders: Become market orders when triggered. Model with additional slippage in fast markets
- Iceberg orders: Hide size, reduce market impact. Advanced modeling needed
Data Requirements
Data Quality is Everything
Algorithmic strategies are only as good as their data. Garbage in, garbage out applies 10x for algo trading.
- Tick data: Every trade/quote. Required for HFT, useful for day trading algos. 10GB+ per symbol per year
- Minute bars: OHLCV every minute. Sufficient for most intraday strategies. 500MB per symbol per year
- Daily data: Good for swing/position algos. Cheap, widely available. 1-10MB per symbol
- Order book depth: L2/L3 data. Essential for market making, expensive and complex to process
- Fundamental data: Earnings, ratios for quantitative strategies. Quarterly snapshots
Data Providers
- Free: Yahoo Finance, Alpha Vantage (daily, limited)
- Budget ($50-200/mo): Polygon.io, IEX Cloud, Alpaca
- Professional ($500+/mo): Interactive Brokers, Bloomberg, Reuters
- Crypto: Binance API (free), Kaiko, CryptoCompare
Data Pitfalls
- Survivorship bias: Missing delisted/bankrupt companies
- Look-ahead bias: Using future data in signals
- Splits/dividends: Must adjust historical prices
- Bad ticks: Outliers from data errors. Clean before backtesting
Backtesting Challenges
Overfitting Danger
Algorithmic strategies are especially vulnerable to overfitting. With enough parameters and computing power, you can create a strategy that perfectly fits historical data but fails miserably live. Solutions: Out-of-sample testing, walk-forward analysis, parameter stability tests, simple strategies (fewer parameters), robust optimization (accept suboptimal backtest if more stable).
Transaction Costs
High-frequency algos can make 100-1000 trades per day. If each trade costs 0.1% in slippage + commissions:
100 round trips/day = 200 fills
200 × 0.1% = 20% daily cost = 5,000%+ annual drag
Strategy must generate > 5,000% annual return just to break even!
This is why most HFT strategies fail retail traders. You don't have the infrastructure for low enough costs.
Market Regime Changes
Markets evolve. A trend-following algo that worked beautifully in 2010-2020 might fail in 2021-2023 if market structure changed. Test across multiple regimes: bull markets, bear markets, high volatility (2008, 2020), low volatility (2017), rising rates, falling rates. If strategy only works in one regime, it's not robust.
Implementation Gap
Difference between backtest code and live trading code. Common issues: Timezone bugs (using wrong close time), Rounding errors (prices vs actual fills), State management (positions not tracking correctly), API failures (connection drops, rate limits). Solution: Keep backtest and live code identical. Test extensively in paper trading before live.
Common Algorithmic Trading Mistakes
Backtesting on Adjusted Prices, Trading on Actual
Adjusted prices account for splits/dividends. Actual prices are what you trade. If you backtest on adjusted but don't adjust entry/stop prices for live trading, your strategy will behave completely differently. Always use consistent price series or properly map between adjusted and actual.
Assuming Infinite Liquidity
Backtest shows "buy 10,000 shares at $50.00". But order book only has 500 shares at $50.00, rest at $50.05-50.10. Your fill is $50.06 average, not $50.00. This adds up fast. Always model available liquidity and market impact, especially for larger positions or illiquid securities.
Optimizing for Sharpe Ratio Alone
High Sharpe looks great but may hide problems. Can achieve high Sharpe by selling far OTM options (steady premium, rare large losses) or using huge leverage with tight stops (many small wins, occasional blowup). Always check: maximum drawdown, worst single trade, consecutive losers, recovery time, tail risk exposure.
No Kill Switch
Algo keeps trading during flash crash, exchange outage, or when clearly malfunctioning. One bad day can erase months of profits. Implement: Maximum daily loss limit (shut down if hit), Position size limits (prevent runaway positions), Connection monitoring (stop if data feed dies), Manual override (emergency stop button). Test these regularly.
Underestimating Development Time
"I'll have it running in a week" turns into 6 months. Reality: Data cleaning (20%), Backtest engine (20%), Strategy coding (15%), Debugging (20%), Paper trading (15%), Live testing (10%). Budget 3-6 months for first algo, 1-3 months for subsequent strategies. Trying to rush leads to bugs and losses.
Frequently Asked Questions
Do I need to know programming to do algorithmic trading?
How much capital do I need for algorithmic trading?
What's a realistic return expectation for algo trading?
Should I use cloud computing or local machine for backtesting?
How do I know if my algo is ready for live trading?
Can algorithmic trading work for cryptocurrency?
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