Processamento de Sinais Financeiros
Algorithmic trading (algo trade) is using computer programs to execute trades automatically based on predefined rules, mathematical models, or machine learning signals.
The idea: instead of a human watching charts and clicking buy/sell, an algorithm monitors market data in real time and executes trades in milliseconds â faster and more consistently than any human could.
Types of algo trading strategies:
- Trend following â identify and ride market trends. Buy when the price is going up, sell when itâs going down. Simple in concept, hard in execution (when is a trend a trend vs. noise?).
- Mean reversion â prices tend to return to their average over time. If a stock drops unusually far, buy it expecting a bounce back.
- Arbitrage â exploit price differences between markets. If the same asset is priced differently on two exchanges, buy low and sell high simultaneously. Requires extreme speed.
- Market making â provide liquidity by continuously offering to buy and sell, profiting from the bid-ask spread. High frequency, low margin per trade.
- Statistical arbitrage â find correlations between assets and trade when they diverge from their usual relationship.
- Sentiment analysis â analyze news, social media, and other text data to predict market movements.
Key components:
- Signal generation â the model that identifies trading opportunities
- Risk management â position sizing, stop losses, exposure limits. This is what keeps you alive.
- Execution â actually placing trades with minimal market impact and latency
- Backtesting â testing your strategy against historical data before risking real money
The danger: backtesting can be deceiving. Strategies that look amazing on historical data often fail in live markets. Overfitting, survivorship bias, and changing market conditions are real threats.
Related: Investing, Data crunching, Machine Learning