Intro to Algo Trading

To move from a manual trader to an algorithmic trader, you must view your strategy not as a "feeling" but as an "if/then" logic statement. A robust algorithmic system consists of three distinct pillars: the Alpha (Strategy), the Execution, and the Risk Management.

1. The Alpha: Defining the Strategy

Your algorithm needs a strict set of rules. You cannot tell a computer to "buy when the trend looks strong." You must be specific.

• Trend Following: "Buy if the 50-day Moving Average crosses above the 200-day Moving Average."
• Mean Reversion: "Buy if the price deviates 2 standard deviations from the VWAP (Volume Weighted Average Price)."
• Arbitrage: "Buy Stock A on Exchange X and sell it on Exchange Y if the price difference exceeds transaction costs."

2. The Backtest: The Reality Check

Before you go live, you must backtest. This involves running your code against historical data (e.g., the last 5 years of the S&P 500 or GBP/JPY).

• Warning on Overfitting: A common pitfall is "curve-fitting," where you tweak parameters until the past data looks profitable. A robust algorithm should work on "out-of-sample" data—data it has never seen before.
• Data Quality: Garbage in, garbage out. Ensure you are using clean, tick-level data that accounts for dividends, splits (in stocks), and spreads (in forex).

3. Execution and Infrastructure

Once the strategy is proven, it needs to interact with the market.

• APIs (Application Programming Interfaces): Your code (written in Python, C++, or Pine Script) talks to your broker via an API.
• Latency: In Forex, speed matters. If your signal triggers at 1.0500 but your internet lag fills you at 1.0505, your edge might disappear. Many algo traders use Virtual Private Servers (VPS) co-located near exchange servers to minimize this delay.

4. Automated Risk Management

This is your safety net. Your code must include a "Kill Switch."

• If the algorithm loses X% of equity in a single day, it shuts down.
• If volatility spikes beyond a certain threshold, it halts trading. This prevents a coding error from draining your account in minutes (a "flash crash" scenario).

Conclusion Algorithmic trading is a journey of continuous refinement. It requires you to be part trader, part data scientist. However, the reward is a trading business that is scalable, consistent, and independent of your hourly labor. Start small, learn Python or a dedicated platform language, and test relentlessly.