What Is Algorithmic Trading?
Algorithmic trading — also called algo trading, automated trading, or quant trading — is the use of computer programs to execute trades according to predefined rules. Instead of a human watching charts and pressing buy or sell, software monitors the market continuously and places orders the moment specific conditions are met.
At its simplest, an algorithm might say: "If the 20-period moving average crosses above the 50-period moving average, buy 1 BTC." At its most sophisticated, it might analyse dozens of indicators simultaneously across hundreds of symbols, adapt to changing market regimes, size positions dynamically based on volatility, and manage risk in real time.
Algorithmic trading is not a niche practice for hedge funds. Today it accounts for over 70% of daily volume in US equities and a growing share of crypto market activity. Understanding how it works is increasingly essential for anyone serious about trading.
Why Traders Use Algorithms Instead of Manual Trading
The human brain is a poor trading instrument. We experience fear and greed, lose concentration, misremember past trades, and make inconsistent decisions under pressure. A well-designed algorithm has none of these weaknesses.
Key advantages of automated trading:
- Consistency: the algorithm applies the same logic to every trade, at any hour, with no exceptions. It will take the 3 AM signal on a Sunday with exactly the same discipline as the 9 AM signal on Monday. - Speed: computers can react to market conditions in milliseconds. By the time a human notices a breakout, an algorithm has already entered, set stops, and is monitoring the position. - Scale: a single algorithm can simultaneously monitor 500+ trading pairs across multiple exchanges and markets. No human trader can track more than a handful at once. - Emotion-free execution: the algorithm does not panic-sell at the bottom or hold losers hoping they recover. It follows its rules precisely. - Backtesting: before risking real capital, you can run the algorithm against years of historical data to measure its expected performance.
The trade-off is that algorithms require rigorous design and testing. A flawed algorithm will execute its flawed logic perfectly — and lose money just as consistently as a good algorithm makes it.
How an Algorithmic Trading Strategy Works
Every algorithmic trading strategy has four components:
1. Signal generation: the algorithm analyses market data (price, volume, technical indicators, news sentiment) to identify a potential trade opportunity. For example, RSI crossing back above 30 after being oversold signals a potential long entry.
2. Entry rules: specific conditions that must be met to place a trade. Most strategies require multiple confirmations — not just RSI, but also a trend filter (EMA-200 pointing up) and volatility check (ATR within normal range).
3. Exit rules: when to close the trade. This includes take-profit targets (where you book profits), stop-loss levels (where you accept a loss to protect capital), and time-based exits (close if position is still open after 24 hours).
4. Position sizing: how large the trade should be, calculated from your account size, the stop-loss distance, and your maximum risk per trade (typically 1-2% of capital).
FerroQuant combines these four components across 165+ individual strategies, each optimised per instrument using walk-forward validation on 7 years of historical data.
Types of Algorithmic Trading Strategies
The two broadest categories are mean reversion and momentum trading.
Mean reversion strategies assume that prices oscillate around a long-term average. When an asset becomes "too cheap" (RSI oversold, price at lower Bollinger Band), the strategy buys, expecting a return to average. When it becomes "too expensive," it sells or shorts. These strategies work best in ranging, sideways markets.
Momentum strategies assume that trends persist. When an asset breaks out of a range on high volume, or when a moving average crossover confirms a new trend, the strategy enters in the direction of the move. These strategies work best during sustained directional moves.
Beyond these fundamentals, algorithmic traders also use:
- Statistical arbitrage: exploiting temporary price discrepancies between correlated instruments - Market making: placing limit orders on both sides to profit from the bid-ask spread - High-frequency trading (HFT): thousands of tiny trades per second, exploiting microstructure inefficiencies - Machine learning strategies: models trained to predict short-term price direction using large feature sets
FerroQuant focuses on technical indicator strategies (mean reversion and momentum) across crypto, forex, and commodities — a robust approach that does not require co-location or special market access.
Getting Started with Algorithmic Trading
If you are new to algorithmic trading, the learning path looks like this:
1. Understand the basics: learn how technical indicators work (RSI, MACD, Bollinger Bands, moving averages). These are the building blocks of most strategies.
2. Learn to backtest: before running any algorithm live, test it against historical data. This reveals whether the strategy has a positive expected value and what kind of drawdowns to expect.
3. Start with a defined strategy: do not build a strategy from scratch on day one. Use a proven strategy type (RSI mean reversion, MACD crossover) and understand exactly why it should work.
4. Use a platform: running raw code against live exchange APIs requires significant infrastructure. Platforms like FerroQuant handle data feeds, signal generation, and execution — letting you focus on strategy selection and risk management.
5. Start small: risk 0.5-1% per trade maximum when starting out. The goal is to learn, not to get rich in month one. Consistency compounds over time.
Algorithmic trading rewards patience and discipline. The systems that succeed long-term are not the cleverest — they are the most rigorously tested and the most honestly managed.