How to Create an Algorithmic Trading Strategy from Scratch

You have the motivation for algorithmic trading. You've seen the available tools. But there's a question nobody answers well: how do you create a strategy from scratch?

Most resources jump straight to backtesting. They teach you how to test strategies, but not how to generate them. It's like teaching how to edit a text without explaining how to write it. And yet, over 90% of trading volume in global stock markets is now executed through algorithmic trading (SEC), which shows that mastering this process is more relevant than ever.

This article covers the complete CREATION process: from where to get ideas to having functional code ready to test. We start with the basics.

"Most traders spend 90% of their time optimizing strategies and 10% generating them. It should be the other way around." — Ernest Chan, Quantitative Trading

Ideas don't appear from nowhere. Professional traders have systematic sources where they look. Here are the main ones, ordered by accessibility and quality.

1. Academic literature

Academic papers document market anomalies that contradict efficient market theory. There are more than 150 documented anomalies in financial literature. While many disappear after publication, they're a solid starting point.

Andreas Clenow, author of Following the Trend and Stocks on the Move, recommends starting with classic papers like Jegadeesh & Titman (1993) on momentum or Fama & French on value. These are proven ideas with decades of evidence.

2. Classic trading books

Old books are gold for algorithmic traders. Why? Because if a strategy was published 20 or 30 years ago, you can validate it with decades of real data. If it still works, you have robustness evidence that no recent paper can offer.

Larry Williams won the Robbins World Cup Championship turning $10,000 into $1,100,000 in one year. His volatility breakout strategies are still studied today. Toby Crabel's book is so valued that the author tried to buy back all copies, driving up its price on the secondary market.

Curtis Faith, in Way of the Turtle, documents how the legendary Turtle Traders experiment proved that a system of simple rules (Donchian Channel breakout with volatility-based position sizing) could generate extraordinary returns consistently. Faith emphasizes that the system's success wasn't in the complexity of the rules, but in the discipline to follow them and in proper position sizing.

3. Documented market anomalies

Anomalies are empirical patterns that contradict the efficient market hypothesis. They form the basis of many quantitative strategies.

However, not all anomalies are exploitable. According to Hou et al. (2020), 65% of 452 documented anomalies fail to meet the statistical significance threshold, a figure that rises to 82% when multiple-testing adjustments are applied.

Furthermore, according to McLean & Pontiff (2016), academic strategy returns decline 26% out of sample and 58% after publication, indicating that much of the alpha disappears once the market learns about the anomaly. This reinforces the need to work with quality market data to verify any pattern before trading it.

3. Direct market observation

Sometimes the best ideas come from watching the market in real time. Patterns you see repeating, behaviors at certain hours, reactions to news.

Kevin Davey, author of Building Winning Algorithmic Trading Systems and World Cup Trading Championship winner, shares that some of his best strategies came from observing how oil futures behaved in the first hours of the session. Davey estimates that only 1 in 20 strategy ideas survives the complete validation process to trade live, which underscores the importance of generating many quality ideas and not becoming emotionally attached to any single one.

4. Adapting existing strategies

You don't need to reinvent the wheel. Classic strategies like moving average crossovers, RSI, Bollinger Bands or Breakouts have decades of use because they capture fundamental market concepts.

• Trend Following: Follow established trends (Turtle Trading, moving averages)
• Mean Reversion: Bet on return to the mean (extreme RSI, Bollinger Bands)
• Breakout: Enter when price breaks a range (Donchian Channels)
• Momentum: Buy recent winners (moving average crossovers, ROC)

The key isn't copying, but understanding why they work and adapting them to your market, timeframe and style.

5. Intuition + experience

If you have discretionary trading experience, you already have implicit hypotheses. The process is making them explicit and testable.

"When VIX spikes a lot, the market tends to bounce" is intuition. Converting it to "If VIX rises >30% in a day, buying SPY at close has positive mean return at 5 days" is a testable hypothesis.

An idea is not a hypothesis. "Momentum works" is an idea. A hypothesis is specific, measurable and falsifiable.

Criteria for a testable hypothesis

The HARKing problem

HARKing = Hypothesizing After the Results are Known. It's formulating the hypothesis after seeing what worked in the data. It seems legitimate but guarantees overfitting.

Marcos López de Prado, in Advances in Financial Machine Learning, documents how more than 90% of academic strategies fail in real implementation precisely because of this problem.

Hypothesis template

HYPOTHESIS: [Descriptive name]
DATE: [Formulation date - BEFORE testing]

PREMISE:
[Why should it work? What inefficiency does it exploit?]

RULES:
- Market: [E.g.: SPY, ES, EURUSD]
- Timeframe: [E.g.: Daily, 1H]
- Entry: [Specific condition]
- Exit: [Specific condition]
- Stop Loss: [If applicable]
- Position Sizing: [Fixed or variable]

TARGET METRICS (define BEFORE testing):
- Minimum Profit Factor: [E.g.: > 1.3]
- Minimum Sharpe Ratio: [E.g.: > 0.8]
- Maximum Max Drawdown: [E.g.: < 20%]
- Minimum number of trades: [E.g.: > 100]

NULL HYPOTHESIS:
"The strategy is no better than random/buy-and-hold"

Without edge, there's no strategy. Only statistical noise that happened to work in the past. The edge is the reason why your strategy should keep working in the future.

Types of exploitable edge

Is it real or noise?

Ask yourself: Can I explain WHY this pattern should persist? If the answer is "because it worked in the backtest," you don't have an edge.

Simplicity is an advantage, not a limitation. The most robust strategies typically have 2-4 main rules. Adding more increases overfitting risk without improving real performance.

"If you need more than 5 rules to describe your strategy, you're probably fitting noise, not signal." — Robert Pardo, Design, Testing, and Optimization of Trading Systems

Essential components

Every strategy needs these basic components. You can review the anatomy of a strategy for more detail.

What NOT to include in the creation phase

• Optimized filters: "I added this filter because it improved the backtest" → HARKing
• Adjusted parameters: If you change RSI from 14 to 12 because "it works better," you're optimizing, not creating
• Exception rules: "Except on Mondays" or "only in even months" → Overfitting signs
• Complex multiple timeframes: Add complexity without clear edge

Code is the implementation of your hypothesis. Code errors = misleading results. Best practices now save pain later.

Choosing a platform and language

Your tool choice depends on your experience and goals. The most popular options include Python with Backtrader for custom strategies, TradingView with Pine Script for rapid visual prototyping, MetaTrader for Forex/CFDs, and QuantConnect for institutional strategies. You can see a complete comparison of algorithmic trading tools in our dedicated article.

Maintainable code structure

# Example: Clear structure in Python

class MomentumStrategy:
    """
    Simple momentum strategy.

    Hypothesis: Assets with higher returns in the last N days
    continue to outperform in the next M days.

    Author: [Your name]
    Creation date: 2026-01-24
    Version: 1.0.0
    """

    def __init__(self, lookback_period=20, holding_period=5):
        """
        Parameters with justified default values:
        - lookback_period=20: ~1 month of trading, standard in literature
        - holding_period=5: 1 week, balance between signal and costs
        """
        self.lookback = lookback_period
        self.holding = holding_period

    def generate_signal(self, prices):
        """Generate entry signal based on momentum."""
        returns = prices.pct_change(self.lookback)
        signal = returns > 0  # Simplified: positive momentum
        return signal

    def calculate_position_size(self, capital, risk_per_trade=0.02):
        """Fixed position sizing: 2% of capital per trade."""
        return capital * risk_per_trade

Version control with Git

Git is not optional. Without version control, you can't reproduce past results or understand what changed when something stopped working.

Documentation your future self will thank you for

Document while creating, not after. In 6 months you won't remember why you used those parameters.

# CHANGELOG.md

## v1.0.0 - 2026-01-24 (Initial version)
- Simple momentum implementation
- Lookback: 20 days (1 trading month)
- Holding: 5 days (1 week)
- No additional filters
- Justification: Test base hypothesis before adding complexity

## v1.1.0 - 2026-02-01 (In development)
- TODO: Add volatility filter if v1.0 shows edge
- Justification: Limit exposure in high volatility periods

Before moving to formal validation, you need to know if the idea has potential. But there's a critical rule: in this creation phase you optimize NOTHING.

What to do in the first test

Rough test: Run a simple backtest with default parameters or standard values from the literature. Don't look for the perfect profit factor, look for signs of life.

Interpreting results

Key formulas to evaluate your first test

Even though you don't optimize in this phase, you need to know what to look at. These three formulas are the essential algorithmic trading metrics for interpreting your first backtest:

Before moving to validation, make sure you avoid these mistakes. They're the most frequent and each one can ruin months of work. Many of these errors manifest as backtesting problems that make your results lie.

• Skip to code without hypothesis: Programming before having a clear idea is random exploration. You find something that "works" in the backtest but fails live because it has no economic logic.
• Optimize before designing: Adjusting parameters (RSI from 14 to 12) before the base logic works is covert HARKing. Guaranteed overfitting.
• Add filters without logic: "It improved the backtest" is not a valid reason. Each filter without economic justification is an overfitting opportunity.
• Ignore the edge: If you can't answer "why should this keep working?", you don't have an edge, just statistical noise.

The golden rule: First CREATE, then VALIDATE. Optimization and filters belong to the validation phase, not creation.

Before moving to the validation phase, make sure you have all of this:

If you meet everything: Your strategy is ready for formal validation.

Next step: Validation

You've completed the creation phase. Now the serious stuff begins: testing whether your strategy really works or is just statistical noise.

Validation includes everything we HAVEN'T done here:

• Rigorous backtesting: With quality market data and professional metrics
• Walk Forward Analysis: Testing on data the strategy never saw
• Monte Carlo simulation: Stress-testing the strategy under thousands of scenarios
• Parameter optimization: Now yes, finding optimal values (without overfitting)
• Paper Trading: Testing in real-time without risking capital
• Drawdown analysis: Understanding and controlling maximum capital declines

Conclusion

Creating a strategy is a structured process, not random inspiration. Start with proven idea sources (academic papers, classic books, anomalies), formulate specific hypotheses, identify the edge you're exploiting, design simple logic, and code with best practices.

The temptation to jump straight to backtesting and optimize is strong. But without a clear hypothesis formulated BEFORE seeing results, any "profitable" strategy is probably statistical noise. Remember: no optimization in the creation phase.

You've learned the complete creation process. Now you have the tools to generate solid ideas, turn them into testable hypotheses, and code them correctly. The next step is validation, where you'll test if your strategy really has an edge.

A simple strategy that works is worth more than a complex one that appears to work.