Introduction
Building a trading algorithm can seem like an incredibly advanced task, you might even think that this is something only programmers and mathematicians do. It's 2024, advancements in technology, data accessibility, and analytical tools make creating a trading algorithm more simple and easier than ever before, even for individual retail traders.
In this guide, we’ll break down the 6 steps you need to know to build a successful trading algorithm from scratch.
Understanding Trading Algorithms
A trading algorithm is a program that executes trades automatically based on a set of predetermined rules. Algorithms use financial and technical data, combined with mathematical models, to identify patterns and opportunities in the markets. They are particularly useful because they can trade at high speeds and accuracy, making split-second decisions without the hesitation or bias of human traders.
Common benefits of algorithmic trading include:
Precision and Speed: Algorithms can execute trades within milliseconds.
Elimination of Emotional Bias: Trades are based on statistics and data, not on impulse or fear.
Backtesting: Algorithms allow you to test strategies on historical data multiple decades before risking capital.
Step 1: Find Inspiration and Choose a Strategy
Every great trading algorithm begins with an idea. Start by looking at graphs and identify trends or patterns that spark your interest. Inspiration can come from:
Market Observations: Study historical price charts and look for recurring patterns. Are there consistent behaviours during specific times of the day, week, or year?
Successful Traders: Research strategies used by successful algorithmic traders. Whether it’s mean reversion, momentum trading, or arbitrage, exploring what have worked for others might give you some a really good idea of what could work for you.
Don't wanna spend hours to find good blogs, books, podcasts and forums?
Check out my list of FREE Resources for learning Systematic Trading.
Once inspired, brainstorm strategies that fit your goals. Ask yourself:
What is my trading objective? Are you looking to capitalize on short-term price movements, or do you prefer longer-term trends? Are you more interested in the rotational style of trading or more of a entry/exit style.
What assets will I trade? Will you focus on stocks/indices, forex, cryptocurrencies, or commodities? The choice of assets will choose the direction of your research.
What kind of data will I use? Will your trading algorithm be based on technical indicators, price action patterns, or time-based/seasonal parameters?
Step 2: How To Choose Programming Language?
Selecting the right programming language is a crucial step in building your trading algorithm. The choice largely depends on your skill level, the complexity of your strategy, and the resources you have available.
For Beginners: Start Simple
If you’re new to coding or algorithmic trading, consider platforms that provide user-friendly interfaces and pre-built tools. These options allow you to focus on finding ideas and inspiration for new strategies instead of spending too much time on technical details:
MetaTrader 5 (MT5): Ideal for forex and stock trading, both Futures and CFDs can be traded. MT5 provides a built-in scripting language (MQL5) that’s easy to learn and use. It has a great community with plenty of users worldwide for you to learn from, it also has a freelancer section where you can pay skilled programmers for a translation of your strategies and don't want to learn programming right away.
ProRealTime (PRT): Known for its intuitive interface, PRT is perfect for creating simple algorithms with minimal coding knowledge. It also offers backtesting and real-time market data integration.
Sponsored Tip: Brokers like EightCap offer beginner-friendly platforms like MT5 and TradingView to kickstart your algorithmic trading journey. With Eightcap you can backtest, trade and host multiple trading algorithms with a complete and simple solution.
Step 3: How to Choose the Right Entries, Filters, and Exits?
Entry and Exit
Deciding when to enter and exit trades is the foundation of any algorithmic strategy. Here’s how to define these parameters:
Entry Criteria: Determine the exact conditions to enter a trade. For example:
Use technical indicators like moving averages, RSI (Relative Strength Index), or MACD (Moving Average Convergence Divergence) to identify potential entries.
Use price action patterns like after 3 bullish days or when the recent close is higher than the day before.
Want to see some examples? Check out 12 Essential Entries for Mean Reversion Strategies.
Exit Criteria: Decide when to take profits or cut losses. Some examples include:
Setting a fixed profit target or percentage gain.
Stop-loss orders to limit losses if the market moves against your position.
Trailing stops to lock in profits as the trade moves in your favor.
Want to see some examples? Check out 13 Essential Exits for Mean Reversion Strategies.
When Not to Trade
One of the most overlooked aspects of algorithmic trading is knowing when not to trade. Market conditions aren’t always favorable, and trading during the wrong times can delete profits. Consider the following filters:
Low Volatility Periods: Avoid trading when market volatility is unusually low, as price movements may not provide sufficient opportunities for profit.
High-Impact News Events: Unless specifically designed for news trading, avoid trading during major events like central bank announcements, earnings reports, or geopolitical crises. These can cause unpredictable market swings.
Overbought or Oversold Markets: Some algorithms are ineffective in extreme market conditions. Implementing filters to avoid trades when indicators like RSI show overbought or oversold signals can reduce unnecessary risk.
Want to see some examples? Check out 11 Trend Filters to Improve Your Trading.
Step 4: Check Robustness and Backtest Your Algorithm
Keep It Simple: Fewer Optimizations, Less Risk of Overfitting
Over-optimizing your algorithm to perform perfectly on historical data can lead to overfitting, where the strategy only works in past conditions and fails in live markets.
To avoid this:
Limit the number of parameters in your strategy to keep it straightforward and simple.
Focus on creating a robust algorithm that performs reasonably well across different datasets rather than excelling in one specific scenario.
A simpler algorithm often translates to better real-world performance because it’s less reliant on perfect conditions.
Parameter Sensitivity
To test the robustness of your algorithm, analyze how sensitive its performance is to changes in key parameters. For example:
Adjust inputs like moving average lengths, RSI thresholds, or stop-loss levels slightly higher or lower. You can use optimization tools to test the parameters but you should use this to choose the most robust plateau of values NOT the best performing one.
Evaluate whether small changes drastically affect profitability or if the algorithm remains steady.
A robust strategy should produce consistent results across a range of parameter values, indicating it’s not overly dependent on precise settings.
Monte Carlo Simulation
Monte Carlo simulations add an extra layer of robustness testing by introducing randomness into your backtesting results. This method simulates various market conditions, helping you understand how your algorithm might perform under different scenarios. Steps include:
Randomizing trade orders, market conditions, or start dates within your backtest.
Analyzing performance across multiple simulations to identify the range of potential outcomes.
Monte Carlo simulations help identify the risk of extreme losses and ensure your strategy is prepared for unpredictable market fluctuations.
Remove Top 5-15% of Trades
To gauge the reliability of your algorithm, try removing the top 5-15% of its most profitable trades during backtesting. This step shows how much your strategy depends on a few lucky trades to achieve its overall performance. If the algorithm still produces solid results after removing these trades, it indicates a more stable and reliable strategy.
By keeping your algorithm simple, testing for parameter sensitivity, using Monte Carlo simulations, and identifying reliance on outliers, you can build a strategy that stands up to the unpredictable nature of live markets. A robust and well-tested algorithm is the key to long-term trading success.
Step 5: Test Your Algorithm in Real-Time with Paper Trading
Before deploying your trading algorithm in live markets, it’s crucial to test it in real-time using a demo account. This step allows you to observe how your strategy performs in current market conditions without risking actual capital. I do this with all of my strategies before deploying them on the live account.
Why Paper Trading?
Paper trading simulates live market conditions, giving you a risk-free way to evaluate your algorithm’s behaviour. Spend at least 6-12 months paper trading to ensure the strategy remains consistent and adapts well to various market scenarios, including market volatility, trends, and potential slippage. This time frame provides a comprehensive understanding of how your algorithm reacts to real-world dynamics.
Account for Fees and Commissions
One limitation of paper trading is that it often doesn’t include fees, commissions, or spreads in the results. These costs can significantly impact profitability, especially for short-term strategies with frequent trades.
Sponsored Tip: Platforms like MetaTrader 5 (MT5) are designed to help you account for these costs during testing. MT5 offers realistic simulations that include fees and commissions, giving you a more accurate view of your algorithm’s true profitability before you go live. You can try out MT5 by creating an EightCap account below.
Step 6: Deploy and Monitor Your Algorithm
Diversify with Multiple Trading Algorithms
The most effective way to optimize performance and manage risk is by running multiple trading algorithms simultaneously. Each algorithm can target different strategies, markets, or time frames, allowing you to diversify your portfolio and reduce dependence on a single system. For example:
Run one algorithm focused on catching the longer-term swings in the bond market and another on swing trading equities.
Deploy algorithms with different time frames or trading styles to balance exposure to various market conditions.
Diversification through multiple algorithms spreads risk and increases the likelihood of consistent profitability.
Conclusion
Building a trading algorithm from scratch in 2024 is an exciting and potentially profitable journey. With the right tools, a clear process, and the right mindset, you can become very successful with algotrading. Start small, learn from each step, and refine your process over time to gain a competitive edge in the world of trading.
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