Free Download Successful Algorithmic Trading By Mike Halls-Moore
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Struggling To Make Profitable Systematic Trading Strategies?
You never intended to lose money while trading, yet a series of small mistakes often led to strategies that looked strong in backtests but failed once live.
With over a decade in algorithmic trading, we’ve seen many of the common pitfalls traders face.
Through years of trial and error, we discovered that success in quantitative trading comes from persistence, structure, and treating the process scientifically.
In Successful Algorithmic Trading, you’ll learn a step-by-step framework for spotting profitable strategies early, running meaningful backtests, cutting costs, and executing trades seamlessly in a fully automated setup.
No matter your level of experience, these methods can help you build a profitable algorithmic trading business.
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300+ pages of quantitative trading insights
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How to build a complete equities backtesting system with Python
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Instant PDF ebook download
Designing profitable strategies isn’t easy—it’s extremely challenging.
Despite the benefits, it’s important to recognize that algo trading is not a shortcut to fast wealth. But by breaking down the process into manageable parts, improving steadily, and committing to daily progress, you can build a system that works.
Early on, consistent profits may feel out of reach. But once you adopt a strategy pipeline—constantly generating fresh ideas—you’ll never be reliant on just one method. If one setup underperforms, you’ll have plenty of alternatives ready to test.
Progress in research, testing, and execution—made slowly and consistently—is the true path to trading profitability.
Commit to disciplined work on your strategy components, and you’ll see results far sooner than you might expect.
What if you’re not an expert at algorithmic trading?
Truth is, neither were we when starting out. We didn’t understand order types, the difference between buy-side and sell-side, or even what a stop loss meant. Over eight years of practice, we’ve learned the essential lessons of quantitative trading step by step.
If we could learn, so can you. We’re not claiming to be the absolute best, but we have successfully built profitable systems and want to guide you through the same journey.
Chances are, you’re already knowledgeable in another field—algorithmic trading is no different. Expertise is gained with patience, repetition, and structured effort. Building reliable strategies follows the same formula.
Every profitable quant we know started out with very little knowledge of the markets. Use your beginner mindset as motivation to push harder and build real skill.
What Topics Are Included In The Book?
Strategy Research
Learn how to generate and evaluate trading ideas that fit into your portfolio.
Securities Master Databases
Discover how to create a secure, reliable database for storing pricing data.
Successful Backtesting
Apply the scientific method to thoroughly backtest every trading concept.
Performance Measurement
Evaluate strategies using professional-grade metrics and benchmarks.
Statistical Testing
Use time series analysis to detect patterns like mean reversion and momentum.
Mean-Reversion Strategies
Explore proven mean-reverting setups for both equities and futures.
Risk Management
Understand portfolio-grade risk control methods such as Variance-at-Risk (VaR).
Position Sizing
Learn about advanced money management methods like the Kelly Criterion.
Execution Systems
Build and launch an automated execution system to handle live trades.
What Technical Skills Will You Learn?
Python Scientific Tools
Get hands-on with Python’s core scientific libraries—NumPy, SciPy, pandas, scikit-learn, and IPython.
Historical Data
Collect and clean market data from both free and premium providers, including equities and continuous futures contracts.
Backtesting Research
Backtest with pandas to measure Sharpe ratio, drawdowns, and win/loss performance.
Parameter Optimisation
Run sensitivity analyses to fine-tune strategy parameters and visualize results with matplotlib and IPython.
Advanced Trading Strategies
Experiment with predictive models, intraday pair-trading, regressions, ensembles, and SVMs using scikit-learn.
Strategy Execution
Integrate with the Interactive Brokers API via Python, account for transaction costs, and incorporate them into your performance stats.
