A lot of PyQuant News subscribers are just getting started with quant finance. Most people I talk to are looking for a structured, step-by-step approach to learning so they don't get overwhelmed or waste their time.

You're in luck.

In today's newsletter, I'm going to show you the step-by-step process for building algorithmic trading strategies.

It's the same one I teach in my best-selling course, ​Getting Started With Python for Quant Finance​.

We're going to take a break from code today and focus on the concepts.

Then, stay tuned for ways you can put what you learn into practice.

Let's go!

The 6 step guide for building trading strategies

The best algorithmic trading processes follow something close to the Scientific Method.

The Scientific Method allows us to start with a hypothesis, collect data, and rigorously test it.

You don't have to strictly follow it, but it's a great guide.

Let's see how each step works.

Step 1: Ask a Question (Define the Trading Problem)

A successful strategy begins with a question: What ​asset mispricing​ exists in the market?

We must continuously generate hypotheses about where market inefficiencies might emerge and test them rigorously.

There are many sources of trading ideas.

Sometimes, the same hypothesis can be expressed through different instruments, such as stocks, options, or futures.

Other opportunities arise in illiquid or mispriced ETFs, where temporary dislocations create arbitrage potential.

Geopolitical shocks and macroeconomic shifts can also introduce inefficiencies, disrupting normal market relationships.

Beyond broad economic factors, specific events like supply and demand imbalances or company announcements can create short-term trading opportunities.

Whether it’s an earnings surprise, regulatory change, or unexpected merger, these events can shift market sentiment and pricing temporarily.

The key is to recognize when these dislocations present actionable trades and develop a systematic approach to capturing the edge.

Step 2: Conduct Background Research

Once we identify a potential market inefficiency, the next step is to conduct thorough background research to validate our hypothesis.

Using tools like pandas, NumPy, and SciPy, we can collect and analyze data, working to disprove our initial assumptions.

Rather than seeking confirmation, our goal is to test whether the inefficiency is real and whether it presents a repeatable trading opportunity.

We need to understand the economic fundamentals driving the inefficiency, whether it stems from supply and demand imbalances, mispriced assets, or macroeconomic shifts.

By grounding our ideas in "economic reality," we can distinguish between random noise and meaningful patterns, improving our chances of finding sustainable edges.

Historical analysis gives us insight into how similar market conditions played out in the past.

We examine price movements, correlations, and event-driven behaviors to see if our hypothesis holds up over time.

If the data supports our idea, we refine and test it further. If not, we adjust our approach or explore new angles.

This iterative process helps us develop robust strategies backed by both intuition and evidence.

Step 3: Formulate a Hypothesis (Define Strategy Rules)

With a market inefficiency in mind, we now define our hypothesis and strategy rules.

Our goal is to create a systematic approach that generates trade signals based on economic rationale.

A strong hypothesis connects market behavior to a repeatable trading opportunity.

For example, refining profitability may influence crude oil demand.

If the crack spread—the difference between crude and refined product prices—widens, refiners become more profitable.

This could drive more crude purchases, pushing oil prices higher.

A strategy based on this relationship would trigger trades when the crack spread expands or contracts.

We want clear, testable rules at this stage.

We define entry and exit conditions, risk management, and validation steps.

This structure keeps our strategy disciplined and adaptable.

As we analyze performance, we refine the approach to improve robustness and reliability.

Step 4: Design and Conduct an Experiment

With our hypothesis and strategy rules defined, we move to backtesting—an important step in evaluating performance.

A backtest simulates past market conditions to see how our strategy would have performed.

This helps us identify strengths, weaknesses, and areas for improvement before risking real capital.

Performance isn’t just about profit and loss: We assess the statistical significance of our results to determine if returns are driven by skill or luck.

We also test the strategy’s sensitivity to parameter changes, ensuring it isn’t overfitted to historical data.

A robust strategy should perform well across different market conditions.

Real-world factors like commissions and slippage can erode profitability.

We account for these costs in our backtest to get a more accurate picture of potential returns.

By refining our approach based on the data, we improve our chances of developing a strategy that holds up in live trading.

Step 5:  Collect and Analyze Data

After running a backtest, we ​collect and analyze​ the data to evaluate our strategy’s performance.

The goal is to assess its statistical significance and understand how it behaves under different conditions.

A strong strategy isn’t just profitable—it should also demonstrate consistency and resilience across various market environments.

We use many risk and performance metrics to gain a deeper understanding.

Transactional-based metrics help us evaluate execution efficiency, including trade frequency, slippage, and commission costs.

Returns-based metrics focus on profitability, measuring factors like Sharpe ratio, drawdowns, and volatility.

Factor-based metrics examine how our strategy reacts to broader market forces, helping us determine whether it exploits a genuine inefficiency or simply tracks existing trends.

By analyzing these metrics, we refine our strategy to improve robustness.

If the results show strong performance with stable risk characteristics, we move forward.

If weaknesses appear, we adjust parameters or revisit our hypothesis to enhance long-term viability.

Step 6: Draw Conclusions (Validate the Strategy)

Once we analyze our backtest results, we need to determine whether the strategy is worth pursuing.

A key question is whether it outperforms a benchmark.

If it doesn’t, we might be better off investing in the benchmark itself rather than deploying a strategy with additional risks and costs.

To validate the strategy, we focus on key performance metrics.

Risk-adjusted returns tell us whether we’re being adequately compensated for the risk taken.

Drawdown duration helps us understand how long the strategy experiences losses before recovering.

Volatility and win rate provide insight into consistency—stable, repeatable performance is preferable to sporadic, high-risk returns.

If the strategy meets our expectations, we refine and prepare for live trading.

If it falls short, we reassess our hypothesis, adjust parameters, or abandon the idea altogether.

The goal is to ensure we deploy strategies with a strong edge and sustainable performance.

Execution

There's a lot of work to do before we even put on a trade.

And this is where most people go wrong: they trade first and post-rationalize their results.

In a follow-up newsletter, we'll talk about the stages of building automated execution.