Unveiling the World of Analytics in Python Build: Insights and Visualisations

Have you ever wondered what is the secret sauce to creating a high-performing algorithmic trading strategy? It's analytics. Analytics is the guide for creating winning strategies, providing you with critical insights to fine-tune and optimise your trading approach. In this blog post of the series, we'll delve deep into the world of analytics in Python Build by AlgoBulls, shedding light on how you can harness these insights to elevate your trading game.

In case you have not read our previous blogs of this series, make sure to start from “Exploring Trading Strategies with AlgoBulls' Python Build” so you get a much clearer understanding of algorithmic trading using AlgoBulls’ Python Build.

Before we view the Analytics, make sure you have executed a strategy. To do that simply execute a trading strategy by following the steps given in our previous blog “Validating and Configuring your Trading Strategy”.

Once execution is complete, you can see right below your coding section, there are 2 tabs: Analytics and Data. Click on Analytics Tab to view the Charts and Metrics generated to analyse the performance and returns of your strategy.

In case you have not run a Backtest for your strategy yet, you can go to any Ready Templates and check out ready analytics by scrolling down. As an example, check the Analytics of one such Ready Template.

Performance Metrics Demystified

Before we dive into the fascinating world of analytics, let's first understand the performance metrics that Python Build offers to assess your strategy's effectiveness. These metrics are your trusted allies, helping you evaluate and improve your trading strategy.

Total Returns

Total returns represent the cumulative sum of all profits and losses generated by your executed strategy, from its inception to the present day. It's the grand total of your trading journey.

Sharpe Ratio

The Sharpe ratio is a crucial metric in evaluating your strategy's performance relative to the risks it takes. It quantifies the excess return your strategy achieves per unit of risk endured. A higher Sharpe ratio indicates superior risk-adjusted performance.

Sortino Ratio

While the Sharpe ratio looks at overall risk, the Sortino ratio hones in on downside risk. It assesses your strategy's returns concerning downside volatility, focusing solely on negative price movements. A higher Sortino ratio means your strategy excels at mitigating the impact of downside risk.

Volatility

Volatility measures how much your strategy's returns fluctuate from one trade to another. High volatility implies larger fluctuations, signalling both the potential for substantial gains and losses. Low volatility, on the other hand, suggests more stable returns.

Unlocking Visual Insights

Now, let's venture into the realm of visualisations, where we can uncover trends and patterns with remarkable clarity.

Cumulative Returns

This chart paints a picture of how your strategy's returns have evolved over time. Positive values indicate gains, while negatives represent losses. The larger the positive returns, the better. Keep in mind that this chart focuses solely on gains and losses, excluding considerations of risk and timing.

PNL Returns

This chart showcases the profit or loss per trade plotted on a timeline. You can switch between viewing values in percentages or dollars, depending on your preference.

End of Year Returns

This chart offers a bird's-eye view of your strategy's annual performance as a percentage, providing an overview of its overall performance across each year.

Monthly Returns Distribution Histogram

This chart reveals the frequency distribution of returns across consecutive months. This histogram helps you understand the range and patterns in monthly returns, shedding light on your strategy's volatility and performance trends.

Monthly Returns Heatmap

This chart provides a colour-coded view of your strategy's performance over months. Shades of green signify positive returns, while shades of red indicate losses. This visual tool aids in identifying trends and patterns, making it easier to spot strong and weak performance periods.

Daily Returns

This chart displays the percentage change in your strategy's returns between trading days. It offers insights into short-term volatility, trend analysis, and risk assessment.

Underwater Plot

This plot visually represents your strategy's performance concerning its historical peaks. It showcases how much your returns have declined from their highest point over time, helping you understand periods of drawdown and recovery, along with historical risk and resilience.

Heatmaps

We've also included three calendar Heatmaps for Gross ROI, Trading Volume, and Total Number of Trades. These heat maps use colour codes to depict daily aggregated values, offering visual insights into these metrics.

Data

Now that you have completely explored the Analytics tab, you can check out the Data tab as well. Here we have trade by trade data of all the trades, the execution logs of your strategy, and the timeline showing the events that occurred in the strategy. The execution logs display the execution stages for each candle. You can log custom debug prints by using “self.logger.info(<content>)” inside your strategy code.

To wrap things up, we present a comprehensive trade-by-trade history in a user-friendly tabular format. This table provides details on entry and exit trades, prices, timestamps, transaction types, and trade-by-trade profit and loss. You can display these figures in either percentages or dollars.

Exploring Beyond

For those eager to replicate these metrics and charts within a Jupyter Notebook, we've developed a Python package called 'pyalgotrading' on GitHub to assist you in achieving the same.

And for those wondering if you can analyse your own trading strategies on our website, even if executed outside the AlgoBulls platform, we have a feature that will allow you to generate these metrics by simply uploading a CSV file with trade details.

This concludes our in-depth exploration of the analytics provided by AlgoBulls for every strategy. If you haven't read our previous articles in this series, you can check them all out here.

May this wealth of information empower your trading endeavours. Thank you for your time, and remember – happy trading!

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