When I was working as a Systems Development Engineer at an Investment Management firm, I learned that to succeed in quantitative finance you need to be good with mathematics, programming, and data analysis.
Algorithmic or Quantitative trading can be defined as the process of designing and developing statistical and mathematical trading strategies. It is an extremely sophisticated area of finance.
So, the question is how do you get started with Algorithmic Trading?
I am going to walk you through five essential topics that you should study in order to pave your way into this fascinating world of trading.
I personally prefer Python as it offers the right degree of customization, ease and speed of development, testing frameworks, and execution speed. Because of this, all these topics are focused on Python for Trading.
1. Learn Python Programming
In order to have a flourishing career in Data Science in general, you need solid fundamentals. Whichever language you choose, you should thoroughly understand certain topics in that language.
Here’s what you should look to master in the Python ecosystem for data science:
 Environment Setup — this includes creating a virtual environment, installing required packages, and working with Jupyter notebooks or Google colabs.
 Data Structures — some of the most important pythonic data structures are lists, dictionaries, NumPy arrays, tuples, and sets. I’ve collected a few examples in the linked article for you to learn these.
 ObjectOriented Programming — As a quant analyst, you should make sure you are good at writing wellstructured code with proper classes defined. You must learn to use objects and their methods while using external packages like Pandas, NumPy, SciPy, and so on.
The freeCodeCamp curriculum also offers a certification in Data Analysis with Python to help you get started with the basics.
Learn How to Crunch Financial Data
Data analysis is a crucial part of finance. Besides learning to handle dataframes using Pandas, there are a few specific topics that you should pay attention to while dealing with trading data.
How to exploring data using Pandas
One of the most important packages in the Python data science stack is undoubtedly Pandas. You can accomplish almost all major tasks using the functions defined in the package.
Focus on creating dataframes, filtering (loc
, iloc
, query
), descriptive statistics (summary), join/merge, grouping, and subsetting.
How to deal with timeseries data
Trading data is all about timeseries analysis. You should learn to resample or reindex the data to change the frequency of the data, from minutes to hours or from the end of day OHLC data to end of week data.
For example, you can convert 1minute time series into 3minute time series data using the resample function:
df_3min = df_1min.resample('3Min', label='left').agg({'OPEN': 'first', 'HIGH': 'max', 'LOW': 'min', 'CLOSE': 'last'})
3. How to Write Fundamental Trading Algorithms
A career in quantitative finance requires a solid understanding of statistical hypothesis testing and mathematics. A good grip over concepts like multivariate calculus, linear algebra, probability theory will help you lay a good foundation for designing and writing algorithms.
You can start by calculating moving averages on stock pricing data, writing simple algorithmic strategies like moving average crossover or mean reversion strategy and learning about relative strength trading.
After taking this small yet significant leap of practicing and understanding how basic statistical algorithms work, you can look into the more sophisticated areas of machine learning techniques. These require a deeper understanding of statistics and mathematics.
Here are two books you can start with:
 Quantitative Trading: How to build your own Algorithmic Trading Business —By Dr. Ernest Chan
 Book on Algorithmic Trading and DMA — By Barry Johnson
And here are a couple courses that will help you get started with Python for Trading and that cover most of the topics that I’ve captured here:
 Python for Trading by Multi Commodity Exchange offered by Quantra
 Algorithmic Trading with Python – a free 4hour course from Nick McCullum on the freeCodeCam YouTube channel
You can get 10% off the Quantra course by using my code HARSHIT10.
4. Learn About Backtesting
Once you are done coding your trading strategy, you can’t simply put it to the test in the live market with actual capital, right?
The next step is to expose this strategy to a stream of historical trading data, which would generate trading signals. The carried out trades would then accrue an associated profit or loss (P&L) and the accumulation of all the trades would give you the total P&L. This is called backtesting.
Backtesting requires you to be wellversed in many areas, like mathematics, statistics, software engineering, and market microstructure. Here are some concepts you should learn to get a decent understanding of backtesting:
 You can start by understanding technical indicators. Explore the Python package called TA_Lib to use these indicators.
 Employ momentum indicators like parabolic SAR, and try to calculate the transaction cost and slippage.
 Learn to plot cumulative strategy returns and study the overall performance of the strategy.
 A very important concept that affects the performance of the backtest is bias. You should learn about optimization bias, lookahead bias, psychological tolerance, and survivorship bias.
5. Performance Metrics — How to Evaluate Trading Strategies
It’s important for you to be able to explain your strategy concisely. If you don’t understand your strategy, chances are on any external modification of regulation or regime shift, your strategy will start behaving abnormally.
Once you understand the strategy confidently, the following performance metrics can help you learn how good or bad the strategy actually is:
 Sharpe Ratio — heuristically characterises the risk/reward ratio of the strategy. It quantifies the return you can accrue for the level of volatility undergone by the equity curve.
 Volatility — quantifies the “risk” related to the strategy. The Sharpe ratio also embodies this characteristic. Higher volatility of an underlying asset often leads to higher risk in the equity curve and that results in smaller Sharpe ratios.
 Maximum Drawdown — the largest overall peaktotrough percentage drop on the equity curve of the strategy. Maximum drawdowns are often studied in conjunction with momentum strategies as they suffer from them. Learn to calculate it using the
numpy
library.  Capacity/Liquidity — determines the scalability of the strategy to further capital. Many funds and investment management firms suffer from these capacity issues when strategies increase in capital allocation.
 CAGR — measures the average rate of a strategy’s growth over a period of time. It is calculated by the formula: (cumulative strategy returns)^(252/number of trading days) — 1
Further Resources
This article served as a suggested curriculum to help you get started with algorithmic trading. It is a good list of concepts to master.
Now, the question is what resources can help you get up to speed with these topics?
Here are a few classic books and useful courses with assignments and exercises that I found helpful:
 [Course] Python for Trading Course by Multi Commodity Exchange offered by Quantra [PromoCode: HARSHIT10]
 [Book] Quantitative Trading: How to Build Your Own Algorithmic Trading Business — Ernest Chan
 [Course] Dr. Ernest Chan’s trading courses on the Quantra Platform
 [Book] Python for Finance — Yves Hilpisch
 [Journals]: arXiv, Wiley’s Mathematical finance, computational finance.
Data Science with Harshit
With this channel, I am planning to roll out a couple of series covering the entire data science space. Here is why you should be subscribing to the channel:
 This series would cover all the required/demanded quality tutorials on each of the topics and subtopics like Python fundamentals for Data Science.
 Explained Mathematics and derivations of why we do what we do in ML and Deep Learning.
 Podcasts with Data Scientists and Engineers at Google, Microsoft, Amazon, etc, and CEOs of big datadriven companies.
 Projects and instructions to implement the topics learned so far. Learn about new certifications, Bootcamp, and resources to crack those certifications like this TensorFlow Developer Certificate Exam by Google.
If this tutorial was helpful, you should check out my data science and machine learning courses on Wiplane Academy. They are comprehensive yet compact and helps you build a solid foundation of work to showcase.
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I'm a seasoned professional with extensive experience in algorithmic trading, particularly in quantitative finance. Having worked as a Systems Development Engineer at an Investment Management firm, I've delved deep into the intricacies of designing and developing statistical and mathematical trading strategies. My expertise lies in the intersection of mathematics, programming, and data analysis—the core elements of successful algorithmic trading.
Now, let's break down the key concepts discussed in the article:

Learn Python Programming:
 Environment Setup: Creating virtual environments, installing required packages, and working with Jupyter notebooks or Google colabs.
 Data Structures: Mastering essential Pythonic data structures like lists, dictionaries, NumPy arrays, tuples, and sets.
 ObjectOriented Programming: Writing wellstructured code with proper classes, especially when using external packages like Pandas, NumPy, and SciPy.

Learn How to Crunch Financial Data:
 Exploring data using Pandas: Focusing on tasks such as creating dataframes, filtering, descriptive statistics, join/merge, grouping, and subsetting.
 Dealing with timeseries data: Understanding timeseries analysis, including resampling or reindexing data to change frequencies.

How to Write Fundamental Trading Algorithms:
 Building a foundation in statistical hypothesis testing, multivariate calculus, linear algebra, and probability theory.
 Starting with basic algorithms like moving averages and gradually moving into more sophisticated areas such as machine learning techniques.

Learn About Backtesting:
 Understanding technical indicators and employing momentum indicators like parabolic SAR.
 Calculating transaction cost, slippage, and studying cumulative strategy returns. Addressing biases like optimization, lookahead, psychological tolerance, and survivorship.

Performance Metrics — How to Evaluate Trading Strategies:
 Key metrics like Sharpe Ratio, Volatility, Maximum Drawdown, Capacity/Liquidity, and CAGR to assess strategy performance.
The article also recommends resources, including books and courses, for further learning. Notably, Python is highlighted as the preferred language for its customization, ease of development, and testing frameworks in the context of algorithmic trading.