Algorithmic Trading Code Examples

Welcome to this repository where you'll find the code scripts of the QuantInsti Blog articles

Introduction

This folder contains different Python and R scripts you can use to learn how to trade in the financial markets algorithmically. These code scripts come from the QuantInsti Blog where we publish content related to algorithmic trading. Usually, we have for each Blog, a code script where we develop a trading strategy, a model algorithm, a new technique in the algo trading space, etc. This repository serves the purpose of finding all those code scripts in a single place. Please enjoy the most of it by searching your specific topic, find the code and tweak as per your own trading needs.

Disclaimer

The code scripts and/or their strategies are just templates. You should only use them for live trading with appropriate backtesting and tweaking strategy parameters.

1. Cautionary note

   • Trading is not appropriate for all investors and carries a significant risk.
   • Markets are unpredictable, and past performance does not guarantee future outcomes.
   • The code scripts and the strategies/techniques/models provided here are for educational purposes only; they are not investing advice.

2. Limitations and assumptions

   • Trading experience: The code scripts and the strategies/techniques/models provided here assume that traders possess the necessary expertise to apprehend the risks and customize the templates according to their risk tolerance and preferences.
   • Risk capital: Trading should only be done with risk capital, and only people with enough of it should consider trading. It is not advisable to trade with capital that can affect one's way of life or one's financial commitments. Market volatility: The code scripts and the strategies/techniques/models provided here are contingent upon the market's state and may not yield anticipated results during periods of high market volatility or atypical occurrences.
   • No promises: Trading losses are possible, and neither success nor profit are certain.

3. Additional notes

   • Before implementing any code script and the strategies/techniques/models provided here, you must conduct independent research and due diligence.
   • Trading involves emotions, and managing your emotions and risk tolerance is crucial.

4. Accountability

   • By utilizing these code scripts and the strategies/techniques/models detailed in this repository, you agree that:
     • You have read and understood the disclaimer and points 1, 2 and 3.
     • You assume full accountability for your trading and investing decisions.
     • You alone will bear responsibility for any losses incurred if you employ this trading setup and approach.

Table of contents

1. Contributors
2. Ask for help
3. Available trading setups

Contributors

QuantInsti's EPAT and Quantra content teams are responsible for maintaining and contributing to this repository.

Ask for help

In case of questions, please write to:

• Your support manager (if you’re a present EPAT student)
• The alumni team (if you’re a past EPAT student and an alumnus)
• QuantInsti coordinates you, see on our “Contact Us” page: https://www.quantinsti.com/contact-us

Available Blog code scripts

1. TGAN synthetic data for trading
2. The Risk-Constrained Kelly Criterion
3. A novel drift detection algorithm for machine learning in trading
4. The Boruta-Shap Algorithm: A CPU-and-GPU version
5. Directional Change in Trading: Indicators, Python Coding, and HMM Strategies
6. Faster Downloads using Python Multithreading
7. Ito's Lemma Applied to Stock Trading
8. Trading using GPU-based RAPIDS Libraries from Nvidia
9. A time-varying-parameter vector autoregression model with stochastic volatility
10. The Triple Barrier Method: A Python GPU-based computation
11. Five Indicators To Build Trend-Following Strategies
12. Modelling Asymmetric Volatility with the GJR-GARCH Framework
13. Forecasting Stock Prices Using ARIMA Model
14. Portfolio Optimization Using Monte Carlo Simulation
15. Building a Trading Strategy using Bias-Variance Decomposition
16. Beginner's Guide to Machine Learning Classification in Python
17. RSI Indicator: Calculation, Python Implementation and Trading Strategy
18. Building Blocks of Bias-Variance Tradeoff for Trading the Financial Markets