How to limit Pandas memory usage
Pandas is a powerful library for data analysis in Python. It is widely used in data science and data engineering. However, it can be memory-intensive, especially when working with large datasets. In this post, we will explore how to limit Pandas memory usage and improve it by 80% on an example trading dataset.
Plotting cumulative orderbook in Python
Simple function for plotting cumulative orderbook in Python with Python and Plotly
Quant strategy overfitting intro
In trading, overfitting means that your model or backtest will seem to perform great on historical data, or backtest, but perform poorly on live trading data. This can make you implement a poor trading strategy or allocate more capital to a model than you should. Both are costly mistakes.
Does selling backtests work?
Sometimes we are approached by quants with a great backtest. They either want to sell their strategy or ask us to implement it and give them a share of the profits. (How) does this model work?
Algo-trading resources - books
Starting with algo-trading is hard. As this field is highly competitive and quickly evolving, there are not many quality resources available and those at hand are often of varying quality. This page is a collection of resources that I have found useful.
Algo-trading resources - online
Here are few online resources that I have found useful for algo-trading. Strategy ZOO, analysis mashup, historical hft data provider and podcasts included!
Tips and tricks to avoid overfitting in trading
Overfitting can cost you a lot of time and money. Here are a few trading-related tricks to avoid overfitting that are not known outside the algo-trading community.
Analysing overfitting on individual model features
Overfitting is one of the biggest adversaries of quantitative researchers. Today, we will zoom in into our models and find out which features are useful and which are causing the biggest harm.
Market maker's edge in the market
Many people accuse market makers or HFTs of unfairly benefiting from the market or retail investors. Is this true and what advantages do these market participants have in the cryptocurrency market. And how can other algo-traders compete with them?
Common backtesting problems and how to avoid them
Backtesting is arguably the most important part of quantitative research. Its precision is critical for making decisions in the research process and for money allocation. For certain kinds of strategies, the backtesting precision can be very high and the results trustworthy, other should not be trusted at all. How to distinguish between the two?
Building HFT algos with hftbacktest and Lake
Come back later for: Backtest high-sharpe HFT strategies with hftbacktest integrated with Lake
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