Curated by @jan_skoda, founder of crypto market maker LiquidityLabs and former Head of Research at Quantlane (owned by FTMO). All research is done on Crypto Lake historical market data.
Building HFT algos with hftbacktest and Lake
Come back later for: Backtest high-sharpe HFT strategies with hftbacktest integrated with Lake
New posts are announced on RSS, twitter @jan_skoda or @crypto_lake_com, so follow us!
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?
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?
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.
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.
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!
