This blog is curated by @jan_skoda, founder of crypto market making company Liquidity Labs 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!
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!
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.
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?
