Translating Ernest Chan Kalman Filter Strategy Matlab and Python Code Into R I’m really intrigued by Ernest Chan’s approach in Quant Trading.
Often in the retail trading space, what ‘gurus’ preach often sounds really dubious. But Ernest Chan is different. He’s sincere, down-to-earth and earnest (meant to be a pun here).
In my first month of deploying algo trading strategies, I focus mainly on mean-reversion strategies - paricularly amongst pairs.
How I Find Country Pairs for Mean Reversion Strategy As mentioned in my previous post here, the first step for a mean reversion strategy is to conduct some background quantitative research.
Step 1 First, I use a pair trading function to loop across 800+ country pairs (created from combination function),
pair_trading = function(stock1, stock2, trade_amount, finance_rates, start_date, end_date, prop_train, enter_z_score, exit_z_score){ ## More codes here ## Return this key_info = list( ticker = c(stock1, stock2), start_date = start_date, trade_table = data_trade, sharpe = c(sharpeRatioTrainset, sharpeRatioTestset), half_life = half_life, profits = data_trade_stats, max_drawdown = c(table.