trading

DAX index and Germany ETF I will keep it short in this post since I espoused on this strategy a couple of times. I discovered another market neutral opportunity this month. And this is based on ratio between Germany DAX index and MSCI based Germany ETF (EWG) Based on backtest, sharpe ratio is close to 1.16. The composition between these 2 indexes are largely similar and any significant deviation shouldn’t persist for long.

UK index and UK ETF I discovered another market neutral opportunity this month. And this is based on ratio between FTSE 100 index and MSCI based UK ETF (EWU) Based on backtest, sharpe ratio is close to 0.9. The composition between these 2 indexes are largely similar and any significant deviation shouldn’t persist for long. The optimal lookback period for the MA component in bollinger band is approximately 40 days.

Market neutral strategy As the negative news pile up (trade wars, slump in economy growths, etc), I sought for market neutral stategies that could perform well in any market environment. An idea that struck me recently is to exploit the pair between Berkshire and SnP 500 ETF. The SnP500 ETF/ Berkshire ratio has been falling over the years - insinuating that Berkshire still outperforms the index in the last couple of years.

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.

In these 2 weeks, I’ll deploy my pair trading algo strategy into my server. I modified the code below from a renowned quant trader, Ernest Chan. The basic idea is to find z-scores through moving average & moving SD of spread. If it’s more than absolute of z-score, I will either short or long the spread depending on the polarity. In the backtesting below (using a pair of silver ETFs as an example), I assumed a hypothetical amount of 10,000 dollars per trade.

I simply adapted the code in this post here (https://www.r-bloggers.com/backtesting-a-simple-stock-trading-strategy/) for the following momentum strategy. This is a momentum based strategy: Long if current day is <50 days of 200 days high. Nil position otherwise. Returns are pretty impressive for Singapore market. That being said, it will be useful to try it with different parameters and different markets! #Inspired by the blog post here–>https://www.r-bloggers.com/backtesting-a-simple-stock-trading-strategy/ #http://etfprophet.com/days-since-200-day-highs/ #Simple momentum strategy rm(list = ls(all = TRUE)) library(quantmod) ## Loading required package: xts ## Loading required package: zoo ## ## Attaching package: 'zoo' ## The following objects are masked from 'package:base': ## ## as.