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Developing a performance monitoring system for my algorithmic trading system It’s one thing to backtest your signals and forecasts on historical data but it’s a completely different animal in terms of execution. I have written about this here. Another important component of setting up an algorithmic trading system is performance monitoring. Some important questions I had in mind while developing this component, How do I measure my slippage? Commissions paid?

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Crawling data from basketball-reference and espn

As I required some data for my ML assignment, I thought that predicting NBA player salaries based on previous seasons’ game stats would be cool.

Here’re the codes used for crawling the data from basektball-reference and espn.

Codes

/post/img/crawl1.png /post/img/crawl2.png /post/img/crawl3.png

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Designing, Building and Deploying a Fully Automated Algorithmic Trading System As I developed several inter-day trading/ portfolio management algorithms, I also embarked on a journey in parallel to develop a fully automated execution framework that could satisfy my requirements. Previously orders were executed manually after signals are generated automatically. Requirements A relatively slow trading system triggered by an hourly task scheduler during trading hours. I’m using Linux cron jobs for this.

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Volatility targeting could potentially increase risk-adjusted returns for quantitative strategies Note: Man AHL’s framework in this paper is used in the write-up here. Let’s say you researched and managed to find a quantitative strategy that suits your risk-reward preferences. How do you further improve your strategy while managing your risk? A common way in the trend-following and risk parity space is to target risk - also known as volatility targeting.

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Framework for capital allocation In this resource starved world, capital is scarce. Every dollar that you own has its place and deserves to be allocated properly. Currently, I already have a huge chunk of capital tied up in a diversified portfolio levered up to 1.4 times. Portfolio has (& expected to) outperformed/ matched up to market returns with 2 to 3 times lower risk - in terms of standard deviation and drawdown.

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