quant

Trend following strategy in futures using non binary forecasts Project motivation As David Ricardo, a British economist in the 19th century once said, ‘cut short your losses and let your profits trend’ allude to the point that trend following as a profitable strategy could exist even back then. Having read AQR’s papers on the Time Series Momentum (TSMOM, I am keen to explore this topic in the futures space (Moskowitz, T.

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?

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.

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.

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.

Volatility targeting Currently, I’ve a suite of toolkits integrated into my Jarvis that advises me on the investing decisions that I’ve to make on a daily basis. On the latest feature I cobbled together on a Saturday evening, 2 weeks ago, I’ve decided to measure the volatility of my portfolio formally. Why I’m doing this is because managing risks in the form of volatility is easier than targeting returns.