A Quant’s Approach to Drawdown:
The Cold Blood Index

In part 1 of this series, we talked about how a market-savvy systematic trader would approach a period of drawdown in a trading strategy. Specifically, they’d: do the best job possible of designing and building their trading strategy to be robust to a range of future market conditions chill out and let the strategy do …

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A Quant’s Approach to Drawdown: Part 1

Imagine you’ve tinkered for days or even weeks, perfecting a strategy idea that’s showing a whole lot of promise. You’ve meticulously tweaked a mouth-watering Sharpe Ratio out of your backtests….it even survived costs. YES! Systems go, let’s trade it. Imagine this new strategy enters a drawdown.…maybe a lengthy one….maybe from day one! How would you …

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Bond. Treasury Bond

The Federal Reserve publishes the yield-to-maturity of US Treasury bonds. However, the actual returns earned by investors are not publicly available. Nor are they readily and intuitively discerned from historical yields, since “a bond’s return equals its yield only if its yield stays constant and if all coupons (cash payments) are reinvested at that same …

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Backtesting Bias:
Feels Good, Until You Blow Up

In an ideal trading universe (free from backtesting bias), we’d all have a big golden “causation magnifying glass”. Through the lens of this fictional tool, you’d zoom in and understand the fleeting, enigmatic nature of the financial markets, stripping bare all its causes and effects. Knowing exactly what causes exploitable inefficiencies would make predicting market …

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Momentum Is Dead! Long Live Momentum!

In our inaugural Algo Bootcamp, we teamed up with our super-active community of traders and developed a long-only, always-in-the-market strategy for harvesting risk premia. It holds a number of different ETFs, varying their relative weighting on a monthly basis. We’re happy with it. However, the perennial question remains: can we do better? As you might …

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Simulating Variable FX Swaps in Zorro and Python

One of the ongoing research projects inside the Robot Wealth community involves an FX strategy with some multi-week hold periods. Such a strategy can be significantly impacted by the swap, or the cost of financing the position. These costs change over time, and we decided that for the sake of more accurate simulations, we would …

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ETF Rotation Strategies in Zorro

At Robot Wealth we get more questions than even the most sleep-deprived trader can handle. So whilst we develop the algo equivalent of Siri and brag about how we managed to get 6 hours downtime last night, we thought we’d start a new format of blog posts — answering your most burning questions. Lately our …

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Deep Learning for Trading Part 4: Fighting Overfitting with Dropout and Regularization

Deep Learning for Trading Part 4: Fighting Overfitting is the fourth in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Deep Learning for Trading Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques …

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Deep Learning for Trading Part 3: Feed Forward Networks

This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals …

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Neural Network Trading: A Getting Started Guide for Algo Trading

This article is adapted from one of the units of Advanced Algorithmic Trading. If you like what you see, check out the entire curriculum here. Find out what Robot Wealth is all about here. If you’re interested in using artificial neural networks (ANNs) for algorithmic trading, but don’t know where to start, then this article …

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