Quant tradingRTrading strategies
This article continues our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way to manage turnover and not get
FactorsQuant tradingRTime series modellingTrading strategies
Modeling features as expected returns can be a useful way to develop trading strategies, but it requires some care. The main advantage is that it directly aligns with the objective of predicting and capitalising on future returns. This can make optimisation and implementation more intuitive. It also facilitates direct comparison between features and provides a
BacktestingFactorsQuant tradingRTrading strategies
Every time we trade, we incur a cost. We pay a commission to the exchange or broker, we cross spreads, and we might even have market impact to contend with. A common issue in quant trading is to find an edge, only to discover that if you executed it naively, you’d get killed with costs.
FactorsQuant tradingR
In this article, I’ll take some crypto stat arb features from our recent brainstorming article and show you how you might quantify their strength and decay characteristics and then combine them into a trading signal. This article continues our recent articles on stat arb: A short take on stat arb trading in the real world
FactorsTrading strategies
This article continues our recent articles on stat arb: In this article, I’ll brainstorm some ideas for predictive features that you could potentially use in a crypto stat arb model. The ideas draw insights from recent discussions and market observations, but of course, you should do your own research. In future articles, I’ll pick some
Quant tradingTrading strategies
Last week, I wrote a short article about statistical arbitrage trading in the real world. Statistical arbitrage is a well-understood concept: find pairs or baskets of assets you expect to move together, wait for them to diverge, and bet on them converging again. Simple enough. But making it work, especially at scale, is a little
Think like a traderTrading strategies
In textbooks, one often sees pairs trading algorithms start by regressing prices of Asset A on Asset B to calculate a hedge ratio. I’ve rarely seen anyone actually do this in the real world. That’s because it is a very unstable thing – especially for a pair of volatile assets, and especially over a large
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Here in Australia, we’re right in the depths of the silly season. We indulge in long lunches, take days off work, and generally let our hair down. In that spirit, I thought I might have some fun punting shitcoins. (Maybe my definition of fun differs from yours, but let’s run with it). For the uninitiated,
Trading strategies
Let’s talk about carry trades. First, what exactly is a carry trade? A carry trade is a trade that pays you to hold it. A position where, if nothing changes except the passing of time, you expect to make money. Let’s go through some examples. FX carry The classic example is the FX carry trade,
DataTools of the trade
Disclaimer: the links in this post are affiliate links. If you use them to purchase a TradingView subscription, you’ll receive $15 in addition to the massive Black Friday discounts TradingView is currently running. We’ll receive a few dollars, too – so thanks in advance if you purchase using our link. I’m sure you’ve heard of TradingView.
Quant tradingRTrading strategies
The Equal Risk Contribution (ERC) portfolio seeks to maximally diversify portfolio risk by equalising the risk contribution of each component. The intuition is as follows: Imagine we have a 3-asset portfolio Assets 1 and 2 are perfectly correlated (correlation of 1.0) Asset 3 is uncorrelated with the other two (correlation of 0.0) Let’s say we
RTime series modelling
Exponential weighting schemes can help navigate the trade-off between responsiveness and stability of the inherently noisy estimates we make from market data. We previously saw examples of calculating the exponentially weighted moving average of a vector, and estimating the correlation between SPY and TLT using an exponential weighting scheme [link]. In this article, we’ll implement
Think like a trader
Think like a trader
Think like a trader
CryptoData analysisThink like a trader
Think like a trader