Quant tradingRTrading strategiesZorro
In this post, we look at tools and functions from the field of digital signal processing. Can these tools be useful to us as quantitative traders? What’s a Digital Signal? A digital signal is a representation of physical phenomena created by sampling that phenomena at discrete time intervals. If you think about the way we
RTools of the trade
How might we calculate rolling correlations between constituents of an ETF, given a dataframe of prices? For problems like this, the tidyverse really shines. There are a number of ways to solve this problem … read on for our solution, and let us know if you’d approach it differently! First, we load some packages and
PythonRTools of the trade
Modern data science is fundamentally multi-lingual. At a minimum, most data scientists are comfortable working in R, Python and SQL; many add Java and/or Scala to their toolkit, and it’s not uncommon to also know one’s way around JavaScript. Personally, I prefer to use R for data analysis. But, until recently, I’d tend to reach
RThink like a traderTrading strategiesZorro
Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick (GRQ) trading strategy. Of course, they don’t work. Deep down even the greenest of newbies knows this. Yet, still, we are compelled to give them a try, just once, just for fun (or so we
OptionsQuant tradingR
For simulating stock prices, Geometric Brownian Motion (GBM) is the de-facto go-to model. It has some nice properties which are generally consistent with stock prices, such as being log-normally distributed (and hence bounded to the downside by zero), and that expected returns don’t depend on the magnitude of price. Of course, GBM is just a
BacktestingQuant tradingZorro
Optimisation tools have a knack for seducing systematic traders. And what’s not to love? Find me the unique set of parameters that delivered the greatest return in my ten-year backtest. And do it in under five seconds. That’s certainly attractive. But do you want to hear something controversial? When it comes to the parameters of
BacktestingQuant tradingTrading as a businessTrading infrastructureZorro
One of the keys to running a successful systematic trading business is a relentless focus on high return-on-investment activities. High ROI activities include: Implementing new trading strategies within a proven framework. An example might be to implement a portfolio of pairs trades in the equity market. Scaling existing strategies to new instruments or markets. For
BacktestingFactorsQuant tradingRTrading strategies
To say we’re living through extraordinary times would be an understatement. We saw the best part of 40% wiped off stock indexes in a matter of weeks, unprecedented co-ordinated central bank intervention on a global scale, and an unfolding health crisis that for many has already turned into a tragedy. As an investor or trader,
Quant tradingTime series modellingTrading strategies
What is Vector Autoregression The vector autoregression (VAR) framework is common in econometrics for modelling correlated variables with bi-directional relationships and feedback loops. If you google “vector autoregression” you’ll find all sorts of academic papers related to modelling the effects of monetary and fiscal policy on various aspects of the economy. This is only of
Machine learningRTools of the trade
Way back in November 2007, literally weeks after SPX put in its pre-GFC all-time high, Friedman, Hastie and Tibshirani published their Graphical Lasso algorithm for estimation of the sparse inverse covariance matrix. Are you suggesting that Friedman and his titans of statistical learning somehow caused the GFC by publishing their Graphical Lasso algorithm? Not at
BacktestingQuant tradingRTrading strategiesZorro
In the first three posts of this mini-series on pairs trading with Zorro and R, we: Implemented a Kalman filter in R Implemented a simple pairs trading algorithm in Zorro Connected Zorro and R and exchanged data between the two platforms In this fourth and final post, we’re going to put it all together and
BacktestingQuant tradingRZorro
In the last two posts, we implemented a Kalman filter in R for calculating a dynamic hedge ratio, and presented a Zorro script for backtesting and trading price-based spreads using a static hedge ratio. The goal is to get the best of both worlds and use our dynamic hedge ratio within the Zorro script. Rather
Think like a trader
Think like a trader
Think like a trader
Trading strategies
Trading strategies