Hi all, this is the second part to the “Trading Strategy Analysis using Python and the FFN Package” post (the first part can be found here).
Last time we went over the use of the “PerformanceStats” object in ffn, whereas this time I want to concentrate on the “GroupStats” object. The former is for use with single series of data, whereas the latter can accept a full DataFrame with multiple columns representing multiple price/equity series simultaneously.
In this post I will be reviewing and running through examples of using the brilliant python module, “ffn – Financial Functions for Python“, which has been created by Philippe Morissette and released on the MIT license. The github page can be found here (http://pmorissette.github.io/ffn/index.html) The module helps quickly carry out analysis of trading strategies and financial asset price series/history. It…
For this post, I will be creating a script to download pricing data for the S&P 500 stocks, calculate their historic returns and volatility and then proceed to use the K-Means clustering algorithm to divide the stocks into distinct groups based upon said returns and volatilities. So why would we want to do this you ask? Well dividing stocks into groups…
Hi all, this post is going to be a relatively short and to the point run through of creating an annotated heatmap for the Dow 30 stock returns using the Python Seaborn package. Let’s start with what is a heatmap actually is; it’s defined as “a representation of data in the form of a map or diagram in which data…
I thought for this post I would just continue on with the theme of testing trading strategies based on signals from some of the classic “technical indicators” that many traders incorporate into their decision making; the last post dealt with Bollinger Bands and for this one I thought I’d go for a Stochastic Oscillator Trading Strategy Backtest in Python. Let’s…
So, after a long time without posting (been super busy), I thought I’d write a quick Bollinger Band Trading Strategy Backtest in Python and then run some optimisations and analysis much like we have done in the past. It’s pretty easy and can be written in just a few lines of code, which is why I love Python so much…
Carrying on from the last post which outlined an intra-day mean reversion stock trading strategy, I just wanted to expand on that by adapting the backtest to allow short selling too. So as well as buying stocks that have gapped down, we will be allowing the strategy to short sell stocks that have gapped up. I was interested as to…
After completing the series on creating an inter-day mean reversion strategy, I thought it may be an idea to visit another mean reversion strategy, but one that works on an intra-day scale. That is, we will be looking for the mean reversion to take place within one trading day. Stock prices tend to follow geometric random walks, as we are…
Staying on the same topic of optimisation that we visited in the last post concerning portfolio holdings and efficient frontiers/portfolio theory, I thought I would quickly revisit the moving average crossover strategy we built a few posts ago; the previous article can be found here. Optimisation of Moving Average Crossover Trading Strategy In Python In that post we built a…
In this post I’ll be looking at investment portfolio optimisation with python, the fundamental concept of diversification and the creation of an efficient frontier that can be used by investors to choose specific mixes of assets based on investment goals; that is, the trade off between their desired level of portfolio return vs their desired level of portfolio risk.