Often, certain projects or classes involving python require a set of modules/packages for the code to work.
1 solution is to create a Python Environment dedicated to that project.
First set up a folder, and include a .yml file with the specific modules and environment that you wish to install. Here is an example (env.yml),
name: env channels: !!python/tuple - !!python/unicode 'defaults' dependencies: - nb_conda=2.2.0=py27_0 - python=2.7.13=0 - cycler=0.10.0 - functools32=3.
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Translating Ernest Chan Kalman Filter Strategy Matlab and Python Code Into R I’m really intrigued by Ernest Chan’s approach in Quant Trading.
Often in the retail trading space, what ‘gurus’ preach often sounds really dubious. But Ernest Chan is different. He’s sincere, down-to-earth and earnest (meant to be a pun here).
In my first month of deploying algo trading strategies, I focus mainly on mean-reversion strategies - paricularly amongst pairs.
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How I Find Country Pairs for Mean Reversion Strategy As mentioned in my previous post here, the first step for a mean reversion strategy is to conduct some background quantitative research.
Step 1 First, I use a pair trading function to loop across 800+ country pairs (created from combination function),
pair_trading = function(stock1, stock2, trade_amount, finance_rates, start_date, end_date, prop_train, enter_z_score, exit_z_score){ ## More codes here ## Return this key_info = list( ticker = c(stock1, stock2), start_date = start_date, trade_table = data_trade, sharpe = c(sharpeRatioTrainset, sharpeRatioTestset), half_life = half_life, profits = data_trade_stats, max_drawdown = c(table.
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Research to Production Pipeline for Mean Reversion
Here is a high level overview of something that I’m working on.
I’ve been grappling with the finite state automata Event Driven Computing transitions and I kinda sorted it out for production use.
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In these 2 weeks, I’ll deploy my pair trading algo strategy into my server.
I modified the code below from a renowned quant trader, Ernest Chan. The basic idea is to find z-scores through moving average & moving SD of spread. If it’s more than absolute of z-score, I will either short or long the spread depending on the polarity.
In the backtesting below (using a pair of silver ETFs as an example), I assumed a hypothetical amount of 10,000 dollars per trade.
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Colorization The following is a high level project pipeline of my Computational Photography Colorization report. The project scope involves minimizing a quadratic cost function. An artist would only need to make a few colour scribble on a grey photograph and the algorithm will automatically populate the entire photograph with the associated colours.
1.Input: I first read in the image using imread function.
2.Find the difference: Next I compute the difference between the marked and grey scale image.
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Snippet of my Seam Carving Report from my Msc Computer Science Georgia Tech’s Computational Photography module Besides removing of streams, we can also add streams. We identify k streams for removal and duplicate by averaging the left and right neighbours. The computation of these averages is done by convolving the following matrix with the images’ colour channels.
kernel = np.array([[0, 0, 0], [0.5, 0, 0.5], [0, 0, 0]]) In the implementation of my scaling_up algorithm, I first remove k streams (depending on ratio set by user) and recorded the coordinates and cumulative energy values of the original picture in each removal.
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Decomposing a Position Into Exchange Rate and Non Exchange Rate Effects If you are someone with a stake in foreign positions, this package I wrote here may be a useful tool to help you understand the impact of foreign currency on your positions. For instance,
If you are an investor, you may use it to analyze impact of exchange rate on your investment positions. If you are in the treasury department, you may wish to analyze the impact of exchange rates on your bonds.
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