[Draft] Minimum Profit Condition Optimization - Cointegration Approach #7
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# Description Implementations in this module are based on the book by Ernest P. Chan **"Algorithmic Trading: Winning Strategies and Their Rationale"**. Implemented features are: - Johansen cointegration test - Engle-Granger cointegration test - Half-life of mean reversion function - Linear trading strategy - Bollinger Bands trading strategy ## Type of change - [x] New feature (non-breaking change which adds functionality) - [x] This change requires a documentation update # How Has This Been Tested? This unit tests file was created to test the added functions: - [x] test_mean_reversion.py **Test Configuration**: * Windows 10 * PyCharm # Checklist: - [x] My code follows the style guidelines of this project - [x] I have performed a self-review of my own code - [x] I have commented my code, particularly in hard-to-understand areas - [x] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [x] Any dependent changes have been merged and published in downstream modules
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minimum_profit/MinimumProfit.py
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| import warnings | |||
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We should add a copyright note to every code and unit test file.
minimum_profit/MinimumProfit.py
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| import warnings | |||
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Also, these files should be inside the arbitragelab folder.
We will solve the questions regarding naming during the call.
minimum_profit/MinimumProfit.py
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| import numpy as np | ||
| import pandas as pd | ||
| import statsmodels.api as sm | ||
| from mlfinlab.statistical_arbitrage import JohansenPortfolio, EngleGrangerPortfolio |
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A reminder to later redirect this to functions from the ArbitrageLab.
minimum_profit/MinimumProfit.py
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| Build a pd.DataFrame of both series for cointegration error calculation. | ||
| Initialize a built-in trade book with position, entry price, P&L, and number of trades information. |
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We can try to simplify this by requiring a pd.DataFrame as an input format.
minimum_profit/MinimumProfit.py
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| :param price_s1: (pd.Series or np.array) Share S1 price series | ||
| :param price_s2: (pd.Series or np.array) Share S2 price series | ||
| :param s1_name: (str) Share S1 name | ||
| :param s2_name: (str) Share S2 name |
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This is the right style for variable descriptions, but please add fulls stops at the end.
minimum_profit/MinimumProfit.py
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| if capital_req <= dollar_constraint: | ||
| # Record the entry price. | ||
| self._entry_price = current_price | ||
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| # Do we open a U-trade or L-trade? | ||
| if trade_df_with_cond['otc_U'].iloc[i]: | ||
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| # U-trade, short share S1, long share S2 | ||
| self._position = position * np.array([-1, 1]) | ||
| current_trade = 1 | ||
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| elif allow_ltrade and trade_df_with_cond['otc_L'].iloc[i]: | ||
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| # L-trade, long share S1, short share S2 | ||
| self._position = position * np.array([1, -1]) | ||
| current_trade = -1 | ||
| else: | ||
| # No opening condition met, forward to next day | ||
| continue |
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We'll have to refactor this to make it more compact and readable. I'll help.
minimum_profit/MinimumProfit.py
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| Methods: | ||
| train_test_split(date_cutoff, num_cutoff): A method that splits the price data into a training set and | ||
| a test set according to the cutoff. In-sample simulation can be done when both cutoffs are set to None. | ||
| fit(train_df): Derive the cointegration coefficient, cointegration error, AR(1) cofficient and the fitted | ||
| residual of the AR(1) process. | ||
| optimize(ar_coeff, epsilon_t, ar_resid, horizon, granularity): | ||
| Optimize the upper bound for U-trade by optimizing minimum trade profit | ||
| trade(self, trade_df, upper_bound, minimum_profit, beta, epsilon_t, **kwarg): | ||
| Simulate trades applying the optimal upper bound and lower bound for U-trade and L-trade, respectively. |
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Not sure if we'll keep this style for internal methods. But it's ok if you write them for now as:
Methods:
- train_...
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- fit ...
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| "ar_coeff": 0.1, | ||
| "white_noise_var": 0.5, | ||
| "constant_trend": 13. |
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We should later make these input parameters instead of hardcoded ones.
| Setter for price simulation parameters | ||
| :param param: (str) Parameter dictionary key | ||
| :param value: (float) Parameter value | ||
| :return: |
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Returns can be removed if it's a setter method.
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| :return: | ||
| (np.array, np.array, np.array) Price series of share S1, price series of share S2, and cointegration error. |
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Please don't move the variable description to the next line like this. It should be:
:return: (np.array, np.array, np.array) Price series of share S1, price series of share S2,
and cointegration error.
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This PR was moved to PR#19. |
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Description
Implementations in this module are based on the following paper:
Lin, Y.-X., McCrae, M., and Gulati, C. (2006). Loss protection in pairs trading through minimum profit bounds: A cointegration approach.Journal of Applied Mathematics and Decision Sciences, 2006(1):1–14.
Puspaningrum, H., Lin, Y.-X., and Gulati, C. M. (2010). Finding the optimal pre-set boundaries for pairs trading strategy based on cointegration technique. Journal of Statistical Theory and Practice, 4(3):391–419.
Implemented features are:
Dependency:
Type of change
How Has This Been Tested?
This unit tests file was created to test the added functions:
Test Configuration:
Checklist: