Metadata-Version: 2.1
Name: mc-sim-fin
Version: 0.1.1b2
Summary: montecarlo simulations/analysis library for finance
Home-page: https://github.com/gaugau3000/montecarlo_simulation_finance
Author: Gautier Pialat
Author-email: g.pialat@gmail.com
License: UNKNOWN
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        # Montecarlo simulations/analysis for finance (equity simulator)
        
        An inspiration of the book [BUILDING WINNING ALGORITHMIC TRADING SYSTEMS](https://www.amazon.com/Building-Winning-Algorithmic-Trading-Systems/dp/1118778987) of 'Kevin J. Davey' (chapter 7 detailed analysis)
        
        What's happened if your trades happened in an other order and you iterate many times to extract statistics ? What are your chances to be ruin ? What's max drawdown you may met ?
        
        Pass the trade results to the library and it will help you to manage the risk.
        
        CAUTION : The simulator include assumption that your trades are independent one of the others (you use a durbin watson statistic from [statsmodels library](https://www.statsmodels.org/dev/generated/statsmodels.stats.stattools.durbin_watson.html) to see that)
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install montecarlo simulation finance.
        
        ```bash
        pip install mc-sim-fin
        ```
        
        ## Usage
        
        For the code example below you have 5000 dollar for trading, you stop trading if you capital go below 4000. Your backtest results show that you bot make one trade per day and alternate a win trade of 200 then a lose trade of 150 during the 2017 year.
        
        By default it simulate 1 year of trading with 10000 iterations (look at the documentation to modify this params).
        
        ```python
        import pandas as pd
        import numpy as np
        from mc_sim_fin.mc import mc_analysis
        
        
        date_results = pd.date_range(start='1/1/2017', end='31/12/2017').tolist()
        profit_results = np.resize([200, -150], 365)
        
        results = pd.DataFrame({'date_results': date_results, 'profit_results': profit_results})
        
        mc_sims_results = mc_analysis(results, start_equity=5000, ruin_equity=4000)
        
        
        print(mc_sims_results)
        
        # print output
        {
        'risk_of_ruin_percent': 0.156,
        'med_max_drawdown_percent': 0.36,
        'med_profit_percent': 1.83,
        'prob_profit_is_positive': 0.9979
        }
        
        ```
        
        Ok so seems like I have 15.6% changes to be ruin, I can expect 36% max drawdown and 183% profit and I have 99.79% change to win money the first year.
        
        ## Documentation
        
        You need more information about how the simulation work? You would like to contribute ?
        
        Look at the [documentation](https://gaugau3000.github.io/mc_sim_fin/)
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
        
Keywords: finance montecarlo simulations backtest risk management
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Topic :: Office/Business :: Financial
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
