Metadata-Version: 2.1
Name: stock-backtest
Version: 0.1.1
Summary: stock-backtest is a python library         for stock technical analysis backtest on Python 3.7 and above.
Home-page: https://github.com/10mohi6/stock-backtest-python
Author: 10mohi6
Author-email: 10.mohi.6.y@gmail.com
License: MIT
Description: # stock-backtest
        
        [![PyPI](https://img.shields.io/pypi/v/stock-backtest)](https://pypi.org/project/stock-backtest/)
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        [![codecov](https://codecov.io/gh/10mohi6/stock-backtest-python/branch/main/graph/badge.svg?token=ODOV9LETK1)](https://codecov.io/gh/10mohi6/stock-backtest-python)
        [![Build Status](https://travis-ci.com/10mohi6/stock-backtest-python.svg?branch=main)](https://travis-ci.com/10mohi6/stock-backtest-python)
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/stock-backtest)](https://pypi.org/project/stock-backtest/)
        [![Downloads](https://pepy.tech/badge/stock-backtest)](https://pepy.tech/project/stock-backtest)
        
        stock-backtest is a python library for stock technical analysis backtest on Python 3.7 and above.
        
        
        ## Installation
        
            $ pip install stock-backtest
        
        ## Usage
        
        ### basic run
        ```python
        from stock_backtest import Backtest
        
        class MyBacktest(Backtest):
            def strategy(self):
                fast_ma = self.sma(period=5)
                slow_ma = self.sma(period=25)
                # golden cross
                self.sell_exit = self.buy_entry = (fast_ma > slow_ma) & (
                    fast_ma.shift() <= slow_ma.shift()
                )
                # dead cross
                self.buy_exit = self.sell_entry = (fast_ma < slow_ma) & (
                    fast_ma.shift() >= slow_ma.shift()
                )
        
        MyBacktest("AAPL").run()
        ```
        ![AAPL--.png](https://raw.githubusercontent.com/10mohi6/stock-backtest-python/main/tests/AAPL--.png)
        
        ### advanced run
        ```python
        from stock_backtest import Backtest
        from pprint import pprint
        
        class MyBacktest(Backtest):
            def strategy(self):
                rsi = self.rsi(period=10)
                ema = self.ema(period=20)
                atr = self.atr(period=20)
                lower = ema - atr
                upper = ema + atr
                self.buy_entry = (rsi < 30) & (self.df.C < lower)
                self.sell_entry = (rsi > 70) & (self.df.C > upper)
                self.sell_exit = ema > self.df.C
                self.buy_exit = ema < self.df.C
        
        bt = MyBacktest(
            "AAPL",  # ticker
            shares=100,  # number of shares (default=1)
            start="2010-01-01",  # start date (default="")
            end="2020-01-01",  # end date (default="")
            data_dir="data",  # data directory (default=.)
        )
        pprint(bt.run(), sort_dicts=False)
        ```
        ```python
        {'total profit': -2779.465,
         'total trades': 102,
         'win rate': 66.667,
         'profit factor': 0.641,
         'maximum drawdown': 3147.5,
         'recovery factor': -0.883,
         'riskreward ratio': 0.321,
         'sharpe ratio': -0.085,
         'average return': -68.929,
         'stop loss': 0,
         'take profit': 0}
        ```
        ![AAPL-2010-01-01-2020-01-01.png](https://raw.githubusercontent.com/10mohi6/stock-backtest-python/main/tests/AAPL-2010-01-01-2020-01-01.png)
        
        
        ## Supported indicators
        - Simple Moving Average 'sma'
        - Exponential Moving Average 'ema'
        - Moving Average Convergence Divergence 'macd'
        - Relative Strenght Index 'rsi'
        - Bollinger Bands 'bbands'
        - Stochastic Oscillator 'stoch'
        - Average True Range 'atr'
        
        ## Strategy examples
        ### MACD
        ```python
        class MyBacktest(Backtest):
            def strategy(self):
                macd, signal = self.macd(fast_period=12, slow_period=26, signal_period=9)
                self.sell_exit = self.buy_entry = (macd > signal) & (
                    macd.shift() <= signal.shift()
                )
                self.buy_exit = self.sell_entry = (macd < signal) & (
                    macd.shift() >= signal.shift()
                )
        ```
        ### Bollinger Bands
        ```python
        class MyBacktest(Backtest):
            def strategy(self):
                upper, mid, lower = self.bbands(period=20, band=2)
                self.sell_exit = self.buy_entry = (upper > self.df.C) & (
                    upper.shift() <= self.df.C.shift()
                )
                self.buy_exit = self.sell_entry = (lower < self.df.C) & (
                    lower.shift() >= self.df.C.shift()
                )
        ```
        ### Stochastic
        ```python
        class MyBacktest(Backtest):
            def strategy(self):
                k, d = self.stoch(k_period=5, d_period=3)
                self.sell_exit = self.buy_entry = (
                    (k > 20) & (d > 20) & (k.shift() <= 20) & (d.shift() <= 20)
                )
                self.buy_exit = self.sell_entry = (
                    (k < 80) & (d < 80) & (k.shift() >= 80) & (d.shift() >= 80)
                )
        ```
        ### Moving average divergence rate
        ```python
        class MyBacktest(Backtest):
            def strategy(self):
                sma = self.sma(period=20)
                ratio = (self.df.C - sma) / sma * 100
                self.sell_exit = self.buy_entry = ratio > -5 & (ratio.shift() <= -5)
                self.buy_exit = self.sell_entry = ratio < 5 & (ratio.shift() >= 5)
        ```
        ### Momentum
        ```python
        class MyBacktest(Backtest):
            def strategy(self):
                mom = self.df.C - self.df.C.shift(10)
                self.sell_exit = self.buy_entry = mom > 0 & (mom.shift() <= 0)
                self.buy_exit = self.sell_entry = mom < 0 & (mom.shift() >= 0)
        ```
        ### Donchian Channels
        ```python
        class MyBacktest(Backtest):
            def strategy(self):
                high = self.df.H.rolling(20).max()
                low = self.df.L.rolling(20).min()
                self.sell_exit = self.buy_entry = (high > self.df.C) & (
                    high.shift() <= self.df.C
                )
                self.buy_exit = self.sell_entry = (low < self.df.C) & (
                    low.shift() >= self.df.C
                )
        ```
        ### Relative Vigor Index
        ```python
        class MyBacktest(Backtest):
            def rvi(
                self, *, period: int = 10, price: str = "C"
            ) -> Tuple[pd.DataFrame, pd.DataFrame]:
                co = self.df.C - self.df.O
                n = (co + 2 * co.shift(1) + 2 * co.shift(2) + co.shift(3)) / 6
                hl = self.df.H - self.df.L
                d = (hl + 2 * hl.shift(1) + 2 * hl.shift(2) + hl.shift(3)) / 6
                rvi = n.rolling(period).mean() / d.rolling(period).mean()
                signal = (rvi + 2 * rvi.shift(1) + 2 * rvi.shift(2) + rvi.shift(3)) / 6
                return rvi, signal
        
            def strategy(self):
                rvi, signal = self.rvi(period=5)
                self.sell_exit = self.buy_entry = (rvi > signal) & (
                    rvi.shift() <= signal.shift()
                )
                self.buy_exit = self.sell_entry = (rvi < signal) & (
                    rvi.shift() >= signal.shift()
                )
        ```
        
Keywords: stock python backtest technical analysis trading strategy
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Operating System :: OS Independent
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
