# What is StratLab?
StratLab is a Python library designed to backtest stock market strategies. The library currently uses the yfinance (Yahoo Finance) API as a means for extracting financial data, which is then manipulated utilizing Pandas dataframes and Numpy functions. There are also options to extract the backtested results directly into excel files.
# How do you install it?
Run the following command in your terminal
```bash
pip install StratLab
```
# How do you use it?
Step 1: Initialize backtest
```python
# This imports the StratLab library and intializes the Backtest.
# The to_excel argument writes an excel file to your desktop with an analysis of the backtest.
import StratLib as sl
bt = sl.Backtest(to_excel=True)
```
Step 2: Add condition for trade
```python
# This example creates a condition in the backtest for when
# ^NDX (Nasdaq 100 Index) price is above its 200D moving average...
bt.add_condition(
name='200 SMA Bullish',
ticker_1='^NDX',
study_1='price',
operator='>',
ticker_2='^NDX',
study_2='sma',
study_2_period=200
)
```
Step 3: Add holding for the condition(s)
```python
# This example tells the backtest to hold ^NDX (Nasdaq 100 Index)
# when the "200 SMA Bullish" condition is True...
bt.add_holding(
conditions=['200 SMA Bullish'],
flags=['True'],
holdings_list=['^NDX']
)
```
Step 4: Run the backtest
```python
bt.run()
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"description": "# What is StratLab?\nStratLab is a Python library designed to backtest stock market strategies. The library currently uses the yfinance (Yahoo Finance) API as a means for extracting financial data, which is then manipulated utilizing Pandas dataframes and Numpy functions. There are also options to extract the backtested results directly into excel files.\n# How do you install it?\nRun the following command in your terminal\n```bash\npip install StratLab\n```\n# How do you use it?\nStep 1: Initialize backtest\n```python\n# This imports the StratLab library and intializes the Backtest.\n# The to_excel argument writes an excel file to your desktop with an analysis of the backtest.\nimport StratLib as sl\n\nbt = sl.Backtest(to_excel=True)\n```\nStep 2: Add condition for trade\n```python\n# This example creates a condition in the backtest for when\n# ^NDX (Nasdaq 100 Index) price is above its 200D moving average...\nbt.add_condition(\n name='200 SMA Bullish',\n ticker_1='^NDX',\n study_1='price',\n operator='>',\n ticker_2='^NDX',\n study_2='sma',\n study_2_period=200\n)\n```\n\nStep 3: Add holding for the condition(s)\n```python\n# This example tells the backtest to hold ^NDX (Nasdaq 100 Index)\n# when the \"200 SMA Bullish\" condition is True...\nbt.add_holding(\n conditions=['200 SMA Bullish'],\n flags=['True'],\n holdings_list=['^NDX']\n)\n```\n\nStep 4: Run the backtest\n```python\nbt.run()\n\n",
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