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Learn about some commonly used Python libraries in quantitative finance, such as: * **zipline**: A library for backtesting and implementing trading algorithms, installation command: pip install zipline. * **Quantlib**: A library for pricing financial instruments and performing financial calculations, installation command: pip install Quantlib. * **TA-Lib**: A library for technical analysis, installation command: pip install TA-Lib. * **pyfolio**: A library for evaluating portfolio performance, which can be integrated with backtesting tools like zipline to provide tools for analyzing portfolio returns, risks, etc., installation command: pip install pyfolio. * **statsmodels**: A library for estimating statistical models, including linear regression, time series analysis, etc. In quantitative finance, it can be used to build and test trading strategies, installation command: pip install statsmodels. * * * ## zipline zipline is an open-source framework for quantitative finance research and algorithmic trading. It was developed by Quantopian to provide researchers and developers with a convenient tool for building, testing, and executing quantitative trading strategies. Before using zipline, please make sure you have installed the library. You can install it using the following command: conda install -c conda-forge zipline Here we use (#) to install zipline to avoid strange issues later. Next, we log in to the (https://data.nasdaq.com/account/api) official website, register, and obtain the api key: [https://data.nasdaq.com/account/profile](https://data.nasdaq.com/account/profile). Then set the api key and download the data bundle. The specific commands are as follows: set QUANDL_API_KEY=your_key For macOS systems, use the following command: export QUANDL_API_KEY=your_key-zZQN Download the data bundle: zipline ingest -b quandl Query the data bundle: # zipline bundles csvdir quandl 2023-12-09 06:02:03.178299 quandl 2023-12-09 05:59:04.273082 quandl 2023-12-09 05:54:57.277732 quandl 2023-12-09 05:52:15.532504 quandl 2023-12-09 03:32:03.853032 quantopian-quandl Now, let's use zipline for a simple test. Here is a simple Zipline strategy script for backtesting stock trading: ## Example from zipline.api import order, record,symbol def initialize(context): pass def handle_data(context, data): order(symbol('AAPL'),10) record(AAPL=data.current(symbol('AAPL'),'price')) The above is a simple strategy that buys 10 shares of Apple stock at the current price every trading day, and records the current price of AAPL for each trading day. * `order(symbol('AAPL'), 10)`: This line means that on each trading day, 10 shares of Apple Inc. (AAPL) stock are purchased at the current price. `symbol('AAPL')` is used to get the stock symbol for AAPL. * `record(AAPL=data.current(symbol('AAPL'), 'price'))`: This line means recording the current price of AAPL for each trading day. `data.current(symbol('AAPL'), 'price')` is used to get the current stock price of AAPL. Then execute the following command: # zipline run -f my_strategy.py --start 2016-1-1 --end 2018-1-1 -o buyapple_out.pickle --no-benchmarkSimulated 503 trading days first open: 2016-01-04 14:30:00+00:00 last close: 2017-12-29 21:00:00+00:00 After successful execution, a **buyapple_out.pickle** file will be generated, which we can read using the **pickle** module. **Command Explanation:** * **`zipline run`:** Launch Zipline to run the backtest. * **`-f my_strategy.py`:** Specify the strategy file. In this example, `my_strategy.py` is the Python file containing your written strategy. * **`--start 2016-1-1` and `--end 2018-1-1`:** Specify the start and end dates for the backtest. In this example, the backtest time range is from January 1, 2016 to January 1, 2018. * **`-o buyapple_out.pickle`:** Specify the name of the output file. In this example, the backtest results will be saved as a `buyapple_out.pickle` file. This file contains various output information from the backtest, such as trading records, performance metrics, etc. * **`--no-benchmark`:** Disable the benchmark. In backtesting, a benchmark is sometimes used to compare the performance of a strategy. Using the `--no-benchmark` option means no benchmark will be used. The pickle module is used for serializing and deserializing objects, making it convenient to save objects to files or load objects from files. The following demonstrates how to read the contents of the buyapple_out.pickle file: ## Example import pickle # Specify pickle file path pickle_file_path ='buyapple_out.pickle' # Read pickle file with open(pickle_file_path,'rb')as file: buyapple_out_data =pickle.load(file) # Print read data print(buyapple_out_data) The output content is as follows: period_open period_close short_value pnl long_exposure ... max_leverage excess_return treasury_period_return trading_days period_label 2016-01-04 21:00:00+00:00 2016-01-04 14:31:00+00:00 2016-01-04 21:00:00+00:00 0.0 0.00000 0.0 ... 0.000000 0.0 0.0 1 2016-012016-01-05 21:00:00+00:00 2016-01-05 14:31:00+00:00 2016-01-05 21:00:00+00:00