FinLab 選股策略產生器-stock selection and backtesting tool
AI-Powered Stock Selection and Backtesting
來個月營收策略吧！
週換股策略更厲害！
我其實喜歡巴菲特選股策略！
台股的高報酬選股奧秘？！
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Introduction to FinLab 選股策略產生器
FinLab 選股策略產生器 is a powerful stock selection and backtesting platform designed for quantitative traders and analysts. It provides comprehensive tools for retrieving financial data, creating custom trading strategies, and running backtests to evaluate their performance. The platform supports various financial datasets, including daily stock prices, monthly revenues, and quarterly fundamental indicators. For example, you can use the platform to download the daily closing prices of stocks, calculate moving averages, and create a strategy that buys stocks when they are trading above their 60-day moving average.
Main Functions of FinLab 選股策略產生器
Data Retrieval
ExampleUsing data.get('price:收盤價') to retrieve daily closing prices of stocks.
ScenarioA user wants to analyze the historical performance of a stock. They can retrieve its daily closing prices, calculate moving averages, and visualize the trend over time.
Backtesting
ExampleUsing backtest.sim(position, resample='W') to backtest a strategy with weekly rebalancing.
ScenarioA trader develops a momentum strategy that buys stocks with high relative strength. They can backtest this strategy to evaluate its historical performance and optimize the parameters.
Indicator Calculation
ExampleCalculating the RSI using data.indicator('RSI', timeperiod=14).
ScenarioAn analyst wants to create a trading strategy based on the Relative Strength Index (RSI). They can calculate the RSI for their stock universe and create buy/sell signals based on the indicator.
Ideal Users of FinLab 選股策略產生器
Quantitative Traders
Quantitative traders benefit from the platform's ability to automate the retrieval of financial data and the backtesting of trading strategies. They can develop complex models and evaluate their performance using historical data.
Financial Analysts
Financial analysts can use the platform to analyze the financial health of companies, calculate key financial ratios, and identify potential investment opportunities. The platform's comprehensive dataset and analytical tools make it easier to perform in-depth analysis.
How to Use FinLab 選股策略產生器
Visit aichatonline.org for a free trial without login, also no need for ChatGPT Plus.
Start your journey by visiting the website for a free trial. No login or premium account is required to begin exploring the capabilities of the tool.
Set up your environment
Install the necessary Python packages and import them. Use `from finlab import data, backtest` to access financial data and backtesting tools.
Retrieve financial data
Utilize `data.get('dataset_name')` to fetch various datasets such as stock prices or financial statements. Make sure you understand the frequency and format of the data.
Create your stock selection strategy
Combine financial indicators and conditions to form a stock selection strategy. Use logical operations to filter stocks based on your criteria.
Backtest your strategy
Simulate your strategy with `backtest.sim(position, resample='W')` and review the performance. Adjust your strategy based on the results and optimize for better outcomes.
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- Data Analysis
- Financial Modeling
- Backtesting
- Investment Strategies
- Stock Selection
Frequently Asked Questions about FinLab 選股策略產生器
What kind of data can I retrieve using FinLab 選股策略產生器?
You can retrieve various datasets including daily stock prices, monthly revenues, and quarterly financial statements. These datasets are essential for creating and backtesting investment strategies.
How do I calculate indicators like moving averages?
You can calculate moving averages using pandas functions. For example, use `close.rolling(10).mean()` to compute the 10-day moving average of stock closing prices.
Can I combine multiple conditions in my stock selection strategy?
Yes, you can combine multiple conditions using logical operators. For instance, `condition1 & condition2` will only select stocks that meet both conditions.
How do I perform a backtest with my strategy?
After defining your position DataFrame, use `backtest.sim(position, resample='W')` to simulate your strategy and assess its performance over time.
Is it possible to simulate different rebalancing frequencies?
Yes, you can specify the rebalancing frequency in the `sim` function using the `resample` parameter, which can be set to 'D', 'W', 'M', 'Q', etc.