Get cache value

Add screening criteria

Screen stocks based on criteria

Select stocks based on factor data

Preprocess text for analysis

Clear all registered strategies

Run a single strategy Args: strategy_name: Name of the strategy to run factor_data: Factor data

Generate response from LLM

Connect to WebSocket and subscribe to symbols

Parse LLM response into TradingInsight

Calculate performance metrics for strategy result

Make predictions with a specific model

Create default insight when LLM fails

处理市场数据

Add volatility filter

Calculate sentiment factors for a symbol

Create a momentum investing screener

Check if key exists

Get model information

Get detailed information about a specific strategy

Run backtest for a single factor

Apply minimum and maximum weight constraints

Calculate composite factor returns

Calculate Information ratio for given factor weights

Calculate Sharpe ratio for given factor weights

Get parameter ranges - to be overridden by subclasses

Process incoming WebSocket messages

Trigger an alert

Add custom filter function

Add market cap filter

Calculate overall market sentiment from multiple results

Create a multi-factor screener

Create a quality investing screener

Create a value investing screener

获取历史数据 :param symbol: 股票代码 (例如: 'AAPL', 'MSFT') :param start_time: 开始时间 :param end_time: 结束时间 :param interval

Getters

Optimize factor weights to maximize objective function

Analyze sentiment using local models

Calculate Sortino ratio for given factor weights

Check if request is within rate limit

Create default market analysis

Create default sentiment factor when calculation fails

Create default strategy recommendation

Create necessary database tables

Generate cache key for endpoint and parameters

Generate sample performance data for demonstration

Initialize local sentiment analysis models

Log optimization result

Analyze market data and generate insights

Analyze sentiment for multiple texts

Analyze sentiment of given text

Answer trading-related questions

Assess portfolio risk using LLM

Calculate portfolio performance metrics

计算RSI指标

Close database connection

Create equity curve chart

Create sample data for demonstration

Export screening results

获取历史K线数据 :param symbol: 交易对 :param interval: K线间隔 :param start_time: 开始时间 :param end_time: 结束时间 :para

Fetch posts from Reddit API

生成示例数据

Generate sample factor and price data for demonstration

Generate trading signals based on factor and price data

Get parameter information for the strategy

Get API performance metrics

Get current portfolio summary

Get information about the current LLM provider

Get usage statistics

Generate portfolio optimization recommendations

Register a strategy class Args: strategy_class: Strategy class to register strategy_name: Optional c

Set strategy parameters

Set price alert

Start real-time data feeds

Stop all real-time data feeds

Analyze drawdown periods

Analyze sentiment of text