完成了一版商品期货T型报价的代码

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a quantitative trading strategies repository focused on JoinQuant platform implementations. The codebase contains trading strategies across multiple asset classes including options, futures, ETFs, and stocks, primarily for the Chinese A-share market.
+
+## Architecture & Structure
+
+### High-Level Organization
+- **API/**: Documentation and resources for JoinQuant API usage
+- **Lib/**: Core strategy implementations organized by asset class
+  - **Options/**: Options trading strategies (bull spreads, covered calls, straddles, collars)
+  - **Fund/**: ETF/LOF fund premium/discount strategies
+  - **Future/**: Futures trading strategies (trend-following, breakout, mean-reversion)
+  - **Stock/**: Equity strategies and stock selection algorithms
+  - **Research/**: Data analysis and research notebooks
+  - **Utils/**: Utility functions and data processing tools
+- **Data/**: Sample data files and datasets
+- **Log/**: Strategy execution logs
+- **Resources/**: API documentation and external resources
+
+### Key Dependencies
+- Python 3.11+
+- pandas (data manipulation)
+- matplotlib (visualization)
+- jqdata (JoinQuant data access)
+
+## Common Development Commands
+
+### Setup & Installation
+```bash
+# Install dependencies
+pip install -r requirements.txt
+# or
+uv sync
+
+# Run basic strategy test
+python quick_test.py
+```
+
+### Strategy Development & Testing
+```bash
+# Run a specific strategy
+python Lib/Options/deep_itm_bull_spread_strategy.py
+
+# Analyze strategy performance
+python Lib/utils/read_log.py
+
+# Generate research reports
+jupyter notebook Lib/research/
+```
+
+### Common Operations
+- **Strategy Testing**: Use JoinQuant's backtesting framework
+- **Data Analysis**: Leverage pandas for financial data processing
+- **Visualization**: matplotlib for strategy performance charts
+- **Logging**: Built-in logging to /log/ directory
+
+## Strategy Categories
+
+### Options Strategies
+- **Bull Spreads**: Deep ITM call spreads with systematic roll-over
+- **Covered Calls**: ETF-based covered call strategies with dynamic adjustment
+- **Straddles/Strangles**: Volatility-based strategies for range-bound markets
+- **Collar Strategies**: Downside protection with upside cap
+- **Calendar Spreads**: Time decay arbitrage strategies
+
+### Fund Strategies
+- **Premium/Discount Arbitrage**: ETF/LOF fund discount strategies with dynamic position sizing
+- **Momentum Rotation**: ETF momentum-based sector rotation
+- **Grid Trading**: Systematic grid-based trading on fund premiums
+
+### Futures Strategies
+- **Trend Breakout**: Multi-MA crossover and breakout systems
+- **Spider Web**: Futures positioning-based sentiment analysis
+- **K-line Pattern**: Candlestick pattern recognition with trend filtering
+- **Mean Reversion**: Counter-trend strategies with strict risk controls
+
+### Risk Management Framework
+- **Dynamic Position Sizing**: Position sizing based on volatility and market conditions
+- **Multi-level Stop Loss**: Individual position and portfolio-level stops
+- **Holiday Risk Management**: Automatic position reduction before holidays
+- **Market State Filtering**: Adaptive strategies based on market regime detection
+
+## Key Code Patterns
+
+### Strategy Structure
+Most strategies follow this pattern:
+1. **Initialize**: Set up parameters, universe, and global variables
+2. **Signal Generation**: Technical analysis or fundamental filtering
+3. **Position Management**: Dynamic sizing and risk controls
+4. **Execution**: Order management with slippage and cost considerations
+5. **Monitoring**: Real-time P&L tracking and risk alerts
+
+### Common Parameters
+- `g.max_position`: Maximum number of positions
+- `g.loss_limit`: Stop loss threshold (typically 10%)
+- `g.proportion_cash`: Cash reserve for drawdowns
+- `g.holiday`: Holiday calendar for risk management
+
+### Data Sources
+- **JoinQuant API**: Real-time and historical market data
+- **Fundamental Data**: Financial statements and company metrics
+- **Options Data**: Greeks, implied volatility, and option chains
+- **Futures Data**: Positioning data, volume, and open interest
+
+## File Naming Conventions
+- Strategy files use descriptive names: `asset_class_strategy_type.py`
+- Research notebooks: `analysis_*.ipynb`
+- Test files: `test_*.py` or `*_test.py`
+- Log files: `strategy_name_YYYYMMDD.log`
+
+## Development Guidelines
+
+### Strategy Implementation
+1. Use JoinQuant's API functions (`get_price`, `order_target_value`, etc.)
+2. Implement proper error handling for API failures
+3. Include detailed logging for strategy execution
+4. Follow the existing parameter naming conventions
+
+### Testing Approach
+- Backtest using JoinQuant's framework
+- Validate with out-of-sample data
+- Check edge cases (holidays, limit moves, etc.)
+- Monitor transaction costs and slippage impact
+
+### Code Organization
+- Group related strategies in appropriate directories
+- Use consistent parameter naming across strategies
+- Implement shared utilities in `/Lib/utils/`
+- Document strategies in corresponding README.md files
+
+## Quick Start for New Strategies
+
+1. **Choose Strategy Type**: Options/Futures/Fund/Stock
+2. **Create File**: Follow naming convention in appropriate directory
+3. **Implement Framework**: Use existing strategy templates
+4. **Test Locally**: Use `quick_test.py` for basic validation
+5. **Backtest**: Upload to JoinQuant for full backtesting
+6. **Document**: Add description to relevant README.md
+
+## Important Notes
+- All strategies assume JoinQuant platform API availability
+- Some strategies may have hardcoded parameters for Chinese markets
+- Transaction costs and slippage assumptions vary by strategy
+- Holiday schedules follow Chinese market calendar

Lib/Options/product_T_price.py

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+import pandas as pd
+import numpy as np
+import talib as tb
+from pandas.plotting import  table
+from jqdata import *
+
+# 期权合约信息
+class OptionContract:
+    
+    close: 0 #现价
+    volume: 0 #成交量
+    position: 0 #当前持仓量
+    priceChangePct: 0 #收盘价涨跌幅
+    intrinsic_value: 0 #内在价值
+    time_value: 0 #时间价值
+        
+        
+    def __init__(self,close, volume, position, priceChangePct, intrinsic_value=0, time_value=0):
+        self.close = close
+        self.volume = volume
+        self.position = position
+        self.priceChangePct = priceChangePct
+        self.intrinsic_value = intrinsic_value
+        self.time_value = time_value
+
+
+        
+# 同一行权价的一组期权
+class OptionGroup:
+    
+    exercisePrice: 0 ##行权价
+    call: None ##提醒的品种
+    put: None  ##提醒的类型
+        
+    def __init__(self, exercisePrice, call, put):
+        self.exercisePrice = exercisePrice
+        self.call = call
+        self.put = put
+        
+    def __init__(self, exercisePrice):
+        self.exercisePrice = exercisePrice
+
+#数据内容
+
+CONTENT = [
+    'close', 
+    'volume', 
+    'position', 
+    'priceChangePct', 
+]
+
+
+##数据域
+CONTENT_HEADER_MAP = {
+    'prePosition': '昨日持仓',
+    'close': '现价', 
+    'volume': '成交量', 
+    'position': '持仓量', 
+    'priceChangePct': '涨跌幅'
+}
+
+
+# 豆粕期权
+symbol = 'AU'
+
+starttime =  '2025-06-30'
+endtime = '2025-07-14'
+
+SUBJECT_MATTER = get_dominant_future(symbol,date = starttime)
+print(f"SUBJECT_MATTER: {SUBJECT_MATTER}")
+
+#m2407 匹配 2024-04-19
+EXPIRE_MONTH = '2025-09-24'
+
+## 行权价间隔 比如3800 3750 每50元一档
+gap = 8
+
+# 获取期货价格
+try:
+    # 获取期货价格数据
+    future_price_data = get_price(SUBJECT_MATTER, end_date=endtime, count=1, fields=['close'])
+    current_future_price = float(future_price_data['close'].iloc[-1])
+except Exception as e:
+    print(f"获取期货价格失败: {e}")
+    current_future_price = 781.4  # 使用模拟价格
+
+print(f"标的期货价格: {current_future_price}")
+
+#查询相关的合约,适用于商品
+
+qy = query(opt.OPT_CONTRACT_INFO).filter(
+    opt.OPT_CONTRACT_INFO.underlying_symbol == SUBJECT_MATTER, ##期权标的物
+    opt.OPT_CONTRACT_INFO.expire_date == EXPIRE_MONTH,##期权到期日
+).order_by(opt.OPT_CONTRACT_INFO.exercise_price, opt.OPT_CONTRACT_INFO.contract_type)
+
+optList = opt.run_query(qy)
+
+optList
+
+code = SUBJECT_MATTER.split('.')[0]
+print(f"code: {code}")
+
+# optList.to_csv(f'price_list_{code}.csv')
+
+# 获取实时数据
+optionGroups = {}
+
+for index, row in optList.iterrows():
+    
+    code = row['code'] #key - 期权代码
+#     print(f"检查{code}的数据")
+    #查询具体合约的信息,商品需要去掉开盘前的静态信息
+    realTimeQuery = query(opt.OPT_DAILY_PRICE).filter(
+        opt.OPT_DAILY_PRICE.code==code, 
+        opt.OPT_DAILY_PRICE.date==endtime
+    ).order_by(opt.OPT_DAILY_PRICE.date.desc()).limit(1)
+    realTimeData = opt.run_query(realTimeQuery)
+#     print(f"realTimeQuery: {realTimeQuery}")
+#     print(f"realTimeData: {realTimeData}")
+    
+    #期权基本信息
+    exercisePrice = row['exercise_price'] #行权价
+    contractType = row['contract_type'] #合约类型。CO-认购期权，PO-认沽期权
+    
+    #实时表查询
+    close = realTimeData.loc[0].at['close'] #现价
+    volume = int(realTimeData.loc[0].at['volume']) #成交量
+    position = realTimeData.loc[0].at['position'] #当前持仓量
+    priceChangePct = str(round(realTimeData.loc[0].at['change_pct_close'], 2)) + '%' #收盘价涨跌幅
+        
+    #去除非标准的带A合约
+    if(exercisePrice  % gap != 0):
+        continue
+        
+    # 计算内在价值和时间价值
+    if contractType == 'CO':  # 认购期权
+        intrinsic_value = max(current_future_price - exercisePrice, 0)
+        time_value = close - intrinsic_value
+        print(f"处理认购期权, 期货价格: {current_future_price}, 行权价：{exercisePrice}")
+        print(f"获得内在价值: {intrinsic_value}, 时间价值: {time_value}")
+    else:  # 认沽期权
+        intrinsic_value = max(exercisePrice - current_future_price, 0)
+        time_value = close - intrinsic_value
+        print(f"处理认沽期权, 期货价格: {current_future_price}, 行权价：{exercisePrice}")
+        print(f"获得内在价值: {intrinsic_value}, 时间价值: {time_value}")
+        
+    optionContract = OptionContract(close, volume, position, priceChangePct, intrinsic_value, time_value)
+    
+    if(exercisePrice in optionGroups):
+        
+        if(contractType == 'CO'):
+            optionGroups[exercisePrice].call = optionContract
+        else:
+            optionGroups[exercisePrice].put = optionContract
+            
+    else:
+        
+        optionGroup = OptionGroup(exercisePrice)
+        
+        if(contractType == 'CO'):
+            optionGroup.call = optionContract
+        else:
+            optionGroup.put = optionContract
+            
+        optionGroups[exercisePrice] = optionGroup
+
+# 使用optionGroups构造T型报价表
+data = []
+
+for exercisePrice in sorted(optionGroups.keys()):
+    group = optionGroups[exercisePrice]
+#     print(f"exercisePrice: {exercisePrice}, group: {group}")
+    
+    # 认购期权数据
+    call_price = group.call.close if group.call else None
+    call_volume = group.call.volume if group.call else None
+    call_position = group.call.position if group.call else None
+    call_change = group.call.priceChangePct if group.call else None
+    call_intrinsic = group.call.intrinsic_value if group.call else None
+#     print(f"exercisePrice: {exercisePrice}, call_intrinsic: {call_intrinsic}")
+    call_time = group.call.time_value if group.call else None
+    
+    # 认沽期权数据
+    put_price = group.put.close if group.put else None
+    put_volume = group.put.volume if group.put else None
+    put_position = group.put.position if group.put else None
+    put_change = group.put.priceChangePct if group.put else None
+    put_intrinsic = group.put.intrinsic_value if group.put else None
+    put_time = group.put.time_value if group.put else None
+    
+    data.append({
+        '认购涨跌幅': call_change,
+#         '认购持仓量': call_position,
+#         '认购成交量': call_volume,
+        '认购时间价值': call_time,
+        '认购内在价值': call_intrinsic,
+        '认购价格': call_price,
+        '行权价': exercisePrice,
+        '认沽价格': put_price,
+        '认沽内在价值': put_intrinsic,
+        '认沽时间价值': put_time,
+#         '认沽成交量': put_volume,
+#         '认沽持仓量': put_position,
+        '认沽涨跌幅': put_change
+    })
+
+# 创建T型报价DataFrame
+df = pd.DataFrame(data)
+# 确保df的列顺序为：'认购涨跌幅'，'认购持仓量'，'认购成交量'，'认购时间价值'，'认购内在价值'，'认购价格'，'行权价'，'认沽价格'，'认沽内在价值'，'认沽时间价值'，'认沽成交量'，'认沽持仓量'，'认沽涨跌幅'
+df = df[['认购涨跌幅', '认购时间价值', '认购内在价值', '认购价格', '行权价', '认沽价格', '认沽内在价值', '认沽时间价值', '认沽涨跌幅']]
+
+print("\n" + "="*100)
+print("期权T型报价表（含内在价值和时间价值）")
+print("="*100)
+print(f"标的期货价格: {current_future_price}")
+print("="*100)
+
+# 显示T型报价
+print(df.to_string(index=False))

pyproject.toml

@@ -4,4 +4,7 @@ version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11"
-dependencies = []
+dependencies = [
+    "matplotlib>=3.10.3",
+    "pandas>=2.3.1",
+]

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