feat: indicator parameterization support for kline chart
This commit is contained in:
TIANHE
@@ -306,6 +306,48 @@ def delete_indicator():
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return jsonify({"code": 0, "msg": str(e), "data": None}), 500
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@indicator_bp.route("/getIndicatorParams", methods=["GET"])
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@login_required
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def get_indicator_params():
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"""
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获取指标的参数声明
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用于前端在策略创建时显示可配置的参数表单。
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Query params:
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indicator_id: 指标ID
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Returns:
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params: [
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{
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"name": "ma_fast",
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"type": "int",
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"default": 5,
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"description": "短期均线周期"
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},
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...
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]
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"""
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try:
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from app.services.indicator_params import get_indicator_params as get_params
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indicator_id = request.args.get("indicator_id")
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if not indicator_id:
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return jsonify({"code": 0, "msg": "indicator_id is required", "data": None}), 400
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try:
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indicator_id = int(indicator_id)
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except ValueError:
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return jsonify({"code": 0, "msg": "indicator_id must be an integer", "data": None}), 400
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params = get_params(indicator_id)
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return jsonify({"code": 1, "msg": "success", "data": params})
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except Exception as e:
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logger.error(f"get_indicator_params failed: {str(e)}", exc_info=True)
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return jsonify({"code": 0, "msg": str(e), "data": None}), 500
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@indicator_bp.route("/verifyCode", methods=["POST"])
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@login_required
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def verify_code():
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@@ -11,6 +11,7 @@ import numpy as np
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from app.data_sources import DataSourceFactory
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from app.utils.logger import get_logger
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from app.services.indicator_params import IndicatorParamsParser, IndicatorCaller
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logger = get_logger(__name__)
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@@ -1114,6 +1115,21 @@ class BacktestService:
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local_vars['commission'] = backtest_params.get('commission', 0.0002)
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local_vars['trade_direction'] = backtest_params.get('trade_direction', 'both')
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# === 指标参数支持 ===
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# 从 backtest_params 获取用户设置的指标参数
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user_indicator_params = (backtest_params or {}).get('indicator_params', {})
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# 解析指标代码中声明的参数
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declared_params = IndicatorParamsParser.parse_params(code)
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# 合并参数（用户值优先，否则使用默认值）
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merged_params = IndicatorParamsParser.merge_params(declared_params, user_indicator_params)
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local_vars['params'] = merged_params
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# === 指标调用器支持 ===
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user_id = (backtest_params or {}).get('user_id', 1)
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indicator_id = (backtest_params or {}).get('indicator_id')
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indicator_caller = IndicatorCaller(user_id, indicator_id)
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local_vars['call_indicator'] = indicator_caller.call_indicator
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# Add technical indicator functions
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local_vars.update(self._get_indicator_functions())
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@@ -0,0 +1,295 @@
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"""
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Indicator Parameters Parser and Helper Functions
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支持两个核心功能：
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1. 指标参数外部传递 - 解析指标代码中的 @param 声明
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2. 指标调用其他指标 - 提供 call_indicator() 函数
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参数声明格式：
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# @param param_name type default_value 描述
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# @param ma_fast int 5 短期均线周期
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# @param ma_slow int 20 长期均线周期
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# @param threshold float 0.5 阈值
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支持的类型：int, float, bool, str
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"""
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import re
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import json
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from typing import Dict, Any, List, Optional, Tuple
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from app.utils.logger import get_logger
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from app.utils.db import get_db_connection
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logger = get_logger(__name__)
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class IndicatorParamsParser:
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"""解析指标代码中的参数声明"""
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# 参数声明正则：# @param name type default description
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PARAM_PATTERN = re.compile(
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r'#\s*@param\s+(\w+)\s+(int|float|bool|str|string)\s+(\S+)\s*(.*)',
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re.IGNORECASE
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)
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@classmethod
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def parse_params(cls, indicator_code: str) -> List[Dict[str, Any]]:
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"""
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解析指标代码中的参数声明
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Returns:
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List of param definitions:
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[
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{
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"name": "ma_fast",
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"type": "int",
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"default": 5,
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"description": "短期均线周期"
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},
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...
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]
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"""
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params = []
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if not indicator_code:
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return params
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for line in indicator_code.split('\n'):
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line = line.strip()
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match = cls.PARAM_PATTERN.match(line)
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if match:
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name = match.group(1)
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param_type = match.group(2).lower()
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default_str = match.group(3)
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description = match.group(4).strip() if match.group(4) else ''
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# 转换默认值类型
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default = cls._convert_value(default_str, param_type)
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# 规范化类型名
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if param_type == 'string':
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param_type = 'str'
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params.append({
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"name": name,
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"type": param_type,
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"default": default,
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"description": description
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})
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return params
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@classmethod
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def _convert_value(cls, value_str: str, param_type: str) -> Any:
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"""转换字符串值为对应类型"""
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try:
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param_type = param_type.lower()
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if param_type == 'int':
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return int(value_str)
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elif param_type == 'float':
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return float(value_str)
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elif param_type == 'bool':
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return value_str.lower() in ('true', '1', 'yes', 'on')
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else: # str/string
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return value_str
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except (ValueError, TypeError):
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return value_str
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@classmethod
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def merge_params(cls, declared_params: List[Dict], user_params: Dict[str, Any]) -> Dict[str, Any]:
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"""
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合并声明的参数和用户提供的参数
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Args:
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declared_params: 从代码中解析的参数声明
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user_params: 用户提供的参数值
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Returns:
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合并后的参数字典（使用用户值或默认值）
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"""
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result = {}
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for param in declared_params:
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name = param['name']
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param_type = param['type']
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default = param['default']
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if name in user_params:
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# 用户提供了值，转换为正确类型
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result[name] = cls._convert_value(str(user_params[name]), param_type)
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else:
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# 使用默认值
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result[name] = default
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return result
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class IndicatorCaller:
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"""
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指标调用器 - 允许一个指标调用另一个指标
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使用方式（在指标代码中）：
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# 按ID调用
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rsi_df = call_indicator(5, df)
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# 按名称调用（自己的指标）
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macd_df = call_indicator('My MACD', df)
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"""
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# 最大调用深度，防止循环依赖
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MAX_CALL_DEPTH = 5
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def __init__(self, user_id: int, current_indicator_id: int = None):
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self.user_id = user_id
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self.current_indicator_id = current_indicator_id
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self._call_stack = [] # 调用栈，用于检测循环依赖
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def call_indicator(
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self,
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indicator_ref: Any, # int (ID) 或 str (名称)
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df: 'pd.DataFrame',
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params: Dict[str, Any] = None,
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_depth: int = 0
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) -> Optional['pd.DataFrame']:
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"""
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调用另一个指标并返回结果
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Args:
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indicator_ref: 指标ID或名称
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df: 输入的K线数据
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params: 传递给被调用指标的参数
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_depth: 内部使用，跟踪调用深度
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Returns:
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执行后的DataFrame，包含被调用指标计算的列
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"""
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import pandas as pd
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import numpy as np
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# 检查调用深度
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if _depth >= self.MAX_CALL_DEPTH:
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logger.error(f"Indicator call depth exceeded {self.MAX_CALL_DEPTH}")
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return df.copy()
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# 获取指标代码
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indicator_code, indicator_id = self._get_indicator_code(indicator_ref)
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if not indicator_code:
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logger.warning(f"Indicator not found: {indicator_ref}")
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return df.copy()
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# 检查循环依赖
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if indicator_id in self._call_stack:
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logger.error(f"Circular dependency detected: {self._call_stack} -> {indicator_id}")
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return df.copy()
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self._call_stack.append(indicator_id)
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try:
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# 解析并合并参数
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declared_params = IndicatorParamsParser.parse_params(indicator_code)
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merged_params = IndicatorParamsParser.merge_params(declared_params, params or {})
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# 准备执行环境
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df_copy = df.copy()
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local_vars = {
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'df': df_copy,
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'open': df_copy['open'].astype('float64') if 'open' in df_copy.columns else pd.Series(dtype='float64'),
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'high': df_copy['high'].astype('float64') if 'high' in df_copy.columns else pd.Series(dtype='float64'),
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'low': df_copy['low'].astype('float64') if 'low' in df_copy.columns else pd.Series(dtype='float64'),
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'close': df_copy['close'].astype('float64') if 'close' in df_copy.columns else pd.Series(dtype='float64'),
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'volume': df_copy['volume'].astype('float64') if 'volume' in df_copy.columns else pd.Series(dtype='float64'),
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'signals': pd.Series(0, index=df_copy.index, dtype='float64'),
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'np': np,
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'pd': pd,
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'params': merged_params,
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# 递归调用支持
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'call_indicator': lambda ref, d, p=None: self.call_indicator(ref, d, p, _depth + 1)
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}
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# 安全执行
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import builtins
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def safe_import(name, *args, **kwargs):
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allowed_modules = ['numpy', 'pandas', 'math', 'json', 'time']
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if name in allowed_modules or name.split('.')[0] in allowed_modules:
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return builtins.__import__(name, *args, **kwargs)
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raise ImportError(f"Module not allowed: {name}")
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safe_builtins = {k: getattr(builtins, k) for k in dir(builtins)
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if not k.startswith('_') and k not in [
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'eval', 'exec', 'compile', 'open', 'input',
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'help', 'exit', 'quit', '__import__',
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'copyright', 'credits', 'license'
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]}
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safe_builtins['__import__'] = safe_import
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exec_env = local_vars.copy()
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exec_env['__builtins__'] = safe_builtins
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pre_import = "import numpy as np\nimport pandas as pd\n"
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exec(pre_import, exec_env)
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exec(indicator_code, exec_env)
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return exec_env.get('df', df_copy)
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except Exception as e:
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logger.error(f"Error calling indicator {indicator_ref}: {e}")
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return df.copy()
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finally:
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self._call_stack.pop()
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def _get_indicator_code(self, indicator_ref: Any) -> Tuple[Optional[str], Optional[int]]:
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"""获取指标代码"""
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try:
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with get_db_connection() as db:
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cursor = db.cursor()
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if isinstance(indicator_ref, int):
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# 按ID查询
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cursor.execute("""
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SELECT id, code FROM qd_indicator_codes
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WHERE id = %s AND (user_id = %s OR publish_to_community = 1)
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""", (indicator_ref, self.user_id))
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else:
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# 按名称查询（优先自己的指标）
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cursor.execute("""
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SELECT id, code FROM qd_indicator_codes
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WHERE name = %s AND user_id = %s
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UNION
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SELECT id, code FROM qd_indicator_codes
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WHERE name = %s AND publish_to_community = 1
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LIMIT 1
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""", (str(indicator_ref), self.user_id, str(indicator_ref)))
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row = cursor.fetchone()
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cursor.close()
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if row:
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return row['code'], row['id']
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return None, None
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except Exception as e:
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logger.error(f"Error fetching indicator code: {e}")
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return None, None
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def get_indicator_params(indicator_id: int) -> List[Dict[str, Any]]:
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"""
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获取指标的参数声明（供API调用）
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Args:
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indicator_id: 指标ID
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Returns:
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参数声明列表
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"""
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try:
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with get_db_connection() as db:
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cursor = db.cursor()
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cursor.execute("SELECT code FROM qd_indicator_codes WHERE id = %s", (indicator_id,))
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row = cursor.fetchone()
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cursor.close()
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if row and row['code']:
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return IndicatorParamsParser.parse_params(row['code'])
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return []
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except Exception as e:
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logger.error(f"Error getting indicator params: {e}")
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return []
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@@ -21,6 +21,7 @@ from app.utils.logger import get_logger
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from app.utils.db import get_db_connection
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from app.data_sources import DataSourceFactory
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from app.services.kline import KlineService
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from app.services.indicator_params import IndicatorParamsParser, IndicatorCaller
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logger = get_logger(__name__)
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@@ -1789,6 +1790,21 @@ class TradingExecutor:
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# Also provide a backtest-modal compatible nested config object: cfg.risk/cfg.scale/cfg.position.
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tc = dict(trading_config or {})
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cfg = self._build_cfg_from_trading_config(tc)
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# === 指标参数支持 ===
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# 从 trading_config 获取用户设置的指标参数
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user_indicator_params = tc.get('indicator_params', {})
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# 解析指标代码中声明的参数
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declared_params = IndicatorParamsParser.parse_params(indicator_code)
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# 合并参数（用户值优先，否则使用默认值）
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merged_params = IndicatorParamsParser.merge_params(declared_params, user_indicator_params)
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# === 指标调用器支持 ===
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# 获取用户ID和指标ID（用于 call_indicator 权限检查）
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user_id = tc.get('user_id', 1)
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indicator_id = tc.get('indicator_id')
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indicator_caller = IndicatorCaller(user_id, indicator_id)
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local_vars = {
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'df': df,
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'open': df['open'].astype('float64'),
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@@ -1802,6 +1818,8 @@ class TradingExecutor:
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'trading_config': tc,
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'config': tc, # alias
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'cfg': cfg, # normalized nested config
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'params': merged_params, # 指标参数 (新增)
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'call_indicator': indicator_caller.call_indicator, # 调用其他指标 (新增)
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'leverage': float(trading_config.get('leverage', 1)),
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'initial_capital': float(trading_config.get('initial_capital', 1000)),
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'commission': 0.001,
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