dienakdz
296 lines
11 KiB
Python
296 lines
11 KiB
Python
"""
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Indicator Parameters Parser and Helper Functions
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Supports two core functions:
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1. External transfer of indicator parameters - parse the @param statement in the indicator code
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2. Indicators call other indicators - provide call_indicator() function
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Parameter declaration format:
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# @param param_name type default_value description
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# @param ma_fast int 5 short-term moving average period
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# @param ma_slow int 20 long-term moving average period
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# @param threshold float 0.5 threshold
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Supported types: 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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"""Parsing parameter declarations in indicator code"""
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# Parameter declaration rules: # @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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Parsing parameter declarations in indicator code
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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": "Short-term moving average cycle"
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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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# Convert default value type
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default = cls._convert_value(default_str, param_type)
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# Canonical type name
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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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"""Convert string value to corresponding type"""
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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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Merge declared parameters with user-supplied parameters
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Args:
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declared_params: parameter declarations parsed from code
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user_params: user-provided parameter values
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Returns:
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Merged parameter dictionary (using user values or default values)
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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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# User supplied value, converted to correct type
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result[name] = cls._convert_value(str(user_params[name]), param_type)
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else:
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# Use default value
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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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Indicator caller - allows one indicator to call another indicator
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Usage (in indicator code):
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# Call by ID
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rsi_df = call_indicator(5, df)
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# Call by name (own indicator)
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macd_df = call_indicator('My MACD', df)
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"""
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# Maximum call depth to prevent circular dependencies
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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 = [] # Call stack for detecting circular dependencies
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def call_indicator(
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self,
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indicator_ref: Any, # int (ID) or str (name)
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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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Call another indicator and return the result
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Args:
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indicator_ref: indicator ID or name
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df: input K-line data
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params: parameters passed to the called indicator
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_depth: used internally to track call depth
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Returns:
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DataFrame after execution, containing columns calculated by the called indicator
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"""
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import pandas as pd
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import numpy as np
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# Check call depth
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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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# Get indicator code
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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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# Check for circular dependencies
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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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# Parse and merge parameters
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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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# Prepare execution environment
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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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# Recursive call support
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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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# Safe execution
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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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"""Get indicator code"""
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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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# Query by ID
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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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# Query by name (priority to own indicators)
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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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