TIANHE
434 lines
17 KiB
Python
434 lines
17 KiB
Python
"""
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Indicator APIs (local-first).
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These endpoints are used by the frontend `/indicator-analysis` page.
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In the original architecture, the frontend called PHP endpoints like:
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`/addons/quantdinger/indicator/getIndicators`.
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For local mode, we expose Python equivalents under `/api/indicator/*`.
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"""
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from __future__ import annotations
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import json
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import os
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import re
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import time
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from typing import Any, Dict, List
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from flask import Blueprint, Response, jsonify, request
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from app.utils.db import get_db_connection
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from app.utils.logger import get_logger
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import requests
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logger = get_logger(__name__)
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indicator_bp = Blueprint("indicator", __name__)
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def _now_ts() -> int:
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return int(time.time())
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def _extract_indicator_meta_from_code(code: str) -> Dict[str, str]:
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"""
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Extract indicator name/description from python code.
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Expected variables:
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my_indicator_name = "..."
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my_indicator_description = "..."
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"""
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if not code or not isinstance(code, str):
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return {"name": "", "description": ""}
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# Simple assignment capture for single/double quoted strings.
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name_match = re.search(r'^\s*my_indicator_name\s*=\s*([\'"])(.*?)\1\s*$', code, re.MULTILINE)
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desc_match = re.search(r'^\s*my_indicator_description\s*=\s*([\'"])(.*?)\1\s*$', code, re.MULTILINE)
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name = (name_match.group(2).strip() if name_match else "")[:100]
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description = (desc_match.group(2).strip() if desc_match else "")[:500]
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return {"name": name, "description": description}
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def _row_to_indicator(row: Dict[str, Any], user_id: int) -> Dict[str, Any]:
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"""
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Map SQLite row -> frontend expected indicator shape.
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Frontend uses:
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- id, name, description, code
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- is_buy (1 bought, 0 custom)
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- user_id / userId
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- end_time (optional)
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"""
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return {
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"id": row.get("id"),
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"user_id": row.get("user_id") if row.get("user_id") is not None else user_id,
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"is_buy": row.get("is_buy") if row.get("is_buy") is not None else 0,
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"end_time": row.get("end_time") if row.get("end_time") is not None else 1,
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"name": row.get("name") or "",
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"code": row.get("code") or "",
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"description": row.get("description") or "",
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"publish_to_community": row.get("publish_to_community") if row.get("publish_to_community") is not None else 0,
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"pricing_type": row.get("pricing_type") or "free",
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"price": row.get("price") if row.get("price") is not None else 0,
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# Local mode: encryption is not supported; keep field for frontend compatibility (always 0).
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"is_encrypted": 0,
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"preview_image": row.get("preview_image") or "",
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# Prefer MySQL-like time fields; fallback to legacy local columns.
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"createtime": row.get("createtime") or row.get("created_at"),
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"updatetime": row.get("updatetime") or row.get("updated_at"),
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}
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@indicator_bp.route("/getIndicators", methods=["POST"])
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def get_indicators():
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"""
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Get indicator list for a user.
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Request:
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{ userid: number }
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Response:
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{ code: 1, data: [ ... ] }
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"""
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try:
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data = request.get_json() or {}
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user_id = int(data.get("userid") or 1)
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with get_db_connection() as db:
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cur = db.cursor()
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# Local mode: "我的指标" should include both purchased and custom indicators.
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cur.execute(
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"""
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SELECT
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id, user_id, is_buy, end_time, name, code, description,
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publish_to_community, pricing_type, price, is_encrypted, preview_image,
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createtime, updatetime, created_at, updated_at
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FROM qd_indicator_codes
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WHERE user_id = ?
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ORDER BY id DESC
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""",
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(user_id,),
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)
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rows = cur.fetchall() or []
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cur.close()
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out = [_row_to_indicator(r, user_id) for r in rows]
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return jsonify({"code": 1, "msg": "success", "data": out})
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except Exception as e:
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logger.error(f"get_indicators failed: {str(e)}", exc_info=True)
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return jsonify({"code": 0, "msg": str(e), "data": []}), 500
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@indicator_bp.route("/saveIndicator", methods=["POST"])
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def save_indicator():
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"""
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Create or update an indicator.
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Request (frontend sends many extra fields; we store only the essentials):
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{
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userid: number,
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id: number (0 for create),
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name: string,
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code: string,
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description?: string,
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...
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}
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"""
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try:
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data = request.get_json() or {}
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user_id = int(data.get("userid") or 1)
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indicator_id = int(data.get("id") or 0)
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code = data.get("code") or ""
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name = (data.get("name") or "").strip()
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description = (data.get("description") or "").strip()
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publish_to_community = 1 if data.get("publishToCommunity") or data.get("publish_to_community") else 0
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pricing_type = (data.get("pricingType") or data.get("pricing_type") or "free").strip() or "free"
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try:
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price = float(data.get("price") or 0)
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except Exception:
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price = 0.0
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preview_image = (data.get("previewImage") or data.get("preview_image") or "").strip()
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if not code or not str(code).strip():
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return jsonify({"code": 0, "msg": "code is required", "data": None}), 400
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# Local dev UX: if name/description not provided, derive from code variables.
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if not name or not description:
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meta = _extract_indicator_meta_from_code(code)
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if not name:
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name = meta.get("name") or ""
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if not description:
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description = meta.get("description") or ""
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if not name:
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name = "Custom Indicator"
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now = _now_ts()
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with get_db_connection() as db:
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cur = db.cursor()
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if indicator_id and indicator_id > 0:
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cur.execute(
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"""
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UPDATE qd_indicator_codes
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SET name = ?, code = ?, description = ?,
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publish_to_community = ?, pricing_type = ?, price = ?, preview_image = ?,
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updatetime = ?, updated_at = ?
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WHERE id = ? AND user_id = ? AND (is_buy IS NULL OR is_buy = 0)
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""",
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(name, code, description, publish_to_community, pricing_type, price, preview_image, now, now, indicator_id, user_id),
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)
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else:
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cur.execute(
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"""
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INSERT INTO qd_indicator_codes
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(user_id, is_buy, end_time, name, code, description,
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publish_to_community, pricing_type, price, preview_image,
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createtime, updatetime, created_at, updated_at)
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VALUES (?, 0, 1, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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(user_id, name, code, description, publish_to_community, pricing_type, price, preview_image, now, now, now, now),
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)
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indicator_id = int(cur.lastrowid or 0)
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db.commit()
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cur.close()
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return jsonify({"code": 1, "msg": "success", "data": {"id": indicator_id, "userid": user_id}})
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except Exception as e:
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logger.error(f"save_indicator 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("/deleteIndicator", methods=["POST"])
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def delete_indicator():
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"""Delete an indicator by id."""
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try:
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data = request.get_json() or {}
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user_id = int(data.get("userid") or 1)
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indicator_id = int(data.get("id") or 0)
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if not indicator_id:
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return jsonify({"code": 0, "msg": "id is required", "data": None}), 400
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with get_db_connection() as db:
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cur = db.cursor()
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cur.execute(
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"DELETE FROM qd_indicator_codes WHERE id = ? AND user_id = ? AND (is_buy IS NULL OR is_buy = 0)",
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(indicator_id, user_id),
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)
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db.commit()
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cur.close()
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return jsonify({"code": 1, "msg": "success", "data": None})
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except Exception as e:
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logger.error(f"delete_indicator 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("/aiGenerate", methods=["POST"])
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def ai_generate():
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"""
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SSE endpoint to generate indicator code.
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Frontend expects 'text/event-stream' with chunks:
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data: {"content":"..."}\n\n
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then:
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data: [DONE]\n\n
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Local-first: if OpenRouter key is not configured, we return a reasonable template.
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"""
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data = request.get_json() or {}
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prompt = (data.get("prompt") or "").strip()
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existing = (data.get("existingCode") or "").strip()
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if not prompt:
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# Keep SSE contract (match PHP behavior) so frontend doesn't look "stuck".
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def _err_stream():
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yield "data: " + json.dumps({"error": "提示词不能为空"}, ensure_ascii=False) + "\n\n"
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yield "data: [DONE]\n\n"
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return Response(
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_err_stream(),
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mimetype="text/event-stream",
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headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
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)
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# System prompt copied/adapted from the legacy PHP implementation.
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SYSTEM_PROMPT = """# Role
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You are an expert Python quantitative trading developer. Your task is to write custom indicator or strategy scripts for a professional K-line chart component running in a browser (Pyodide environment).
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# Context & Environment
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1. **Runtime Environment**: Code runs in a browser sandbox, **network access is prohibited** (cannot use `pip` or `requests`).
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2. **Pre-installed Libraries**: The system has already imported `pandas as pd` and `numpy as np`. **DO NOT** include `import pandas as pd` or `import numpy as np` in your generated code. Use `pd` and `np` directly.
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3. **Input Data**: The system provides a variable `df` (Pandas DataFrame) with index from 0 to N.
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- Columns include: `df['time']` (timestamp), `df['open']`, `df['high']`, `df['low']`, `df['close']`, `df['volume']`.
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# Output Requirement (Strict)
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At the end of code execution, you **MUST** define a dictionary variable named `output`. The system only reads this variable to render the chart.
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Additionally, you MUST define:
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- my_indicator_name = "..."
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- my_indicator_description = "..."
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`output` MUST follow this shape:
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output = {
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"name": my_indicator_name,
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"plots": [ { "name": str, "data": list, "color": "#RRGGBB", "overlay": bool, "type": "line" (optional) } ],
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"signals": [ { "type": "buy"|"sell", "text": str, "data": list, "color": "#RRGGBB" } ] (optional),
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"calculatedVars": {} (optional)
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}
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Where `data` lists MUST have the same length as `df` and use `None` for "no value".
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Backtest/execution compatibility (recommended):
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- Also set df['buy'] and df['sell'] as boolean columns (same length as df).
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# Signal confirmation / execution timing (IMPORTANT)
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- Signals are generally confirmed on bar close. The backtest engine may execute them on the next bar open to better match live trading and avoid look-ahead bias.
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# Robustness requirements (IMPORTANT)
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- Always handle NaN/inf and division-by-zero (common in RSI/BB/RSV calculations).
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- Avoid overly restrictive entry/exit logic that results in zero buy or zero sell signals.
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For multi-indicator strategies, do NOT require a crossover AND extreme RSI on the same bar unless explicitly requested.
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- Prefer edge-triggered signals (one-shot) to avoid repeated consecutive signals:
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buy = raw_buy.fillna(False) & (~raw_buy.shift(1).fillna(False))
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sell = raw_sell.fillna(False) & (~raw_sell.shift(1).fillna(False))
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- If your final conditions produce no buys or no sells in the visible range, relax logically (e.g., remove one filter or widen thresholds).
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IMPORTANT: Output Python code directly, without explanations, without descriptions, start directly with code, and do NOT use markdown code blocks like ```python.
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"""
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def _template_code() -> str:
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# Fallback template that follows the project expectations.
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header = (
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f"my_indicator_name = \"Custom Indicator\"\n"
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f"my_indicator_description = \"{prompt.replace('\\n', ' ')[:200]}\"\n\n"
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)
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body = (
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"df = df.copy()\n\n"
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"# Example: robust RSI with edge-triggered buy/sell (no position management, no TP/SL on chart)\n"
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"rsi_len = 14\n"
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"delta = df['close'].diff()\n"
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"gain = delta.clip(lower=0)\n"
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"loss = (-delta).clip(lower=0)\n"
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"# Wilder-style smoothing (stable and avoids early NaN explosion)\n"
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"avg_gain = gain.ewm(alpha=1/rsi_len, adjust=False).mean()\n"
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"avg_loss = loss.ewm(alpha=1/rsi_len, adjust=False).mean()\n"
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"rs = avg_gain / avg_loss.replace(0, np.nan)\n"
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"rsi = 100 - (100 / (1 + rs))\n"
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"rsi = rsi.fillna(50)\n\n"
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"# Raw conditions (avoid overly strict filters)\n"
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"raw_buy = (rsi < 30)\n"
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"raw_sell = (rsi > 70)\n"
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"# One-shot signals\n"
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"buy = raw_buy.fillna(False) & (~raw_buy.shift(1).fillna(False))\n"
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"sell = raw_sell.fillna(False) & (~raw_sell.shift(1).fillna(False))\n"
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"df['buy'] = buy.astype(bool)\n"
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"df['sell'] = sell.astype(bool)\n\n"
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"buy_marks = [df['low'].iloc[i] * 0.995 if bool(buy.iloc[i]) else None for i in range(len(df))]\n"
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"sell_marks = [df['high'].iloc[i] * 1.005 if bool(sell.iloc[i]) else None for i in range(len(df))]\n\n"
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"output = {\n"
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" 'name': my_indicator_name,\n"
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" 'plots': [\n"
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" {'name': 'RSI(14)', 'data': rsi.tolist(), 'color': '#faad14', 'overlay': False}\n"
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" ],\n"
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" 'signals': [\n"
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" {'type': 'buy', 'text': 'B', 'data': buy_marks, 'color': '#00E676'},\n"
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" {'type': 'sell', 'text': 'S', 'data': sell_marks, 'color': '#FF5252'}\n"
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" ]\n"
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"}\n"
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)
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if existing:
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header = "# Existing code was provided as context.\n" + header
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return header + body
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def _openrouter_base_and_key() -> tuple[str, str]:
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"""
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Support both:
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- OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
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- OPENROUTER_API_URL=https://openrouter.ai/api/v1/chat/completions
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"""
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key = os.getenv("OPENROUTER_API_KEY", "").strip()
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base = os.getenv("OPENROUTER_BASE_URL", "").strip()
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if not base:
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api_url = os.getenv("OPENROUTER_API_URL", "").strip()
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if api_url.endswith("/chat/completions"):
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base = api_url[: -len("/chat/completions")]
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if not base:
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base = "https://openrouter.ai/api/v1"
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return base, key
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def _generate_code_via_openrouter() -> str:
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base_url, api_key = _openrouter_base_and_key()
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if not api_key:
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return _template_code()
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model = os.getenv("OPENROUTER_MODEL", "openai/gpt-4o-mini").strip() or "openai/gpt-4o-mini"
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# Match legacy PHP default more closely
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temperature = float(os.getenv("OPENROUTER_TEMPERATURE", "0.7") or 0.7)
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# Build user prompt (match PHP behavior)
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user_prompt = prompt
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if existing:
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user_prompt = (
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"# Existing Code (modify based on this):\n\n```python\n"
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+ existing.strip()
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+ "\n```\n\n# Modification Requirements:\n\n"
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+ prompt
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+ "\n\nPlease generate complete new Python code based on the existing code above and my modification requirements. Output the complete Python code directly, without explanations, without segmentation."
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)
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payload = {
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"model": model,
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"temperature": temperature,
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"stream": False,
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"messages": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt},
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],
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}
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resp = requests.post(
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f"{base_url}/chat/completions",
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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},
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json=payload,
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timeout=120,
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)
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resp.raise_for_status()
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j = resp.json()
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content = (((j.get("choices") or [{}])[0]).get("message") or {}).get("content") or ""
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return content.strip() or _template_code()
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def stream():
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# 不扣任何 QDT：开源本地版直接生成/返回代码
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try:
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code_text = _generate_code_via_openrouter()
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except Exception as e:
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logger.warning(f"ai_generate openrouter failed, fallback to template: {e}")
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code_text = _template_code()
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# Stream in chunks (front-end appends).
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chunk_size = 200
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for i in range(0, len(code_text), chunk_size):
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chunk = code_text[i : i + chunk_size]
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yield "data: " + json.dumps({"content": chunk}, ensure_ascii=False) + "\n\n"
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yield "data: [DONE]\n\n"
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return Response(
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stream(),
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mimetype="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"X-Accel-Buffering": "no",
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},
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)
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