TIANHE
f4e5a9f8e0
- Fix Invalid Date display in Dashboard notifications - Fix timezone offset (8 hours) in Trading Records time display - Fix position closing failures due to commission discrepancies (fetch actual exchange position size for reduce_only orders) - Fix IBKR connection error 'no current event loop in thread' by ensuring asyncio event loop exists - Fix duplicate orders on same candle by extending signal deduplication to close signals - Add responsive design for Profile page (mobile-friendly) - Remove unused strategy_code module and database table - Fix LLM service to support multiple providers (OpenRouter, OpenAI, DeepSeek, Grok, Google) - Add auto-detection of configured LLM provider based on API key availability - Fix AI code generation to use unified LLMService with proper provider selection - Fix crypto symbol format handling (ETH/USDT no longer becomes ETH/USDT/USDT) - Fix Commission display showing '0E-8' in Trading Records - Fix P&L display for signal-only trades (show '--' for unrealized P&L) - Fix OAuth login not updating last_login_at for new users - Add migration script for notification_settings column - Update env.example with new LLM provider configurations - Remove ESLint rule that was not defined in config
573 lines
22 KiB
Python
573 lines
22 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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import traceback
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from typing import Any, Dict, List
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from flask import Blueprint, Response, jsonify, request, g
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import pandas as pd
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import numpy as np
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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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from app.utils.auth import login_required
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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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def _generate_mock_df(length=200):
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"""Generate mock K-line data for verification."""
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from datetime import datetime, timedelta
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dates = [datetime.now() - timedelta(minutes=i) for i in range(length)]
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dates.reverse()
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# Random walk with trend
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returns = np.random.normal(0, 0.002, length)
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price_path = 10000 * np.exp(np.cumsum(returns))
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close = price_path
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high = close * (1 + np.abs(np.random.normal(0, 0.001, length)))
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low = close * (1 - np.abs(np.random.normal(0, 0.001, length)))
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open_p = close * (1 + np.random.normal(0, 0.001, length)) # Slight deviation from close
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# Ensure High is highest and Low is lowest
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high = np.maximum(high, np.maximum(open_p, close))
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low = np.minimum(low, np.minimum(open_p, close))
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volume = np.abs(np.random.normal(100, 50, length)) * 1000
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df = pd.DataFrame({
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'time': [int(d.timestamp() * 1000) for d in dates],
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'open': open_p,
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'high': high,
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'low': low,
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'close': close,
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'volume': volume
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})
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return df
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@indicator_bp.route("/getIndicators", methods=["GET"])
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@login_required
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def get_indicators():
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"""
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Get indicator list for the current user.
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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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user_id = g.user_id
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with get_db_connection() as db:
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cur = db.cursor()
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# Get user's own indicators (both purchased and custom).
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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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@login_required
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def save_indicator():
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"""
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Create or update an indicator for the current user.
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Request (frontend sends many extra fields; we store only the essentials):
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{
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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 = g.user_id
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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() # For BIGINT fields (createtime, updatetime)
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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 = NOW()
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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, 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, ?, ?, ?, ?, ?, ?, ?, ?, ?, NOW(), NOW())
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""",
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(user_id, name, code, description, publish_to_community, pricing_type, price, preview_image, 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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@login_required
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def delete_indicator():
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"""Delete an indicator by id for the current user."""
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try:
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data = request.get_json() or {}
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user_id = g.user_id
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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("/verifyCode", methods=["POST"])
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@login_required
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def verify_code():
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"""
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Verify/Dry-run indicator code with mock data.
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Checks for:
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- Syntax errors
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- Runtime errors
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- Output format (must define 'output' dict)
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"""
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try:
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data = request.get_json() or {}
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code = data.get("code") or ""
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if not code or not str(code).strip():
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return jsonify({"code": 0, "msg": "Code is empty", "data": None}), 400
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# 1. Generate mock data
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df = _generate_mock_df()
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# 2. Prepare execution environment
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exec_env = {
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'df': df.copy(),
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'pd': pd,
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'np': np,
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'output': None
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}
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# 3. Execute code
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try:
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exec(code, exec_env)
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except SyntaxError as e:
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return jsonify({
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"code": 0,
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"msg": f"Syntax Error at line {e.lineno}: {e.msg}",
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"data": {"type": "SyntaxError", "line": e.lineno, "details": str(e)}
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})
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except Exception as e:
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# Capture traceback for better debugging
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tb = traceback.format_exc()
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# Extract the line number from the exec() call in the traceback if possible
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# This is tricky because the traceback includes the backend frames.
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# We'll just return the exception message.
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return jsonify({
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"code": 0,
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"msg": f"Runtime Error: {str(e)}",
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"data": {"type": type(e).__name__, "details": tb}
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})
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# 4. Check output
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output = exec_env.get('output')
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if output is None:
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return jsonify({
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"code": 0,
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"msg": "Missing 'output' variable. Your code must define an 'output' dictionary.",
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"data": {"type": "MissingOutput"}
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})
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if not isinstance(output, dict):
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return jsonify({
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"code": 0,
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"msg": f"'output' must be a dictionary, got {type(output).__name__}",
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"data": {"type": "InvalidOutputType"}
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})
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# Check required fields
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if 'plots' not in output and 'signals' not in output:
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return jsonify({
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"code": 0,
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"msg": "'output' dict should contain 'plots' or 'signals' list.",
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"data": {"type": "InvalidOutputStructure"}
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})
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# Basic check for lengths
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plots = output.get('plots', [])
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signals = output.get('signals', [])
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for p in plots:
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if 'data' not in p:
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return jsonify({"code": 0, "msg": f"Plot '{p.get('name')}' missing 'data' field.", "data": {"type": "InvalidPlot"}})
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if len(p['data']) != len(df):
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return jsonify({
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"code": 0,
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"msg": f"Plot '{p.get('name')}' data length ({len(p['data'])}) does not match DataFrame length ({len(df)}).",
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"data": {"type": "LengthMismatch"}
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})
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for s in signals:
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if 'data' not in s:
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return jsonify({"code": 0, "msg": f"Signal '{s.get('type')}' missing 'data' field.", "data": {"type": "InvalidSignal"}})
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if len(s['data']) != len(df):
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return jsonify({
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"code": 0,
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"msg": f"Signal '{s.get('type')}' data length ({len(s['data'])}) does not match DataFrame length ({len(df)}).",
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"data": {"type": "LengthMismatch"}
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})
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return jsonify({
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"code": 1,
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"msg": "Verification passed! Code executed successfully.",
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"data": {
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"plots_count": len(plots),
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"signals_count": len(signals)
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}
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})
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except Exception as e:
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logger.error(f"verify_code failed: {str(e)}", exc_info=True)
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return jsonify({"code": 0, "msg": f"System Error: {str(e)}", "data": None}), 500
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@indicator_bp.route("/aiGenerate", methods=["POST"])
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@login_required
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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"
|
|
"# Example: robust RSI with edge-triggered buy/sell (no position management, no TP/SL on chart)\n"
|
|
"rsi_len = 14\n"
|
|
"delta = df['close'].diff()\n"
|
|
"gain = delta.clip(lower=0)\n"
|
|
"loss = (-delta).clip(lower=0)\n"
|
|
"# Wilder-style smoothing (stable and avoids early NaN explosion)\n"
|
|
"avg_gain = gain.ewm(alpha=1/rsi_len, adjust=False).mean()\n"
|
|
"avg_loss = loss.ewm(alpha=1/rsi_len, adjust=False).mean()\n"
|
|
"rs = avg_gain / avg_loss.replace(0, np.nan)\n"
|
|
"rsi = 100 - (100 / (1 + rs))\n"
|
|
"rsi = rsi.fillna(50)\n\n"
|
|
"# Raw conditions (avoid overly strict filters)\n"
|
|
"raw_buy = (rsi < 30)\n"
|
|
"raw_sell = (rsi > 70)\n"
|
|
"# One-shot signals\n"
|
|
"buy = raw_buy.fillna(False) & (~raw_buy.shift(1).fillna(False))\n"
|
|
"sell = raw_sell.fillna(False) & (~raw_sell.shift(1).fillna(False))\n"
|
|
"df['buy'] = buy.astype(bool)\n"
|
|
"df['sell'] = sell.astype(bool)\n\n"
|
|
"buy_marks = [df['low'].iloc[i] * 0.995 if bool(buy.iloc[i]) else None for i in range(len(df))]\n"
|
|
"sell_marks = [df['high'].iloc[i] * 1.005 if bool(sell.iloc[i]) else None for i in range(len(df))]\n\n"
|
|
"output = {\n"
|
|
" 'name': my_indicator_name,\n"
|
|
" 'plots': [\n"
|
|
" {'name': 'RSI(14)', 'data': rsi.tolist(), 'color': '#faad14', 'overlay': False}\n"
|
|
" ],\n"
|
|
" 'signals': [\n"
|
|
" {'type': 'buy', 'text': 'B', 'data': buy_marks, 'color': '#00E676'},\n"
|
|
" {'type': 'sell', 'text': 'S', 'data': sell_marks, 'color': '#FF5252'}\n"
|
|
" ]\n"
|
|
"}\n"
|
|
)
|
|
if existing:
|
|
header = "# Existing code was provided as context.\n" + header
|
|
return header + body
|
|
|
|
def _generate_code_via_llm() -> str:
|
|
"""Use unified LLMService to support all configured providers (OpenRouter, OpenAI, Grok, etc.)."""
|
|
from app.services.llm import LLMService
|
|
|
|
llm = LLMService()
|
|
|
|
# Get provider and model from env config (no frontend override)
|
|
current_provider = llm.provider
|
|
current_model = llm.get_default_model()
|
|
current_api_key = llm.get_api_key()
|
|
base_url = llm.get_base_url()
|
|
|
|
logger.info(f"AI Code Generation - Provider: {current_provider.value}, Model: {current_model}, Base URL: {base_url}, API Key configured: {bool(current_api_key)}")
|
|
|
|
# Check if any LLM provider is configured
|
|
if not current_api_key:
|
|
logger.warning("No LLM API key configured, using template code")
|
|
return _template_code()
|
|
|
|
# Build user prompt (match PHP behavior)
|
|
user_prompt = prompt
|
|
if existing:
|
|
user_prompt = (
|
|
"# Existing Code (modify based on this):\n\n```python\n"
|
|
+ existing.strip()
|
|
+ "\n```\n\n# Modification Requirements:\n\n"
|
|
+ prompt
|
|
+ "\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."
|
|
)
|
|
|
|
temperature = float(os.getenv("OPENROUTER_TEMPERATURE", "0.7") or 0.7)
|
|
|
|
# Call LLM using the unified API (auto-selects provider based on LLM_PROVIDER env)
|
|
# use_json_mode=False because we want raw Python code output
|
|
content = llm.call_llm_api(
|
|
messages=[
|
|
{"role": "system", "content": SYSTEM_PROMPT},
|
|
{"role": "user", "content": user_prompt},
|
|
],
|
|
temperature=temperature,
|
|
use_json_mode=False # Code generation doesn't need JSON mode
|
|
)
|
|
|
|
# Clean up markdown code blocks if present
|
|
content = content.strip()
|
|
if content.startswith("```python"):
|
|
content = content[9:]
|
|
elif content.startswith("```"):
|
|
content = content[3:]
|
|
if content.endswith("```"):
|
|
content = content[:-3]
|
|
|
|
return content.strip() or _template_code()
|
|
|
|
def stream():
|
|
# 不扣任何 QDT：开源本地版直接生成/返回代码
|
|
try:
|
|
code_text = _generate_code_via_llm()
|
|
except Exception as e:
|
|
logger.error(f"ai_generate LLM failed, fallback to template. Error: {type(e).__name__}: {e}")
|
|
code_text = _template_code()
|
|
|
|
# Stream in chunks (front-end appends).
|
|
chunk_size = 200
|
|
for i in range(0, len(code_text), chunk_size):
|
|
chunk = code_text[i : i + chunk_size]
|
|
yield "data: " + json.dumps({"content": chunk}, ensure_ascii=False) + "\n\n"
|
|
yield "data: [DONE]\n\n"
|
|
|
|
return Response(
|
|
stream(),
|
|
mimetype="text/event-stream",
|
|
headers={
|
|
"Cache-Control": "no-cache",
|
|
"X-Accel-Buffering": "no",
|
|
},
|
|
)
|