TIANHE
802 lines
37 KiB
Python
802 lines
37 KiB
Python
"""
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Backtest API routes
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"""
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from flask import Blueprint, request, jsonify
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from datetime import datetime
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import traceback
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import json
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import time
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import os
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from app.services.backtest import BacktestService
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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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import requests
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logger = get_logger(__name__)
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backtest_bp = Blueprint('backtest', __name__)
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backtest_service = BacktestService()
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def _openrouter_base_and_key() -> tuple[str, str]:
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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 _normalize_lang(lang: str | None) -> str:
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"""
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Normalize language code for AI output.
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This should align with frontend i18n locales under `quantdinger_vue/src/locales/lang`.
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Supported:
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- zh-CN, zh-TW, en-US, ko-KR, th-TH, vi-VN, ar-SA, de-DE, fr-FR, ja-JP
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Default: zh-CN
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"""
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supported = {
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"zh-CN",
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"zh-TW",
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"en-US",
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"ko-KR",
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"th-TH",
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"vi-VN",
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"ar-SA",
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"de-DE",
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"fr-FR",
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"ja-JP",
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}
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l = (lang or "").strip()
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if not l:
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return "zh-CN"
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alias = {
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"zh": "zh-CN",
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"zh-cn": "zh-CN",
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"zh-hans": "zh-CN",
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"zh-tw": "zh-TW",
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"zh-hant": "zh-TW",
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"en": "en-US",
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"en-us": "en-US",
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"ko": "ko-KR",
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"ko-kr": "ko-KR",
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"ja": "ja-JP",
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"ja-jp": "ja-JP",
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"fr": "fr-FR",
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"fr-fr": "fr-FR",
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"de": "de-DE",
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"de-de": "de-DE",
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"vi": "vi-VN",
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"vi-vn": "vi-VN",
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"th": "th-TH",
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"th-th": "th-TH",
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"ar": "ar-SA",
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"ar-sa": "ar-SA",
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}
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l2 = alias.get(l.lower(), l)
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return l2 if l2 in supported else "zh-CN"
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@backtest_bp.route('/backtest', methods=['POST'])
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def run_backtest():
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"""
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Run indicator backtest
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Params:
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indicatorId: Indicator ID (optional)
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indicatorCode: Indicator Python code
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symbol: Symbol
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market: Market type
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timeframe: Timeframe
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startDate: Start date (YYYY-MM-DD)
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endDate: End date (YYYY-MM-DD)
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initialCapital: Initial capital (default 10000)
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commission: Commission rate (default 0.001)
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"""
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try:
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data = request.get_json()
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if not data:
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return jsonify({
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'code': 0,
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'msg': 'Request body is required',
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'data': None
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}), 400
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# Extract params
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user_id = int(data.get('userid') or data.get('userId') or 1)
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indicator_code = data.get('indicatorCode', '')
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indicator_id = data.get('indicatorId')
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symbol = data.get('symbol', '')
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market = data.get('market', '')
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timeframe = data.get('timeframe', '1D')
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start_date_str = data.get('startDate', '')
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end_date_str = data.get('endDate', '')
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initial_capital = float(data.get('initialCapital', 10000))
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commission = float(data.get('commission', 0.001))
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slippage = float(data.get('slippage', 0.0))
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leverage = int(data.get('leverage', 1))
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trade_direction = data.get('tradeDirection', 'long') # long, short, both
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strategy_config = data.get('strategyConfig') or {}
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# (Debug) log received params if needed
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# If frontend only provides indicatorId, load code from local DB.
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if (not indicator_code or not str(indicator_code).strip()) and indicator_id:
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try:
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iid = int(indicator_id)
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with get_db_connection() as db:
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cur = db.cursor()
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cur.execute("SELECT code FROM qd_indicator_codes WHERE id = ?", (iid,))
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row = cur.fetchone()
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cur.close()
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if row and row.get('code'):
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indicator_code = row.get('code')
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except Exception:
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pass
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# 参数验证
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if not all([indicator_code, symbol, market, timeframe, start_date_str, end_date_str]):
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return jsonify({
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'code': 0,
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'msg': 'Missing required parameters',
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'data': None
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}), 400
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# 转换日期
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# 开始日期：当天的 00:00:00
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start_date = datetime.strptime(start_date_str, '%Y-%m-%d')
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# 结束日期：当天的 23:59:59，确保包含整天的数据
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end_date = datetime.strptime(end_date_str, '%Y-%m-%d').replace(hour=23, minute=59, second=59)
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# 验证时间范围限制
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days_diff = (end_date - start_date).days
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# 根据周期设置不同的时间限制
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if timeframe == '1m':
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max_days = 30 # 1分钟K线最多1个月
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max_range_text = '1 month'
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elif timeframe == '5m':
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max_days = 180 # 5分钟K线最多6个月
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max_range_text = '6 months'
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elif timeframe in ['15m', '30m']:
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max_days = 365 # 15分钟和30分钟K线最多1年
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max_range_text = '1 year'
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else: # 1H, 4H, 1D, 1W
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max_days = 1095 # 1小时及以上最多3年
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max_range_text = '3 years'
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if days_diff > max_days:
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return jsonify({
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'code': 0,
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'msg': f'Backtest range exceeds limit: timeframe {timeframe} supports up to {max_range_text} ({max_days} days), but you selected {days_diff} days',
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'data': None
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}), 400
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# 执行回测
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result = backtest_service.run(
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indicator_code=indicator_code,
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market=market,
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symbol=symbol,
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timeframe=timeframe,
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start_date=start_date,
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end_date=end_date,
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initial_capital=initial_capital,
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commission=commission,
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slippage=slippage,
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leverage=leverage,
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trade_direction=trade_direction,
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strategy_config=strategy_config
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)
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# Persist backtest run for AI optimization / history
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run_id = None
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try:
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now_ts = int(time.time())
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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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"""
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INSERT INTO qd_backtest_runs
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(user_id, indicator_id, market, symbol, timeframe, start_date, end_date,
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initial_capital, commission, slippage, leverage, trade_direction,
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strategy_config, status, error_message, result_json, created_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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(
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user_id,
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int(indicator_id) if indicator_id is not None else None,
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market,
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symbol,
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timeframe,
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start_date_str,
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end_date_str,
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initial_capital,
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commission,
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slippage,
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leverage,
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trade_direction,
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json.dumps(strategy_config or {}, ensure_ascii=False),
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'success',
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'',
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json.dumps(result or {}, ensure_ascii=False),
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now_ts
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)
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)
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run_id = cur.lastrowid
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db.commit()
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cur.close()
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except Exception:
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# Do not break the main backtest response if persistence fails.
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logger.warning("Failed to persist backtest run", exc_info=True)
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return jsonify({
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'code': 1,
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'msg': 'Backtest succeeded',
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'data': {
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'runId': run_id,
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'result': result
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}
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})
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except ValueError as e:
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logger.warning(f"Invalid backtest parameters: {str(e)}")
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return jsonify({
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'code': 0,
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'msg': str(e),
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'data': None
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}), 400
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except Exception as e:
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logger.error(f"Backtest failed: {str(e)}")
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logger.error(traceback.format_exc())
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# Best-effort persist failed run (if we have enough context)
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try:
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data = data if isinstance(data, dict) else {}
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user_id = int(data.get('userid') or data.get('userId') or 1)
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indicator_id = data.get('indicatorId')
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now_ts = int(time.time())
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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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"""
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INSERT INTO qd_backtest_runs
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(user_id, indicator_id, market, symbol, timeframe, start_date, end_date,
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initial_capital, commission, slippage, leverage, trade_direction,
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strategy_config, status, error_message, result_json, created_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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(
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user_id,
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int(indicator_id) if indicator_id is not None else None,
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str(data.get('market', '') or ''),
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str(data.get('symbol', '') or ''),
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str(data.get('timeframe', '') or ''),
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str(data.get('startDate', '') or ''),
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str(data.get('endDate', '') or ''),
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float(data.get('initialCapital', 0) or 0),
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float(data.get('commission', 0) or 0),
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float(data.get('slippage', 0) or 0),
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int(data.get('leverage', 1) or 1),
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str(data.get('tradeDirection', 'long') or 'long'),
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json.dumps(data.get('strategyConfig') or {}, ensure_ascii=False),
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'failed',
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str(e),
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'',
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now_ts
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)
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)
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db.commit()
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cur.close()
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except Exception:
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pass
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return jsonify({
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'code': 0,
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'msg': f'Backtest failed: {str(e)}',
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'data': None
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}), 500
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@backtest_bp.route('/backtest/history', methods=['POST'])
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def get_backtest_history():
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"""
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Get backtest run history (saved in SQLite).
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Params:
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userid: User ID (default 1)
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limit: Page size (default 50, max 200)
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offset: Offset (default 0)
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indicatorId: Optional indicator id filter
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symbol: Optional symbol filter
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market: Optional market filter
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timeframe: Optional timeframe filter
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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 data.get('userId') or 1)
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limit = int(data.get('limit') or 50)
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offset = int(data.get('offset') or 0)
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limit = max(1, min(limit, 200))
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offset = max(0, offset)
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indicator_id = data.get('indicatorId')
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symbol = (data.get('symbol') or '').strip()
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market = (data.get('market') or '').strip()
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timeframe = (data.get('timeframe') or '').strip()
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where = ["user_id = ?"]
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params = [user_id]
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if indicator_id is not None and str(indicator_id).strip() != "":
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try:
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where.append("indicator_id = ?")
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params.append(int(indicator_id))
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except Exception:
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pass
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if symbol:
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where.append("symbol = ?")
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params.append(symbol)
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if market:
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where.append("market = ?")
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params.append(market)
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if timeframe:
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where.append("timeframe = ?")
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params.append(timeframe)
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where_sql = " AND ".join(where)
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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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f"""
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SELECT id, user_id, indicator_id, market, symbol, timeframe,
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start_date, end_date, initial_capital, commission, slippage,
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leverage, trade_direction, strategy_config, status, error_message,
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created_at
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FROM qd_backtest_runs
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WHERE {where_sql}
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ORDER BY id DESC
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LIMIT ? OFFSET ?
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""",
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(*params, limit, offset)
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)
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rows = cur.fetchall() or []
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cur.close()
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# Parse strategy_config JSON best-effort
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for r in rows:
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try:
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r['strategy_config'] = json.loads(r.get('strategy_config') or '{}')
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except Exception:
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pass
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return jsonify({'code': 1, 'msg': 'OK', 'data': rows})
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except Exception as e:
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logger.error(f"get_backtest_history failed: {e}")
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logger.error(traceback.format_exc())
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return jsonify({'code': 0, 'msg': str(e), 'data': None}), 500
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@backtest_bp.route('/backtest/get', methods=['POST'])
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def get_backtest_run():
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"""
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Get a backtest run detail by run id (includes result_json).
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Params:
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userid: User ID (default 1)
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runId: Backtest run id (required)
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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 data.get('userId') or 1)
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run_id = int(data.get('runId') or 0)
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if not run_id:
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return jsonify({'code': 0, 'msg': 'runId 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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"""
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SELECT id, user_id, indicator_id, market, symbol, timeframe,
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start_date, end_date, initial_capital, commission, slippage,
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leverage, trade_direction, strategy_config, status, error_message,
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result_json, created_at
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FROM qd_backtest_runs
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WHERE id = ? AND user_id = ?
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""",
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(run_id, user_id),
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)
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row = cur.fetchone()
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cur.close()
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if not row:
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return jsonify({'code': 0, 'msg': 'run not found', 'data': None}), 404
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try:
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row['strategy_config'] = json.loads(row.get('strategy_config') or '{}')
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except Exception:
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pass
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try:
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row['result'] = json.loads(row.get('result_json') or '{}')
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except Exception:
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row['result'] = {}
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row.pop('result_json', None)
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return jsonify({'code': 1, 'msg': 'OK', 'data': row})
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except Exception as e:
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logger.error(f"get_backtest_run failed: {e}")
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logger.error(traceback.format_exc())
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return jsonify({'code': 0, 'msg': str(e), 'data': None}), 500
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def _heuristic_ai_advice(runs: list[dict], lang: str) -> str:
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"""
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Heuristic fallback when no model key is configured.
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Returns Chinese suggestions for parameter tuning.
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"""
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if not runs:
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msg_map = {
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"zh-CN": "未找到可分析的回测记录。",
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"zh-TW": "未找到可分析的回測記錄。",
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"en-US": "No backtest runs selected.",
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"ko-KR": "분석할 백테스트 기록을 찾을 수 없습니다.",
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"th-TH": "ไม่พบประวัติแบ็กเทสต์สำหรับการวิเคราะห์",
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"vi-VN": "Không tìm thấy lịch sử backtest để phân tích.",
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"ar-SA": "لم يتم العثور على سجلات اختبار خلفي لتحليلها.",
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"de-DE": "Keine Backtest-Läufe zur Analyse ausgewählt.",
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"fr-FR": "Aucune exécution de backtest sélectionnée pour analyse.",
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"ja-JP": "分析するバックテスト記録が見つかりません。",
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}
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return msg_map.get(lang, msg_map["en-US"])
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# Use the last run as primary context, but mention multi-run comparison if provided.
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r0 = runs[0]
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result = (r0.get("result") or {}) if isinstance(r0, dict) else {}
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cfg = (r0.get("strategy_config") or {}) if isinstance(r0, dict) else {}
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risk = cfg.get("risk") or {}
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pos = cfg.get("position") or {}
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scale = cfg.get("scale") or {}
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total_return = float(result.get("totalReturn") or 0.0)
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max_dd = float(result.get("maxDrawdown") or 0.0)
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sharpe = float(result.get("sharpeRatio") or 0.0)
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win_rate = float(result.get("winRate") or 0.0)
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profit_factor = float(result.get("profitFactor") or 0.0)
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trades = int(result.get("totalTrades") or 0)
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stop_loss = float(risk.get("stopLossPct") or 0.0)
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take_profit = float(risk.get("takeProfitPct") or 0.0)
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trailing = (risk.get("trailing") or {}) if isinstance(risk.get("trailing"), dict) else {}
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trailing_enabled = bool(trailing.get("enabled"))
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trailing_pct = float(trailing.get("pct") or 0.0)
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trailing_act = float(trailing.get("activationPct") or 0.0)
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entry_pct = float(pos.get("entryPct") or 1.0)
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trend_add = scale.get("trendAdd") or {}
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dca_add = scale.get("dcaAdd") or {}
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trend_reduce = scale.get("trendReduce") or {}
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adverse_reduce = scale.get("adverseReduce") or {}
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# Minimal localized headings to keep heuristic readable across locales.
|
||
headings = {
|
||
"zh-CN": {"overall": "【总体建议】", "params": "【参数建议（可直接改回测配置测试）】", "next": "【下一步建议的回测方法】"},
|
||
"zh-TW": {"overall": "【總體建議】", "params": "【參數建議（可直接改回測配置測試）】", "next": "【下一步回測方法建議】"},
|
||
"en-US": {"overall": "Overall", "params": "Parameter suggestions (edit backtest config and re-run)", "next": "Next steps"},
|
||
"ko-KR": {"overall": "요약", "params": "파라미터 제안(백테스트 설정 변경)", "next": "다음 단계"},
|
||
"th-TH": {"overall": "สรุป", "params": "ข้อเสนอแนะพารามิเตอร์ (ปรับค่าที่ตั้งแบ็กเทสต์)", "next": "ขั้นตอนถัดไป"},
|
||
"vi-VN": {"overall": "Tổng quan", "params": "Gợi ý tham số (sửa cấu hình backtest và chạy lại)", "next": "Bước tiếp theo"},
|
||
"ar-SA": {"overall": "ملخص", "params": "اقتراحات المعلمات (عدّل إعدادات الاختبار وأعد التشغيل)", "next": "الخطوات التالية"},
|
||
"de-DE": {"overall": "Überblick", "params": "Parameter-Vorschläge (Backtest-Konfiguration anpassen)", "next": "Nächste Schritte"},
|
||
"fr-FR": {"overall": "Vue d’ensemble", "params": "Suggestions de paramètres (modifier la config et relancer)", "next": "Étapes suivantes"},
|
||
"ja-JP": {"overall": "概要", "params": "パラメータ提案（設定変更→再バックテスト）", "next": "次のステップ"},
|
||
}
|
||
h = headings.get(lang, headings["en-US"])
|
||
|
||
lines = []
|
||
if lang == "en-US":
|
||
if len(runs) > 1:
|
||
lines.append(f"Received {len(runs)} backtest runs. Suggestions below focus on run #{r0.get('id','')}; validate with A/B tests across runs.")
|
||
lines.append(h["overall"])
|
||
elif lang == "zh-TW":
|
||
if len(runs) > 1:
|
||
lines.append(f"已收到 {len(runs)} 條回測記錄。以下以記錄 #{r0.get('id','')} 為主給出參數調整建議，並建議你用多組記錄做 A/B 驗證。")
|
||
lines.append(h["overall"])
|
||
else:
|
||
if len(runs) > 1:
|
||
if lang == "ko-KR":
|
||
lines.append(f"{len(runs)}개의 백테스트 기록을 받았습니다. 아래는 #{r0.get('id','')} 기준으로 제안하며, 여러 기록으로 A/B 검증을 권장합니다.")
|
||
elif lang == "th-TH":
|
||
lines.append(f"ได้รับประวัติแบ็กเทสต์ {len(runs)} รายการ ข้อเสนอแนะด้านล่างอิงจาก #{r0.get('id','')} และแนะนำให้ทำ A/B test เทียบหลายชุด")
|
||
elif lang == "vi-VN":
|
||
lines.append(f"Đã nhận {len(runs)} bản ghi backtest. Gợi ý bên dưới tập trung vào #{r0.get('id','')} và khuyến nghị A/B test với nhiều bản ghi.")
|
||
elif lang == "ar-SA":
|
||
lines.append(f"تم استلام {len(runs)} من سجلات الاختبار الخلفي. تركّز الاقتراحات أدناه على التشغيل #{r0.get('id','')} مع توصية باختبارات A/B.")
|
||
elif lang == "de-DE":
|
||
lines.append(f"{len(runs)} Backtest-Läufe empfangen. Vorschläge unten fokussieren auf Lauf #{r0.get('id','')}; A/B-Tests über mehrere Läufe empfohlen.")
|
||
elif lang == "fr-FR":
|
||
lines.append(f"{len(runs)} exécutions de backtest reçues. Suggestions ci-dessous centrées sur #{r0.get('id','')}; A/B tests recommandés.")
|
||
elif lang == "ja-JP":
|
||
lines.append(f"{len(runs)} 件のバックテスト記録を受け取りました。以下は #{r0.get('id','')} を中心に提案し、複数記録でA/B検証を推奨します。")
|
||
else:
|
||
lines.append(f"Received {len(runs)} backtest runs. Suggestions below focus on run #{r0.get('id','')}; validate with A/B tests across runs.")
|
||
lines.append(h["overall"])
|
||
if sharpe < 0 or total_return < 0:
|
||
if lang == "en-US":
|
||
lines.append("- Strategy is losing/unstable: reduce risk first (lower entryPct, fewer/smaller scale-ins), then refine signal filters.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 目前策略偏虧損/不穩定：先降低風險暴露（降低開倉資金占比 entryPct、減少加倉次數/比例），再調整信號過濾。")
|
||
else:
|
||
lines.append("- 当前策略整体偏亏损/不稳定：优先降低风险暴露（降低开仓资金占比 entryPct、减少加仓次数/比例），再调信号过滤。")
|
||
if max_dd > 30:
|
||
if lang == "en-US":
|
||
lines.append("- Max drawdown is high: tighten stop-loss or reduce leverage/entry size; consider enabling trailing to protect profits.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 最大回撤偏大：建議優先收緊止損或降低槓桿/開倉倉位；同時考慮啟用移動止盈以保護盈利回撤。")
|
||
else:
|
||
lines.append("- 最大回撤较大：建议优先收紧止损或降低杠杆/开仓仓位；同时考虑启用移动止盈保护盈利回撤。")
|
||
if trades < 10:
|
||
if lang == "en-US":
|
||
lines.append("- Too few trades: rules may be too strict; relax thresholds or remove one filter to get enough samples.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 交易次數偏少：可能條件過嚴，建議適度放寬信號門檻或減少過濾條件，確保有足夠樣本驗證。")
|
||
else:
|
||
lines.append("- 交易次数偏少：可能条件过严，建议适当放宽信号阈值或减少过滤条件，确保有足够样本验证。")
|
||
if win_rate < 35 and profit_factor >= 1.2:
|
||
if lang == "en-US":
|
||
lines.append("- Low win rate but decent PF: consider slightly wider stop-loss and use trailing to lock profits.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 勝率偏低但盈虧比不差：可考慮略放寬止損（讓盈利單跑起來），並用移動止盈鎖住利潤。")
|
||
else:
|
||
lines.append("- 胜率偏低但盈亏比不差：可以考虑放宽止损（让盈利单跑起来）并用移动止盈锁利润。")
|
||
if win_rate >= 55 and profit_factor < 1.1:
|
||
if lang == "en-US":
|
||
lines.append("- Win rate is OK but PF is low: raise take-profit or enable trailing to improve winners; avoid taking profits too early.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 勝率不低但盈虧比偏小：考慮提高止盈或啟用移動止盈，讓單筆盈利更充分；避免過早止盈。")
|
||
else:
|
||
lines.append("- 胜率不低但盈亏比偏小：考虑提高止盈或启用移动止盈，让单笔盈利更充分；避免过早止盈。")
|
||
|
||
lines.append("\n" + h["params"])
|
||
if stop_loss <= 0:
|
||
if lang == "en-US":
|
||
lines.append("- Stop-loss: set stopLossPct (margin PnL basis). For crypto leverage, start with 2%~6% (then consider leverage conversion) and grid test.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 止損：建議設定 stopLossPct（按保證金口徑）。在加密+槓桿下，先從 2%~6%（再結合槓桿換算）做網格測試。")
|
||
else:
|
||
lines.append("- 止损：建议设置 stopLossPct（按保证金口径）。在加密+杠杆下，先从 2%~6%（再结合杠杆换算）做网格测试。")
|
||
else:
|
||
if lang == "en-US":
|
||
lines.append(f"- Stop-loss: current stopLossPct={stop_loss:.4f} (margin basis). Test ±30% around it and monitor drawdown/liquidations.")
|
||
elif lang == "zh-TW":
|
||
lines.append(f"- 止損：目前 stopLossPct={stop_loss:.4f}（保證金口徑）。建議圍繞它做 ±30% 區間測試，並觀察回撤/爆倉次數變化。")
|
||
else:
|
||
lines.append(f"- 止损：当前 stopLossPct={stop_loss:.4f}（保证金口径）。建议围绕它做 ±30% 的区间测试，并观察回撤/爆仓次数变化。")
|
||
if take_profit > 0 and (not trailing_enabled):
|
||
if lang == "en-US":
|
||
lines.append(f"- Take-profit: current takeProfitPct={take_profit:.4f}. Also test enabling trailing to reduce profit giveback.")
|
||
elif lang == "zh-TW":
|
||
lines.append(f"- 止盈：目前 takeProfitPct={take_profit:.4f}。建議同時測試啟用移動止盈（trailing）以降低盈利回撤。")
|
||
else:
|
||
lines.append(f"- 止盈：当前 takeProfitPct={take_profit:.4f}。建议同时测试开启移动止盈（trailing）以降低盈利回撤。")
|
||
if trailing_enabled:
|
||
if lang == "en-US":
|
||
lines.append(f"- Trailing: enabled, pct={trailing_pct:.4f}, activationPct={trailing_act:.4f}. Set activation near typical winner PnL and test pct at 0.5x~1.5x.")
|
||
elif lang == "zh-TW":
|
||
lines.append(f"- 移動止盈：已啟用，pct={trailing_pct:.4f}, activationPct={trailing_act:.4f}。建議將 activationPct 設為略低於常見單筆盈利水平，並把 pct 做 0.5x~1.5x 測試。")
|
||
else:
|
||
lines.append(f"- 移动止盈：已启用，pct={trailing_pct:.4f}, activationPct={trailing_act:.4f}。建议把 activationPct 设为略低于常见单笔盈利水平，并把 pct 做 0.5x~1.5x 测试。")
|
||
else:
|
||
if lang == "en-US":
|
||
lines.append("- Trailing: consider trailing.enabled=true; start with pct=1%~3% (margin basis) and test.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 移動止盈：建議開啟 trailing.enabled=true，並從 pct=1%~3%（保證金口徑換算後）開始測試。")
|
||
else:
|
||
lines.append("- 移动止盈：建议开启 trailing.enabled=true，并从 pct=1%~3%（保证金口径换算后）开始测试。")
|
||
if lang == "en-US":
|
||
lines.append(f"- Entry sizing: entryPct={entry_pct:.4f}. Test 0.2/0.3/0.5/0.8 to find a better return/drawdown sweet spot.")
|
||
elif lang == "zh-TW":
|
||
lines.append(f"- 開倉倉位：目前 entryPct={entry_pct:.4f}。建議先用 0.2/0.3/0.5/0.8 分層回測，找收益/回撤更優的甜區。")
|
||
else:
|
||
lines.append(f"- 开仓仓位：当前 entryPct={entry_pct:.4f}。建议先用 0.2/0.3/0.5/0.8 做分层回测，找收益/回撤更优的甜区。")
|
||
|
||
# Scaling (very light guidance)
|
||
if isinstance(trend_add, dict) and trend_add.get("enabled"):
|
||
if lang == "en-US":
|
||
lines.append("- Trend scale-in: reduce sizePct or maxTimes to avoid drawdown expansion; verify same-bar conflict rules match expectations.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 順勢加倉：建議優先降低 sizePct 或 maxTimes，避免回撤擴大；並確認同K線主信號禁用加減倉規則符合預期。")
|
||
else:
|
||
lines.append("- 顺势加仓：建议优先降低 sizePct 或 maxTimes，避免回撤扩大；并确保同K线主信号禁用加减仓的规则与你预期一致。")
|
||
if isinstance(dca_add, dict) and dca_add.get("enabled"):
|
||
if lang == "en-US":
|
||
lines.append("- DCA scale-in: very risky under leverage; keep maxTimes small, sizePct low, and use stricter stop-loss.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 逆勢加倉：加密槓桿下風險極高，建議 maxTimes 更小、sizePct 更低，並採用更嚴格止損。")
|
||
else:
|
||
lines.append("- 逆势加仓：加密杠杆下风险极高，建议 maxTimes 更小、sizePct 更低，并强制更严格止损。")
|
||
if isinstance(trend_reduce, dict) and trend_reduce.get("enabled"):
|
||
if lang == "en-US":
|
||
lines.append("- Trend reduce: can lower volatility but may reduce returns; test together with trailing.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 順勢減倉：有助降低波動，但可能降低收益；建議搭配移動止盈一起做對比測試。")
|
||
else:
|
||
lines.append("- 顺势减仓：适合降低波动，但可能降低收益；建议和移动止盈一起对比测试。")
|
||
if isinstance(adverse_reduce, dict) and adverse_reduce.get("enabled"):
|
||
if lang == "en-US":
|
||
lines.append("- Adverse reduce: can control drawdowns but increases fees/slippage; consider enabling under higher leverage.")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 逆勢減倉：可用於控回撤，但可能增加手續費/滑點成本；建議優先在高槓桿時開啟。")
|
||
else:
|
||
lines.append("- 逆势减仓：可用于控回撤，但可能增加手续费/滑点成本；建议优先在高杠杆时开启。")
|
||
|
||
lines.append("\n" + h["next"])
|
||
if lang == "zh-CN":
|
||
lines.append("- 固定信号逻辑不变，只用参数做网格/分组测试（先粗再细）。每次只改 1~2 个参数，避免结论不可归因。")
|
||
lines.append("- 重点同时看：总收益、最大回撤、夏普、交易次数、爆仓/止损触发次数。")
|
||
elif lang == "zh-TW":
|
||
lines.append("- 固定信號邏輯不變，只用參數做網格/分組測試（先粗後細）。每次只改 1~2 個參數，避免結論不可歸因。")
|
||
lines.append("- 重點同時看：總收益、最大回撤、夏普、交易次數、爆倉/止損觸發次數。")
|
||
else:
|
||
# Keep English for other locales to ensure readability in fallback mode.
|
||
lines.append("- Keep signal logic fixed; run parameter grid tests (coarse → fine). Change only 1-2 params per run.")
|
||
lines.append("- Track: total return, max drawdown, Sharpe, trade count, liquidation/stop-loss triggers.")
|
||
return "\n".join(lines)
|
||
|
||
|
||
@backtest_bp.route('/backtest/aiAnalyze', methods=['POST'])
|
||
def ai_analyze_backtest_runs():
|
||
"""
|
||
AI analyze selected backtest runs and provide strategy_config tuning suggestions.
|
||
|
||
Params:
|
||
userid: User ID (default 1)
|
||
runIds: list[int] (required)
|
||
"""
|
||
try:
|
||
data = request.get_json() or {}
|
||
user_id = int(data.get('userid') or data.get('userId') or 1)
|
||
lang = _normalize_lang(data.get('lang'))
|
||
run_ids = data.get('runIds') or []
|
||
if not isinstance(run_ids, list) or not run_ids:
|
||
return jsonify({'code': 0, 'msg': 'runIds is required', 'data': None}), 400
|
||
|
||
# Limit to avoid huge prompts / payload.
|
||
run_ids = [int(x) for x in run_ids if str(x).strip().isdigit()]
|
||
run_ids = run_ids[:10]
|
||
if not run_ids:
|
||
return jsonify({'code': 0, 'msg': 'runIds is required', 'data': None}), 400
|
||
|
||
placeholders = ",".join(["?"] * len(run_ids))
|
||
with get_db_connection() as db:
|
||
cur = db.cursor()
|
||
cur.execute(
|
||
f"""
|
||
SELECT id, user_id, indicator_id, market, symbol, timeframe,
|
||
start_date, end_date, initial_capital, commission, slippage,
|
||
leverage, trade_direction, strategy_config, status, error_message,
|
||
result_json, created_at
|
||
FROM qd_backtest_runs
|
||
WHERE user_id = ? AND id IN ({placeholders})
|
||
ORDER BY id DESC
|
||
""",
|
||
(user_id, *run_ids),
|
||
)
|
||
rows = cur.fetchall() or []
|
||
cur.close()
|
||
|
||
runs: list[dict] = []
|
||
for r in rows:
|
||
try:
|
||
r['strategy_config'] = json.loads(r.get('strategy_config') or '{}')
|
||
except Exception:
|
||
r['strategy_config'] = {}
|
||
try:
|
||
r['result'] = json.loads(r.get('result_json') or '{}')
|
||
except Exception:
|
||
r['result'] = {}
|
||
r.pop('result_json', None)
|
||
runs.append(r)
|
||
|
||
if not runs:
|
||
return jsonify({'code': 0, 'msg': 'runs not found', 'data': None}), 404
|
||
|
||
# OpenRouter (optional)
|
||
base_url, api_key = _openrouter_base_and_key()
|
||
if not api_key:
|
||
analysis = _heuristic_ai_advice(runs, lang)
|
||
return jsonify({'code': 1, 'msg': 'OK', 'data': {'analysis': analysis, 'mode': 'heuristic', 'lang': lang}})
|
||
|
||
model = (os.getenv("OPENROUTER_MODEL", "openai/gpt-4o-mini") or "").strip() or "openai/gpt-4o-mini"
|
||
temperature = float(os.getenv("OPENROUTER_TEMPERATURE", "0.4") or 0.4)
|
||
|
||
output_lang_map = {
|
||
"zh-CN": "Simplified Chinese",
|
||
"zh-TW": "Traditional Chinese",
|
||
"en-US": "English",
|
||
"ko-KR": "Korean",
|
||
"th-TH": "Thai",
|
||
"vi-VN": "Vietnamese",
|
||
"ar-SA": "Arabic",
|
||
"de-DE": "German",
|
||
"fr-FR": "French",
|
||
"ja-JP": "Japanese",
|
||
}
|
||
output_lang = output_lang_map.get(lang, "English")
|
||
|
||
system_prompt = (
|
||
"You are an expert quantitative trading researcher specialized in crypto leveraged trading. "
|
||
"Your job is to analyze backtest configurations and results, then propose actionable parameter tuning suggestions. "
|
||
f"Output in {output_lang}. Be concise and practical. "
|
||
"Do NOT change indicator code logic. Focus on strategy_config parameters only: risk (stopLossPct/takeProfitPct/trailing), "
|
||
"position (entryPct), scale (trendAdd/dcaAdd/trendReduce/adverseReduce), execution assumptions. "
|
||
"Provide: (1) diagnosis, (2) recommended parameter ranges, (3) suggested A/B test plan (few steps). "
|
||
"Avoid investment advice language; focus on engineering/experimental recommendations."
|
||
)
|
||
|
||
user_payload = {
|
||
"selectedRuns": [
|
||
{
|
||
"id": r.get("id"),
|
||
"market": r.get("market"),
|
||
"symbol": r.get("symbol"),
|
||
"timeframe": r.get("timeframe"),
|
||
"start_date": r.get("start_date"),
|
||
"end_date": r.get("end_date"),
|
||
"leverage": r.get("leverage"),
|
||
"trade_direction": r.get("trade_direction"),
|
||
"strategy_config": r.get("strategy_config") or {},
|
||
"result": r.get("result") or {},
|
||
"status": r.get("status"),
|
||
}
|
||
for r in runs
|
||
]
|
||
}
|
||
|
||
resp = requests.post(
|
||
f"{base_url}/chat/completions",
|
||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||
json={
|
||
"model": model,
|
||
"temperature": temperature,
|
||
"stream": False,
|
||
"messages": [
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": json.dumps(user_payload, ensure_ascii=False)},
|
||
],
|
||
},
|
||
timeout=120,
|
||
)
|
||
try:
|
||
resp.raise_for_status()
|
||
j = resp.json()
|
||
content = (((j.get("choices") or [{}])[0]).get("message") or {}).get("content") or ""
|
||
analysis = content.strip()
|
||
if not analysis:
|
||
analysis = _heuristic_ai_advice(runs, lang)
|
||
return jsonify({'code': 1, 'msg': 'OK', 'data': {'analysis': analysis, 'mode': 'heuristic_fallback', 'lang': lang}})
|
||
return jsonify({'code': 1, 'msg': 'OK', 'data': {'analysis': analysis, 'mode': 'llm', 'lang': lang}})
|
||
except requests.exceptions.RequestException as e:
|
||
# Do not fail the whole endpoint if LLM provider is misconfigured or rate-limited.
|
||
logger.error(f"OpenRouter request failed, falling back to heuristic: {e}")
|
||
analysis = _heuristic_ai_advice(runs, lang)
|
||
return jsonify(
|
||
{
|
||
'code': 1,
|
||
'msg': 'OK',
|
||
'data': {
|
||
'analysis': analysis,
|
||
'mode': 'heuristic_fallback',
|
||
'lang': lang,
|
||
'llmError': str(e),
|
||
},
|
||
}
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"ai_analyze_backtest_runs failed: {e}")
|
||
logger.error(traceback.format_exc())
|
||
return jsonify({'code': 0, 'msg': str(e), 'data': None}), 500
|
||
|