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Restructure into modules/ with single-process CTP and config/ layout.

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Move business code under modules/, env template to config/, PM2 single qihuo process, and _legacy shims for old imports.

Co-authored-by: Cursor <cursoragent@cursor.com>
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dekun
2026-07-01 14:42:16 +08:00
parent b354d6c701
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modules/stats/stats_engine.py
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# Copyright (c) 2025-2026 马建军. All rights reserved.
# 专有软件 — 未经授权禁止复制、传播、转售。
# 严禁用于：带单/代客理财、向他人推荐期货品种或买卖建议、融资配资等业务。
# 详见 LICENSE.zh-CN.txt 与 docs/软件购买与使用协议.md
"""交易统计计算与缓存结构。"""
from __future__ import annotations
import calendar
import json
import threading
from datetime import date, datetime
from typing import Any, Optional
from zoneinfo import ZoneInfo
from modules.core.db_conn import commit_retry, execute_retry
_stats_refresh_lock = threading.Lock()
TZ = ZoneInfo("Asia/Shanghai")
STATS_VIEWS = [
{"key": "by_time", "label": "按时间统计"},
{"key": "by_week", "label": "周统计"},
{"key": "by_month", "label": "月统计"},
{"key": "by_symbol", "label": "按品种统计"},
{"key": "by_fee", "label": "按手续费统计"},
{"key": "by_direction", "label": "按方向统计"},
{"key": "by_trade_type", "label": "按交易类型统计"},
{"key": "by_emotion", "label": "情绪单统计"},
]
BREAKDOWN_COLUMNS = [
{"key": "label", "label": "维度"},
{"key": "count", "label": "交易次数"},
{"key": "wins", "label": "盈利笔数"},
{"key": "losses", "label": "亏损笔数"},
{"key": "win_rate", "label": "胜率(%)"},
{"key": "avg_profit", "label": "平均盈利"},
{"key": "avg_loss", "label": "平均亏损"},
{"key": "profit_loss_ratio", "label": "盈亏比"},
{"key": "total_fee", "label": "累计手续费"},
{"key": "total_net", "label": "净盈亏合计"},
{"key": "max_loss", "label": "最大亏损"},
{"key": "max_profit", "label": "最大盈利"},
]
def _parse_dt(value: str) -> Optional[datetime]:
if not value:
return None
text = value.strip().replace(" ", "T")
try:
return datetime.fromisoformat(text)
except ValueError:
return None
def _row_dict(row) -> dict:
return dict(row) if row is not None else {}
def _net_pnl(row: dict) -> float:
if row.get("pnl_net") is not None:
return float(row["pnl_net"])
pnl = float(row.get("pnl") or 0)
fee = float(row.get("fee") or 0)
return round(pnl - fee, 2)
def _fee(row: dict) -> float:
return float(row.get("fee") or 0)
def _margin_pct(pnl_net: float, margin: Optional[float]) -> Optional[float]:
if margin and margin > 0:
return round(pnl_net / margin * 100, 2)
return None
def _agg_group(rows: list[dict], key_fn) -> list[dict]:
groups: dict[str, list[dict]] = {}
for row in rows:
key = key_fn(row) or "未知"
groups.setdefault(key, []).append(row)
result = []
for label, items in sorted(groups.items(), key=lambda x: x[0]):
result.append(_agg_metrics(label, items))
return result
def _agg_metrics(label: str, items: list[dict]) -> dict:
nets = [_net_pnl(r) for r in items]
wins = [n for n in nets if n > 0]
losses = [n for n in nets if n < 0]
count = len(items)
win_cnt = len(wins)
loss_cnt = len(losses)
avg_profit = round(sum(wins) / len(wins), 2) if wins else 0.0
avg_loss = round(sum(losses) / len(losses), 2) if losses else 0.0
pl_ratio = round(avg_profit / abs(avg_loss), 2) if wins and losses and avg_loss != 0 else 0.0
total_fee = round(sum(_fee(r) for r in items), 2)
total_net = round(sum(nets), 2)
max_loss = round(min(losses), 2) if losses else 0.0
max_profit = round(max(wins), 2) if wins else 0.0
win_rate = round(win_cnt / count * 100, 2) if count else 0.0
return {
"label": label,
"count": count,
"wins": win_cnt,
"losses": loss_cnt,
"win_rate": win_rate,
"avg_profit": avg_profit,
"avg_loss": avg_loss,
"profit_loss_ratio": pl_ratio,
"total_fee": total_fee,
"total_net": total_net,
"max_loss": max_loss,
"max_profit": max_profit,
}
def _max_consecutive_losses(nets: list[float]) -> int:
streak = 0
best = 0
for n in nets:
if n < 0:
streak += 1
best = max(best, streak)
else:
streak = 0
return best
def _max_drawdown(nets: list[float], initial_capital: float) -> tuple[float, float]:
equity = initial_capital
peak = initial_capital
max_dd = 0.0
max_dd_pct = 0.0
for n in nets:
equity += n
if equity > peak:
peak = equity
dd = peak - equity
if dd > max_dd:
max_dd = dd
if peak > 0:
pct = dd / peak * 100
if pct > max_dd_pct:
max_dd_pct = pct
return round(max_dd, 2), round(max_dd_pct, 2)
def fetch_trade_rows(conn) -> list[dict]:
rows = conn.execute(
"SELECT * FROM trade_logs ORDER BY close_time ASC, id ASC"
).fetchall()
return [_row_dict(r) for r in rows]
def fetch_review_rows(conn) -> list[dict]:
rows = conn.execute(
"SELECT * FROM review_records ORDER BY close_time ASC, id ASC"
).fetchall()
return [_row_dict(r) for r in rows]
def compute_summary(trades: list[dict], reviews: list[dict], live_capital: float) -> dict:
nets = [_net_pnl(t) for t in trades]
count = len(trades)
wins = [n for n in nets if n > 0]
losses = [n for n in nets if n < 0]
win_cnt = len(wins)
loss_cnt = len(losses)
avg_profit = round(sum(wins) / len(wins), 2) if wins else 0.0
avg_loss = round(sum(losses) / len(losses), 2) if losses else 0.0
pl_ratio = round(avg_profit / abs(avg_loss), 2) if wins and losses and avg_loss != 0 else 0.0
total_fee = round(sum(_fee(t) for t in trades) + sum(_fee(r) for r in reviews), 2)
max_loss_amt = round(min(losses), 2) if losses else 0.0
max_profit_amt = round(max(wins), 2) if wins else 0.0
margins_loss = [
_margin_pct(_net_pnl(t), t.get("margin"))
for t in trades
if _net_pnl(t) < 0 and t.get("margin")
]
margins_profit = [
_margin_pct(_net_pnl(t), t.get("margin"))
for t in trades
if _net_pnl(t) > 0 and t.get("margin")
]
max_loss_pct = round(min(margins_loss), 2) if margins_loss else 0.0
max_profit_pct = round(max(margins_profit), 2) if margins_profit else 0.0
consec_loss = _max_consecutive_losses(nets)
max_dd, max_dd_pct = _max_drawdown(nets, live_capital)
emotion_cnt = sum(1 for r in reviews if r.get("is_emotion"))
review_cnt = len(reviews)
denom = count if count else review_cnt
emotion_ratio = round(emotion_cnt / denom * 100, 2) if denom else 0.0
return {
"total_trades": count,
"win_rate": round(win_cnt / count * 100, 2) if count else 0.0,
"avg_profit": avg_profit,
"avg_loss": avg_loss,
"profit_loss_ratio": pl_ratio,
"consecutive_losses": consec_loss,
"max_drawdown": max_dd,
"max_drawdown_pct": max_dd_pct,
"max_loss_amount": max_loss_amt,
"max_loss_pct": max_loss_pct,
"max_profit_amount": max_profit_amt,
"max_profit_pct": max_profit_pct,
"total_fee": total_fee,
"emotion_count": emotion_cnt,
"emotion_ratio": emotion_ratio,
"review_count": review_cnt,
"win_count": win_cnt,
"loss_count": loss_cnt,
}
def compute_breakdowns(trades: list[dict], reviews: list[dict]) -> dict[str, dict]:
def day_key(row: dict) -> str:
dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
return dt.date().isoformat() if dt else "未知"
def week_key(row: dict) -> str:
dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
if not dt:
return "未知"
iso = dt.isocalendar()
return f"{iso.year}-W{iso.week:02d}"
def month_key(row: dict) -> str:
dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
return dt.strftime("%Y-%m") if dt else "未知"
def symbol_key(row: dict) -> str:
return row.get("symbol_name") or row.get("symbol") or "未知"
def direction_key(row: dict) -> str:
d = row.get("direction") or ""
return "做多" if d == "long" else ("做空" if d == "short" else d or "未知")
def type_key(row: dict) -> str:
return row.get("monitor_type") or "未知"
by_fee_rows = []
fee_groups = {}
for t in trades:
key = symbol_key(t)
fee_groups.setdefault(key, []).append(t)
for label, items in sorted(fee_groups.items()):
row = _agg_metrics(label, items)
row["avg_fee"] = round(row["total_fee"] / row["count"], 2) if row["count"] else 0.0
by_fee_rows.append(row)
emotion_trades = [r for r in reviews if r.get("is_emotion")]
non_emotion = [r for r in reviews if not r.get("is_emotion")]
emotion_rows = [
_agg_metrics("情绪单", emotion_trades),
_agg_metrics("非情绪单", non_emotion),
]
fee_columns = BREAKDOWN_COLUMNS + [{"key": "avg_fee", "label": "平均手续费"}]
return {
"by_time": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, day_key)},
"by_week": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, week_key)},
"by_month": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, month_key)},
"by_symbol": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, symbol_key)},
"by_fee": {"columns": fee_columns, "rows": by_fee_rows},
"by_direction": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, direction_key)},
"by_trade_type": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, type_key)},
"by_emotion": {"columns": BREAKDOWN_COLUMNS, "rows": emotion_rows},
}
def build_all_stats(conn, live_capital: float = 0.0) -> dict:
trades = fetch_trade_rows(conn)
reviews = fetch_review_rows(conn)
summary = compute_summary(trades, reviews, live_capital)
breakdowns = compute_breakdowns(trades, reviews)
return {
"updated_at": datetime.now(TZ).isoformat(timespec="seconds"),
"summary": summary,
"views": STATS_VIEWS,
"breakdowns": breakdowns,
}
def save_stats_cache(conn, data: dict) -> None:
execute_retry(
conn,
"""INSERT INTO stats_cache (key, data_json, updated_at)
VALUES ('all', ?, ?)
ON CONFLICT(key) DO UPDATE SET data_json=excluded.data_json, updated_at=excluded.updated_at""",
(json.dumps(data, ensure_ascii=False), data["updated_at"]),
)
commit_retry(conn)
def load_stats_cache(conn) -> Optional[dict]:
row = conn.execute(
"SELECT data_json FROM stats_cache WHERE key='all'"
).fetchone()
if not row:
return None
try:
return json.loads(row["data_json"])
except json.JSONDecodeError:
return None
def refresh_stats_cache(conn, live_capital: float = 0.0) -> dict:
with _stats_refresh_lock:
data = build_all_stats(conn, live_capital)
save_stats_cache(conn, data)
return data
def _norm_symbol(symbol: str) -> str:
s = (symbol or "").strip().lower()
if "." in s:
s = s.split(".")[0]
return s
def _close_day_key(row: dict) -> str:
dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
return dt.date().isoformat() if dt else ""
def _close_ts(row: dict) -> float:
dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
return dt.timestamp() if dt else 0.0
def _direction_label(direction: str) -> str:
if direction == "long":
return "做多"
if direction == "short":
return "做空"
return direction or ""
def _index_reviews_by_day_sym(reviews: list[dict]) -> dict[tuple[str, str], list[dict]]:
index: dict[tuple[str, str], list[dict]] = {}
for review in reviews:
day = _close_day_key(review)
if not day:
continue
sym = _norm_symbol(review.get("symbol") or "")
index.setdefault((day, sym), []).append(review)
return index
def _review_match_score(trade: dict, review: dict) -> float:
score = abs(_close_ts(trade) - _close_ts(review))
lots_t = trade.get("lots")
lots_r = review.get("lots")
if lots_t is not None and lots_r is not None and float(lots_t) != float(lots_r):
score += 86400.0
entry_t = trade.get("entry_price")
entry_r = review.get("entry_price")
if entry_t is not None and entry_r is not None and abs(float(entry_t) - float(entry_r)) > 0.01:
score += 3600.0
return score
def _find_review_for_trade(
trade: dict,
review_index: dict[tuple[str, str], list[dict]],
used_review_ids: set[int],
) -> Optional[dict]:
day = _close_day_key(trade)
sym = _norm_symbol(trade.get("symbol") or "")
candidates = [
r for r in review_index.get((day, sym), [])
if r.get("id") not in used_review_ids
]
if not candidates:
return None
return min(candidates, key=lambda r: _review_match_score(trade, r))
def _format_day_entry(
*,
trade: Optional[dict] = None,
review: Optional[dict] = None,
source: str,
) -> dict:
row = review if source == "review" and review else trade or review or {}
symbol = row.get("symbol") or ""
pnl_net = _net_pnl(row)
tags = (row.get("behavior_tags") or "").strip()
is_emotion = bool(row.get("is_emotion"))
return {
"source": source,
"trade_id": trade.get("id") if trade else None,
"review_id": review.get("id") if review else None,
"symbol": row.get("symbol_name") or symbol,
"symbol_code": symbol,
"direction": _direction_label(row.get("direction") or ""),
"lots": row.get("lots"),
"entry_price": row.get("entry_price"),
"close_price": row.get("close_price"),
"stop_loss": row.get("stop_loss"),
"take_profit": row.get("take_profit"),
"open_time": row.get("open_time") or "",
"close_time": row.get("close_time") or "",
"pnl": row.get("pnl"),
"fee": row.get("fee"),
"pnl_net": pnl_net,
"result": row.get("result") if trade else None,
"monitor_type": row.get("monitor_type") if trade else None,
"is_emotion": is_emotion,
"behavior_tags": tags,
"open_type": row.get("open_type") if review else None,
"exit_trigger": row.get("exit_trigger") if review else None,
"exit_supplement": row.get("exit_supplement") if review else None,
"holding_duration": row.get("holding_duration") if review else None,
"initial_pnl": row.get("initial_pnl") if review else None,
"actual_pnl": row.get("actual_pnl") if review else None,
"timeframe": row.get("timeframe") if review else None,
"notes": row.get("notes") if review else None,
"screenshot": row.get("screenshot") if review else None,
}
def build_day_detail(trades: list[dict], reviews: list[dict], day: str) -> list[dict]:
day_trades = [t for t in trades if _close_day_key(t) == day]
day_reviews = [r for r in reviews if _close_day_key(r) == day]
review_index = _index_reviews_by_day_sym(day_reviews)
used_review_ids: set[int] = set()
items: list[dict] = []
for trade in day_trades:
review = _find_review_for_trade(trade, review_index, used_review_ids)
if review:
used_review_ids.add(int(review["id"]))
items.append(_format_day_entry(trade=trade, review=review, source="review"))
else:
items.append(_format_day_entry(trade=trade, source="trade"))
for review in day_reviews:
if int(review.get("id") or 0) in used_review_ids:
continue
items.append(_format_day_entry(review=review, source="review"))
items.sort(key=lambda x: _close_ts(x), reverse=True)
return items
def build_calendar_month(trades: list[dict], reviews: list[dict], year: int, month: int) -> dict:
review_index = _index_reviews_by_day_sym(reviews)
day_map: dict[str, dict] = {}
matched_review_ids: dict[str, set[int]] = {}
for trade in trades:
dt = _parse_dt(trade.get("close_time") or "")
if not dt or dt.year != year or dt.month != month:
continue
day = dt.date().isoformat()
bucket = day_map.setdefault(
day,
{
"date": day,
"count": 0,
"total_net": 0.0,
"review_count": 0,
"emotion_count": 0,
"has_emotion": False,
},
)
bucket["count"] += 1
used = matched_review_ids.setdefault(day, set())
review = _find_review_for_trade(trade, review_index, used)
if review:
rid = int(review["id"])
used.add(rid)
bucket["total_net"] = round(bucket["total_net"] + _net_pnl(review), 2)
bucket["review_count"] += 1
if review.get("is_emotion"):
bucket["emotion_count"] += 1
bucket["has_emotion"] = True
else:
bucket["total_net"] = round(bucket["total_net"] + _net_pnl(trade), 2)
for review in reviews:
if not review.get("is_emotion"):
continue
day = _close_day_key(review)
if not day:
continue
try:
dt = date.fromisoformat(day)
except ValueError:
continue
if dt.year != year or dt.month != month:
continue
bucket = day_map.setdefault(
day,
{
"date": day,
"count": 0,
"total_net": 0.0,
"review_count": 0,
"emotion_count": 0,
"has_emotion": False,
},
)
bucket["has_emotion"] = True
rid = int(review.get("id") or 0)
if rid and rid not in matched_review_ids.get(day, set()):
bucket["emotion_count"] += 1
_, last_day = calendar.monthrange(year, month)
days = []
for d in range(1, last_day + 1):
iso = date(year, month, d).isoformat()
if iso in day_map:
row = day_map[iso]
row["total_net"] = round(row["total_net"], 2)
days.append(row)
else:
days.append(
{
"date": iso,
"count": 0,
"total_net": 0.0,
"review_count": 0,
"emotion_count": 0,
"has_emotion": False,
}
)
return {
"year": year,
"month": month,
"days": days,
"weekday_start": date(year, month, 1).weekday(),
}
def get_calendar_month(conn, year: int, month: int) -> dict:
trades = fetch_trade_rows(conn)
reviews = fetch_review_rows(conn)
return build_calendar_month(trades, reviews, year, month)
def get_calendar_day(conn, day: str) -> dict:
trades = fetch_trade_rows(conn)
reviews = fetch_review_rows(conn)
items = build_day_detail(trades, reviews, day)
total_net = round(sum(float(i.get("pnl_net") or 0) for i in items), 2)
emotion_count = sum(1 for i in items if i.get("is_emotion"))
return {
"date": day,
"count": len(items),
"total_net": total_net,
"emotion_count": emotion_count,
"items": items,
}