重构统计分析页：汇总指标、分项下拉与后台缓存
新增 stats_engine 与 stats_cache，提供 API 自动加载 8 种统计维度；交易与复盘变更时自动刷新缓存。 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
dekun
@@ -29,6 +29,7 @@ from fee_specs import (
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)
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from fee_sync import sync_fees_from_akshare
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from contract_profile import get_contract_profile
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from stats_engine import STATS_VIEWS, load_stats_cache, refresh_stats_cache
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from kline_chart import generate_review_kline_chart
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from market import get_price as market_get_price, set_ths_refresh_token, get_quote_source_label
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@@ -174,6 +175,26 @@ def set_setting(key: str, value: str):
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conn.close()
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def touch_stats_cache():
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try:
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conn = get_db()
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capital = float(get_setting("live_capital", "0") or 0)
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refresh_stats_cache(conn, capital)
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conn.close()
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except Exception as exc:
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app.logger.warning("stats cache refresh failed: %s", exc)
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def get_stats_data() -> dict:
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conn = get_db()
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capital = float(get_setting("live_capital", "0") or 0)
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data = load_stats_cache(conn)
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if not data:
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data = refresh_stats_cache(conn, capital)
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conn.close()
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return data
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def init_db():
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conn = get_db()
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c = conn.cursor()
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@@ -278,6 +299,10 @@ def init_db():
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close_today_fixed REAL DEFAULT 0,
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close_today_ratio REAL DEFAULT 0,
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updated_at TEXT)''')
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c.execute('''CREATE TABLE IF NOT EXISTS stats_cache
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(key TEXT PRIMARY KEY,
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data_json TEXT NOT NULL,
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updated_at TEXT NOT NULL)''')
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conn.commit()
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conn.close()
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@@ -904,6 +929,7 @@ def close_position(pid):
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conn.execute("DELETE FROM position_monitors WHERE id=?", (pid,))
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conn.commit()
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conn.close()
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touch_stats_cache()
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flash(f"已平仓，盈亏 {pnl:.2f} 元（扣费后 {pnl_net:.2f} 元），已记入交易记录")
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return redirect(url_for("positions"))
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@@ -946,6 +972,7 @@ def update_trade(tid):
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)
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conn.commit()
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conn.close()
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touch_stats_cache()
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flash("交易记录已核对保存")
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return redirect(url_for("records"))
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@@ -957,6 +984,7 @@ def del_trade(tid):
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conn.execute("DELETE FROM trade_logs WHERE id=?", (tid,))
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conn.commit()
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conn.close()
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touch_stats_cache()
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flash("已删除")
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return redirect(url_for("records"))
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@@ -1179,6 +1207,7 @@ def add_review():
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)
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conn.commit()
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conn.close()
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touch_stats_cache()
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flash("复盘记录已保存")
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return redirect(url_for("records"))
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@@ -1195,6 +1224,7 @@ def del_review(rid):
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conn.execute("DELETE FROM review_records WHERE id=?", (rid,))
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conn.commit()
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conn.close()
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touch_stats_cache()
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flash("已删除")
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return redirect(url_for("records"))
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@@ -1220,76 +1250,29 @@ def del_record(rid):
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@app.route("/stats")
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@login_required
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def stats():
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return render_template("stats.html")
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@app.route("/api/stats")
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@login_required
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def api_stats():
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return jsonify(get_stats_data())
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@app.route("/api/stats/views")
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@login_required
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def api_stats_views():
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return jsonify({"views": STATS_VIEWS})
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@app.route("/api/stats/refresh", methods=["POST"])
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@login_required
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def api_stats_refresh():
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conn = get_db()
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total = conn.execute(
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"SELECT COUNT(*) FROM trade_records WHERE result IN ('止盈','止损')"
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).fetchone()[0]
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win = conn.execute(
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"SELECT COUNT(*) FROM trade_records WHERE result='止盈'"
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).fetchone()[0]
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loss = conn.execute(
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"SELECT COUNT(*) FROM trade_records WHERE result='止损'"
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).fetchone()[0]
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rate = round(win / total * 100, 2) if total else 0
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by_symbol = conn.execute(
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"""SELECT symbol_name, symbol, COUNT(*) as cnt,
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SUM(CASE WHEN result='止盈' THEN 1 ELSE 0 END) as wins
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FROM trade_records WHERE result IN ('止盈','止损')
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GROUP BY symbol ORDER BY cnt DESC"""
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).fetchall()
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by_type = conn.execute(
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"""SELECT monitor_type, COUNT(*) as cnt,
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SUM(CASE WHEN result='止盈' THEN 1 ELSE 0 END) as wins
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FROM trade_records WHERE result IN ('止盈','止损')
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GROUP BY monitor_type ORDER BY cnt DESC"""
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).fetchall()
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by_direction = conn.execute(
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"""SELECT direction, COUNT(*) as cnt,
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SUM(CASE WHEN result='止盈' THEN 1 ELSE 0 END) as wins
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FROM trade_records WHERE result IN ('止盈','止损')
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GROUP BY direction"""
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).fetchall()
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recent = conn.execute(
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"SELECT * FROM trade_logs ORDER BY id DESC LIMIT 10"
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).fetchall()
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fee_trade = conn.execute(
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"SELECT COALESCE(SUM(fee),0), COALESCE(SUM(pnl),0), COALESCE(SUM(pnl_net),0), COUNT(*) "
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"FROM trade_logs WHERE fee IS NOT NULL"
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).fetchone()
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fee_review = conn.execute(
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"SELECT COALESCE(SUM(fee),0), COALESCE(SUM(pnl),0), COALESCE(SUM(pnl_net),0), COUNT(*) "
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"FROM review_records WHERE fee IS NOT NULL"
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).fetchone()
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total_fee = round((fee_trade[0] or 0) + (fee_review[0] or 0), 2)
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total_gross = round((fee_trade[1] or 0) + (fee_review[1] or 0), 2)
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total_net = round((fee_trade[2] or 0) + (fee_review[2] or 0), 2)
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fee_count = (fee_trade[3] or 0) + (fee_review[3] or 0)
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fee_by_symbol = conn.execute(
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"""SELECT symbol_name, symbol,
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COALESCE(SUM(fee),0) as total_fee,
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COUNT(*) as cnt
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FROM trade_logs WHERE fee IS NOT NULL
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GROUP BY symbol ORDER BY total_fee DESC LIMIT 20"""
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).fetchall()
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capital = float(get_setting("live_capital", "0") or 0)
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data = refresh_stats_cache(conn, capital)
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conn.close()
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return render_template(
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"stats.html",
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total=total, win=win, loss=loss, rate=rate,
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by_symbol=by_symbol, by_type=by_type, by_direction=by_direction,
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recent=recent,
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total_fee=total_fee,
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total_gross=total_gross,
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total_net=total_net,
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fee_count=fee_count,
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fee_by_symbol=fee_by_symbol,
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)
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return jsonify(data)
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@app.route("/contract")
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@@ -0,0 +1,173 @@
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(function () {
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var cache = null;
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function fmtNum(v, suffix) {
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if (v === null || v === undefined || v === '') return '-';
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var n = Number(v);
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if (isNaN(n)) return String(v);
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var s = Number.isInteger(n) ? String(n) : n.toFixed(2);
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return suffix ? s + suffix : s;
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}
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function fmtMoney(v) {
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if (v === null || v === undefined) return '-';
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return fmtNum(v) + ' 元';
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}
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function fmtPct(v) {
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if (v === null || v === undefined) return '-';
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return fmtNum(v) + '%';
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}
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function setSummary(s) {
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var map = {
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total_trades: function () { return fmtNum(s.total_trades); },
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win_rate: function () { return fmtPct(s.win_rate); },
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avg_profit: function () { return fmtMoney(s.avg_profit); },
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avg_loss: function () { return fmtMoney(s.avg_loss); },
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profit_loss_ratio: function () { return fmtNum(s.profit_loss_ratio); },
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consecutive_losses: function () { return fmtNum(s.consecutive_losses); },
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max_drawdown: function () {
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var amt = fmtMoney(s.max_drawdown);
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var pct = s.max_drawdown_pct ? ' (' + fmtPct(s.max_drawdown_pct) + ')' : '';
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return amt + pct;
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},
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max_loss_amount: function () { return fmtMoney(s.max_loss_amount); },
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max_loss_pct: function () { return fmtPct(s.max_loss_pct); },
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max_profit_amount: function () { return fmtMoney(s.max_profit_amount); },
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max_profit_pct: function () { return fmtPct(s.max_profit_pct); },
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total_fee: function () { return fmtMoney(s.total_fee); },
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emotion_count: function () { return fmtNum(s.emotion_count); },
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emotion_ratio: function () { return fmtPct(s.emotion_ratio); },
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};
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document.querySelectorAll('#stats-summary [data-k]').forEach(function (el) {
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var key = el.getAttribute('data-k');
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el.textContent = map[key] ? map[key]() : '-';
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});
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}
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function fillViewSelect(views, selected) {
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var sel = document.getElementById('stats-view-select');
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if (!sel) return;
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sel.innerHTML = '';
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views.forEach(function (v) {
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var opt = document.createElement('option');
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opt.value = v.key;
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opt.textContent = v.label;
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if (v.key === selected) opt.selected = true;
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sel.appendChild(opt);
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});
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}
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function cellClass(key, val) {
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if (key === 'total_net' || key === 'max_profit' || key === 'avg_profit') {
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if (val > 0) return 'text-profit';
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if (val < 0) return 'text-loss';
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}
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if (key === 'max_loss' || key === 'avg_loss' || key === 'total_fee') {
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return 'text-loss';
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}
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return '';
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}
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function renderBreakdown(key) {
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if (!cache || !cache.breakdowns) return;
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var block = cache.breakdowns[key];
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var head = document.getElementById('stats-breakdown-head');
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var body = document.getElementById('stats-breakdown-body');
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if (!block || !head || !body) return;
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head.innerHTML = '';
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block.columns.forEach(function (col) {
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var th = document.createElement('th');
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th.textContent = col.label;
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head.appendChild(th);
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});
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body.innerHTML = '';
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if (!block.rows || !block.rows.length) {
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var tr = document.createElement('tr');
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var td = document.createElement('td');
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td.colSpan = block.columns.length;
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td.className = 'text-muted';
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td.textContent = '暂无数据';
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tr.appendChild(td);
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body.appendChild(tr);
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return;
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}
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block.rows.forEach(function (row) {
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var tr = document.createElement('tr');
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block.columns.forEach(function (col) {
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var td = document.createElement('td');
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var val = row[col.key];
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if (col.key === 'win_rate') {
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td.textContent = fmtPct(val);
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} else if (col.key === 'label') {
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td.textContent = val || '-';
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} else if (typeof val === 'number') {
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td.textContent = fmtNum(val);
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td.className = cellClass(col.key, val);
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} else {
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td.textContent = val != null ? val : '-';
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}
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tr.appendChild(td);
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});
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body.appendChild(tr);
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});
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}
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function applyData(data) {
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cache = data;
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setSummary(data.summary || {});
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var views = data.views || [];
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var sel = document.getElementById('stats-view-select');
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var current = sel && sel.value ? sel.value : (views[0] && views[0].key);
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fillViewSelect(views, current);
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renderBreakdown(current);
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var updated = document.getElementById('stats-updated');
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if (updated) {
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updated.textContent = data.updated_at
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? '统计更新于 ' + data.updated_at.replace('T', ' ')
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: '统计已加载';
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}
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}
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function loadStats() {
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fetch('/api/stats')
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.then(function (r) { return r.json(); })
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.then(applyData)
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.catch(function () {
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var updated = document.getElementById('stats-updated');
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if (updated) updated.textContent = '加载失败，请刷新页面';
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});
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}
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document.addEventListener('DOMContentLoaded', function () {
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var viewSel = document.getElementById('stats-view-select');
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var refreshBtn = document.getElementById('stats-refresh-btn');
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if (viewSel) {
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viewSel.addEventListener('change', function () {
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renderBreakdown(this.value);
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});
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}
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if (refreshBtn) {
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refreshBtn.addEventListener('click', function () {
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var btn = this;
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btn.disabled = true;
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btn.textContent = '计算中…';
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fetch('/api/stats/refresh', { method: 'POST' })
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.then(function (r) { return r.json(); })
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.then(applyData)
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.catch(function () {
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alert('重新计算失败');
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})
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.finally(function () {
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btn.disabled = false;
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btn.textContent = '重新计算';
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});
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});
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}
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loadStats();
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});
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})();
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+310
@@ -0,0 +1,310 @@
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"""交易统计计算与缓存结构。"""
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from __future__ import annotations
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import json
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from datetime import datetime
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from typing import Any, Optional
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from zoneinfo import ZoneInfo
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TZ = ZoneInfo("Asia/Shanghai")
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STATS_VIEWS = [
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{"key": "by_time", "label": "按时间统计"},
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{"key": "by_week", "label": "周统计"},
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{"key": "by_month", "label": "月统计"},
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{"key": "by_symbol", "label": "按品种统计"},
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{"key": "by_fee", "label": "按手续费统计"},
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{"key": "by_direction", "label": "按方向统计"},
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{"key": "by_trade_type", "label": "按交易类型统计"},
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{"key": "by_emotion", "label": "情绪单统计"},
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]
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BREAKDOWN_COLUMNS = [
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{"key": "label", "label": "维度"},
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{"key": "count", "label": "交易次数"},
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{"key": "wins", "label": "盈利笔数"},
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{"key": "losses", "label": "亏损笔数"},
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{"key": "win_rate", "label": "胜率(%)"},
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{"key": "avg_profit", "label": "平均盈利"},
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{"key": "avg_loss", "label": "平均亏损"},
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{"key": "profit_loss_ratio", "label": "盈亏比"},
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{"key": "total_fee", "label": "累计手续费"},
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{"key": "total_net", "label": "净盈亏合计"},
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{"key": "max_loss", "label": "最大亏损"},
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{"key": "max_profit", "label": "最大盈利"},
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]
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def _parse_dt(value: str) -> Optional[datetime]:
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if not value:
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return None
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text = value.strip().replace(" ", "T")
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try:
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return datetime.fromisoformat(text)
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except ValueError:
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return None
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def _row_dict(row) -> dict:
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return dict(row) if row is not None else {}
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def _net_pnl(row: dict) -> float:
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if row.get("pnl_net") is not None:
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return float(row["pnl_net"])
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pnl = float(row.get("pnl") or 0)
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fee = float(row.get("fee") or 0)
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return round(pnl - fee, 2)
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def _fee(row: dict) -> float:
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return float(row.get("fee") or 0)
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def _margin_pct(pnl_net: float, margin: Optional[float]) -> Optional[float]:
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if margin and margin > 0:
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return round(pnl_net / margin * 100, 2)
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return None
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def _agg_group(rows: list[dict], key_fn) -> list[dict]:
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groups: dict[str, list[dict]] = {}
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for row in rows:
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key = key_fn(row) or "未知"
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groups.setdefault(key, []).append(row)
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result = []
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for label, items in sorted(groups.items(), key=lambda x: x[0]):
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result.append(_agg_metrics(label, items))
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return result
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def _agg_metrics(label: str, items: list[dict]) -> dict:
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nets = [_net_pnl(r) for r in items]
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wins = [n for n in nets if n > 0]
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losses = [n for n in nets if n < 0]
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count = len(items)
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win_cnt = len(wins)
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loss_cnt = len(losses)
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avg_profit = round(sum(wins) / len(wins), 2) if wins else 0.0
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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(nets), 2) if nets else 0.0
|
||||
max_profit = round(max(nets), 2) if nets 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(nets), 2) if nets else 0.0
|
||||
max_profit_amt = round(max(nets), 2) if nets 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:
|
||||
conn.execute(
|
||||
"""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"]),
|
||||
)
|
||||
conn.commit()
|
||||
|
||||
|
||||
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:
|
||||
data = build_all_stats(conn, live_capital)
|
||||
save_stats_cache(conn, data)
|
||||
return data
|
||||
+47
-111
@@ -2,122 +2,58 @@
|
||||
{% block title %}统计分析 - 国内期货监控系统{% endblock %}
|
||||
{% block content %}
|
||||
|
||||
<div class="stat-grid">
|
||||
<div class="stat-item"><div class="label">总交易</div><div class="value">{{ total }}</div></div>
|
||||
<div class="stat-item"><div class="label">止盈</div><div class="value text-profit">{{ win }}</div></div>
|
||||
<div class="stat-item"><div class="label">止损</div><div class="value text-loss">{{ loss }}</div></div>
|
||||
<div class="stat-item"><div class="label">胜率</div><div class="value">{{ rate }}%</div></div>
|
||||
<div class="stats-toolbar">
|
||||
<span id="stats-updated" class="hint">正在加载统计…</span>
|
||||
<button type="button" class="btn-secondary" id="stats-refresh-btn">重新计算</button>
|
||||
</div>
|
||||
|
||||
<div class="stat-grid">
|
||||
<div class="stat-item"><div class="label">累计手续费</div><div class="value text-loss">{{ total_fee }} 元</div></div>
|
||||
<div class="stat-item"><div class="label">毛盈亏合计</div><div class="value">{{ total_gross }} 元</div></div>
|
||||
<div class="stat-item"><div class="label">净盈亏合计</div><div class="value {% if total_net > 0 %}text-profit{% elif total_net < 0 %}text-loss{% endif %}">{{ total_net }} 元</div></div>
|
||||
<div class="stat-item"><div class="label">计费笔数</div><div class="value">{{ fee_count }}</div></div>
|
||||
<div class="stat-grid stat-grid-summary" id="stats-summary">
|
||||
<div class="stat-item"><div class="label">总交易次数</div><div class="value" data-k="total_trades">-</div></div>
|
||||
<div class="stat-item"><div class="label">胜率</div><div class="value" data-k="win_rate">-</div></div>
|
||||
<div class="stat-item"><div class="label">平均盈利</div><div class="value text-profit" data-k="avg_profit">-</div></div>
|
||||
<div class="stat-item"><div class="label">平均亏损</div><div class="value text-loss" data-k="avg_loss">-</div></div>
|
||||
<div class="stat-item"><div class="label">盈亏比</div><div class="value" data-k="profit_loss_ratio">-</div></div>
|
||||
<div class="stat-item"><div class="label">连续亏损次数</div><div class="value" data-k="consecutive_losses">-</div></div>
|
||||
<div class="stat-item"><div class="label">最大回撤</div><div class="value" data-k="max_drawdown">-</div></div>
|
||||
<div class="stat-item"><div class="label">最大亏损金额</div><div class="value text-loss" data-k="max_loss_amount">-</div></div>
|
||||
<div class="stat-item"><div class="label">最大亏损占比</div><div class="value text-loss" data-k="max_loss_pct">-</div></div>
|
||||
<div class="stat-item"><div class="label">最大盈利金额</div><div class="value text-profit" data-k="max_profit_amount">-</div></div>
|
||||
<div class="stat-item"><div class="label">最大盈利占比</div><div class="value text-profit" data-k="max_profit_pct">-</div></div>
|
||||
<div class="stat-item"><div class="label">累计手续费</div><div class="value text-loss" data-k="total_fee">-</div></div>
|
||||
<div class="stat-item"><div class="label">情绪单数量</div><div class="value" data-k="emotion_count">-</div></div>
|
||||
<div class="stat-item"><div class="label">情绪单占比</div><div class="value" data-k="emotion_ratio">-</div></div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>按品种统计</h2>
|
||||
<table>
|
||||
<thead><tr><th>品种</th><th>交易次数</th><th>止盈次数</th><th>胜率</th></tr></thead>
|
||||
<tbody>
|
||||
{% for s in by_symbol %}
|
||||
<tr>
|
||||
<td>{{ s.symbol_name or s.symbol }}</td>
|
||||
<td>{{ s.cnt }}</td>
|
||||
<td>{{ s.wins }}</td>
|
||||
<td>{{ round(s.wins / s.cnt * 100, 2) if s.cnt else 0 }}%</td>
|
||||
</tr>
|
||||
{% else %}
|
||||
<tr><td colspan="4" class="text-muted">暂无数据</td></tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="stats-card-head">
|
||||
<h2>分项统计</h2>
|
||||
<div class="field stats-view-field">
|
||||
<label for="stats-view-select">统计维度</label>
|
||||
<select id="stats-view-select"></select>
|
||||
</div>
|
||||
</div>
|
||||
<div class="card-scroll">
|
||||
<table id="stats-breakdown-table">
|
||||
<thead><tr id="stats-breakdown-head"></tr></thead>
|
||||
<tbody id="stats-breakdown-body">
|
||||
<tr><td colspan="12" class="text-muted">加载中…</td></tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>手续费按品种（交易记录）</h2>
|
||||
<table>
|
||||
<thead><tr><th>品种</th><th>笔数</th><th>累计手续费(元)</th></tr></thead>
|
||||
<tbody>
|
||||
{% for f in fee_by_symbol %}
|
||||
<tr>
|
||||
<td>{{ f.symbol_name or f.symbol }}</td>
|
||||
<td>{{ f.cnt }}</td>
|
||||
<td class="text-loss">{{ round(f.total_fee, 2) }}</td>
|
||||
</tr>
|
||||
{% else %}
|
||||
<tr><td colspan="3" class="text-muted">暂无手续费数据</td></tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>按类型统计</h2>
|
||||
<table>
|
||||
<thead><tr><th>类型</th><th>交易次数</th><th>止盈次数</th><th>胜率</th></tr></thead>
|
||||
<tbody>
|
||||
{% for t in by_type %}
|
||||
<tr>
|
||||
<td>{{ t.monitor_type }}</td>
|
||||
<td>{{ t.cnt }}</td>
|
||||
<td>{{ t.wins }}</td>
|
||||
<td>{{ round(t.wins / t.cnt * 100, 2) if t.cnt else 0 }}%</td>
|
||||
</tr>
|
||||
{% else %}
|
||||
<tr><td colspan="4" class="text-muted">暂无数据</td></tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>按方向统计</h2>
|
||||
<table>
|
||||
<thead><tr><th>方向</th><th>交易次数</th><th>止盈次数</th><th>胜率</th></tr></thead>
|
||||
<tbody>
|
||||
{% for d in by_direction %}
|
||||
<tr>
|
||||
<td><span class="badge dir">{{ '做多' if d.direction == 'long' else '做空' }}</span></td>
|
||||
<td>{{ d.cnt }}</td>
|
||||
<td>{{ d.wins }}</td>
|
||||
<td>{{ round(d.wins / d.cnt * 100, 2) if d.cnt else 0 }}%</td>
|
||||
</tr>
|
||||
{% else %}
|
||||
<tr><td colspan="4" class="text-muted">暂无数据</td></tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>最近 10 笔交易记录</h2>
|
||||
<table>
|
||||
<thead><tr><th>品种</th><th>方向</th><th>毛盈亏</th><th>手续费</th><th>净盈亏</th><th>结果</th><th>时间</th></tr></thead>
|
||||
<tbody>
|
||||
{% for r in recent %}
|
||||
<tr>
|
||||
<td>{{ r.symbol_name or r.symbol }}</td>
|
||||
<td><span class="badge dir">{{ '做多' if r.direction == 'long' else '做空' }}</span></td>
|
||||
<td>{{ r.pnl if r.pnl is not none else '-' }}</td>
|
||||
<td class="text-muted">{{ r.fee if r.fee is not none else '-' }}</td>
|
||||
<td>
|
||||
{% if r.pnl_net and r.pnl_net > 0 %}<span class="badge profit">{{ r.pnl_net }}</span>
|
||||
{% elif r.pnl_net and r.pnl_net < 0 %}<span class="badge loss">{{ r.pnl_net }}</span>
|
||||
{% else %}-{% endif %}
|
||||
</td>
|
||||
<td>
|
||||
{% if r.result == '止盈' %}<span class="badge profit">止盈</span>
|
||||
{% elif r.result == '止损' %}<span class="badge loss">止损</span>
|
||||
{% else %}{{ r.result or '-' }}{% endif %}
|
||||
</td>
|
||||
<td>{{ r.close_time[:16] if r.close_time else (r.created_at[:16] if r.created_at else '') }}</td>
|
||||
</tr>
|
||||
{% else %}
|
||||
<tr><td colspan="7" class="text-muted">暂无数据</td></tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<style>
|
||||
.stats-toolbar{display:flex;align-items:center;justify-content:space-between;gap:1rem;margin-bottom:1rem;flex-wrap:wrap}
|
||||
.stats-toolbar .btn-secondary{width:auto;padding:.5rem 1rem;border-radius:8px;border:1px solid var(--input-border);background:var(--toggle-bg);color:var(--text-primary);cursor:pointer;font-size:.85rem}
|
||||
.stats-toolbar .btn-secondary:hover{border-color:var(--accent);color:var(--accent)}
|
||||
.stat-grid-summary{grid-template-columns:repeat(auto-fill,minmax(128px,1fr))}
|
||||
.stats-card-head{display:flex;align-items:flex-end;justify-content:space-between;gap:1rem;flex-wrap:wrap;margin-bottom:1rem}
|
||||
.stats-card-head h2{margin-bottom:0}
|
||||
.stats-view-field{width:auto;min-width:200px}
|
||||
.stats-view-field select{width:100%;min-width:180px}
|
||||
</style>
|
||||
{% endblock %}
|
||||
|
||||
{% block extra_js %}
|
||||
<script src="{{ url_for('static', filename='js/stats.js') }}"></script>
|
||||
{% endblock %}
|
||||
|
||||
Reference in New Issue
Block a user