\u4f60\u8981\u7684\u662f\u80fd\u6bcf\u5929\u4e00\u952e\u8dd1\u3001\u4e24\u6b21\/\u5468\u8c03\u4ed3\u3001\u98ce\u63a7\u4e2d\u6027\u7684\u5b9e\u7528\u811a\u672c\u3002\u4e0b\u9762\u8fd9\u5957\u65b9\u6848\u4ee5\u6caa\u6df1300\u6210\u5206\u4e3a\u5e95\u5c42\u6c60\uff08\u7a33\u6d41\u52a8\u6027\uff0c\u907f\u514d\u5e78\u5b58\u8005\u504f\u5dee\uff09\uff0c\u5468\u4e8c\/\u5468\u56db\u6536\u76d8\u540e\u51fa\u4fe1\u53f7\uff0c\u6b21\u65e5\u5f00\u76d8\u6309\u76ee\u6807\u6743\u91cd\u6210\u4ea4\uff0c\u542b\u6210\u672c\u3001\u6ed1\u70b9\u4e0e\u6da8\u8dcc\u505c\u6210\u4ea4\u7ea6\u675f\uff0c\u5e76\u8f93\u51fa\u8ba2\u5355\u4e0e\u7ee9\u6548\u56fe\u3002<\/p>\n\n\n\n
\u5148\u5b89\u88c5\u4f9d\u8d56\uff1a<\/p>\n\n\n\n
pip install akshare pandas numpy matplotlib<\/code><\/pre>\n\n\n\n# -*- coding: utf-8 -*-\n# A\u80a1\u53cc\u5468\u8c03\u4ed3 \u4e00\u952e\u65e5\u5e38\u7814\u7a76\u4e0e\u56de\u6d4b\u811a\u672c\uff08AkShare\uff09\n# \u8fd0\u884c\u73af\u5883\uff1aPython 3.9+\uff1b\u4f9d\u8d56\uff1aakshare, pandas, numpy, matplotlib\n\nimport akshare as ak\nimport pandas as pd\nimport numpy as np\nimport time\nfrom datetime import datetime, timedelta\nimport matplotlib.pyplot as plt\n\n# \u4fee\u6539\u4e3aWindows\u7cfb\u7edf\u9ed8\u8ba4\u4e2d\u6587\u5b57\u4f53\nplt.rcParams[\"font.family\"] = [\"SimHei\", \"Microsoft YaHei\", \"SimSun\"]\nplt.rcParams[\"axes.unicode_minus\"] = False # \u89e3\u51b3\u8d1f\u53f7\u663e\u793a\u95ee\u9898\n\n# -----------------------\n# \u53c2\u6570\u533a\uff08\u6309\u9700\u4fee\u6539\uff09\n# -----------------------\nSTART_DATE = \"2018-01-01\"\nEND_DATE = None # None \u8868\u793a\u5230\u4eca\u65e5\nCASH_INIT = 1_000_000\nMAX_POS = 15 # \u6700\u591a\u6301\u4ed3\u6570\nMAX_W = 0.12 # \u5355\u7968\u6743\u91cd\u4e0a\u9650\nFEE_COMM = 0.0005 # \u4f63\u91d1\nFEE_STAMP = 0.001 # \u5370\u82b1\u7a0e\uff08\u4ec5\u5356\u51fa\uff09\nSLIPPAGE = 0.0005 # \u6ed1\u70b9\nLIQ_VOL_TH = 1_000_000 # \u8fd160\u65e5\u5e73\u5747\u6210\u4ea4\u91cf\u95e8\u69db\uff08\u624b\/\u80a1\uff09\uff0c\u53ef\u6309\u9700\u8981\u8c03\nREBAL_WEEKDAYS = {1, 3} # \u5468\u4e8c(1)\u3001\u5468\u56db(3) \u8c03\u4ed3\uff1bPython: Mon=0\n\n# -----------------------\n# \u5de5\u5177\u51fd\u6570\uff1a\u6307\u6807\n# -----------------------\ndef sma(s, n):\n return s.rolling(n).mean()\n\ndef rsi(close, n=14):\n delta = close.diff()\n up = np.where(delta > 0, delta, 0.0)\n dn = np.where(delta < 0, -delta, 0.0)\n up_ema = pd.Series(up, index=close.index).ewm(alpha=1\/n, adjust=False).mean()\n dn_ema = pd.Series(dn, index=close.index).ewm(alpha=1\/n, adjust=False).mean()\n rs = up_ema \/ dn_ema.replace(0, np.nan)\n return 100 - (100 \/ (1 + rs))\n\ndef bbands(close, n=20, k=2):\n mid = close.rolling(n).mean()\n std = close.rolling(n).std(ddof=0)\n up = mid + k * std\n dn = mid - k * std\n return mid, up, dn\n\ndef true_range(df):\n prev_close = df[\"close\"].shift(1)\n tr = pd.concat([\n (df[\"high\"] - df[\"low\"]).abs(),\n (df[\"high\"] - prev_close).abs(),\n (df[\"low\"] - prev_close).abs()\n ], axis=1).max(axis=1)\n return tr\n\ndef ann_vol(close, n=20):\n ret = close.pct_change()\n return ret.rolling(n).std() * np.sqrt(252)\n\n# -----------------------\n# \u6570\u636e\u83b7\u53d6\u4e0e\u57fa\u51c6\u65e5\u5386\n# -----------------------\ndef get_trade_calendar(start=START_DATE, end=END_DATE):\n cal = ak.tool_trade_date_hist_sina()\n cal[\"trade_date\"] = pd.to_datetime(cal[\"trade_date\"])\n if end is None:\n end = datetime.now().strftime(\"%Y-%m-%d\")\n cal = cal[(cal[\"trade_date\"] >= pd.to_datetime(start)) &\n (cal[\"trade_date\"] <= pd.to_datetime(end))][\"trade_date\"].sort_values()\n return cal.tolist()\n\ndef get_hs300_symbols():\n df = ak.index_stock_cons(symbol=\"000300\")\n # \u5217\u53ef\u80fd\u662f '\u54c1\u79cd\u4ee3\u7801' \u6216 '\u6210\u5206\u5238\u4ee3\u7801'; \u505a\u517c\u5bb9\n for col in [\"\u54c1\u79cd\u4ee3\u7801\", \"\u6210\u5206\u5238\u4ee3\u7801\", \"\u4ee3\u7801\", \"code\"]:\n if col in df.columns:\n return sorted(df[col].astype(str).str.zfill(6).unique().tolist())\n # \u515c\u5e95\n return sorted(df.iloc[:,0].astype(str).str.zfill(6).unique().tolist())\n\ndef get_hist(code, start=START_DATE, end=END_DATE, adjust=\"qfq\"):\n if end is None:\n end = datetime.now().strftime(\"%Y%m%d\")\n df = ak.stock_zh_a_hist(symbol=code, period=\"daily\",\n start_date=start.replace(\"-\",\"\"),\n end_date=end.replace(\"-\",\"\"),\n adjust=adjust)\n # \u517c\u5bb9\u5217\u540d\n mapper = {\"\u65e5\u671f\":\"date\",\"\u5f00\u76d8\":\"open\",\"\u6536\u76d8\":\"close\",\"\u6700\u9ad8\":\"high\",\"\u6700\u4f4e\":\"low\",\"\u6210\u4ea4\u91cf\":\"volume\",\"\u6210\u4ea4\u989d\":\"amount\"}\n df = df.rename(columns=mapper)\n df[\"date\"] = pd.to_datetime(df[\"date\"])\n cols = [c for c in [\"date\",\"open\",\"high\",\"low\",\"close\",\"volume\",\"amount\"] if c in df.columns]\n df = df[cols].set_index(\"date\").sort_index()\n df = df.dropna()\n return df\n\n# -----------------------\n# \u4fe1\u53f7\u4e0e\u7b5b\u9009\n# -----------------------\ndef compute_indicators(df):\n out = df.copy()\n out[\"MA20\"] = sma(out[\"close\"], 20)\n out[\"MA60\"] = sma(out[\"close\"], 60)\n out[\"RSI14\"] = rsi(out[\"close\"], 14)\n out[\"MOM126\"] = out[\"close\"] \/ out[\"close\"].shift(126) - 1\n mid, up, dn = bbands(out[\"close\"], 20, 2)\n out[\"BB_MID\"], out[\"BB_UP\"], out[\"BB_DN\"] = mid, up, dn\n out[\"ANNVOL20\"] = ann_vol(out[\"close\"], 20)\n out[\"TR\"] = true_range(out)\n out[\"ATR20\"] = out[\"TR\"].rolling(20).mean()\n return out\n\ndef pass_screen(row):\n c1 = row[\"MA20\"] > row[\"MA60\"]\n c2 = row[\"close\"] > row[\"MA20\"]\n c3 = row[\"MOM126\"] > 0\n c4 = 45 <= row[\"RSI14\"] <= 65\n c5 = row[\"close\"] < row[\"BB_UP\"]\n return c1 and c2 and c3 and c4 and c5\n\n# -----------------------\n# \u56de\u6d4b\uff1a\u4e24\u6b21\/\u5468\u8c03\u4ed3\uff0c\u6b21\u65e5\u5f00\u76d8\u6210\u4ea4\n# -----------------------\ndef backtest_portfolio(symbols, start=START_DATE, end=END_DATE, cash_init=CASH_INIT):\n # \u4e0b\u8f7d\u6570\u636e\n data = {}\n for i, sym in enumerate(symbols, 1):\n try:\n df = get_hist(sym, start, end)\n data[sym] = compute_indicators(df)\n except Exception:\n pass\n time.sleep(0.2) # \u6e29\u548c\u9650\u901f\n # \u7edf\u4e00\u65e5\u5386\n all_dates = sorted(set().union(*[df.index for df in data.values()]))\n cal = pd.DatetimeIndex(all_dates)\n # \u9009\u62e9\u8c03\u4ed3\u65e5\uff08\u5468\u4e8c\/\u5468\u56db\u4e14\u662f\u4ea4\u6613\u65e5\uff09\n rebal_days = [d for d in cal if d.weekday() in REBAL_WEEKDAYS]\n # \u8fc7\u6ee4\uff1a\u8fd160\u65e5\u5747\u91cf\n def liquid_ok(df, dt):\n window = df.loc[:dt].tail(60)\n if \"volume\" not in window: \n return True\n return window[\"volume\"].mean() >= LIQ_VOL_TH\n\n # \u72b6\u6001\n cash = cash_init\n positions = {} # sym -> shares\n nav_series = []\n dd_series = []\n equity = cash\n peak = equity\n last_prices = {}\n\n # \u9010\u65e5\u4eff\u771f\n for i, d in enumerate(cal[:-1]): # \u81f3\u5012\u6570\u7b2c\u4e8c\u5929\uff08\u56e0\u6b21\u65e5\u5f00\u76d8\u6210\u4ea4\uff09\n todays_vals = {}\n # \u66f4\u65b0\u6301\u4ed3\u5e02\u503c\n for sym, df in data.items():\n if d in df.index:\n last_prices[sym] = df.at[d, \"close\"]\n if sym in positions and sym in last_prices:\n todays_vals[sym] = positions[sym] * last_prices[sym]\n equity = cash + sum(todays_vals.values())\n peak = max(peak, equity)\n drawdown = (equity \/ peak) - 1\n nav_series.append((d, equity))\n dd_series.append((d, drawdown))\n\n # \u8c03\u4ed3\u4fe1\u53f7\uff08\u7528\u4eca\u65e5\u6536\u76d8\uff09\n if d in rebal_days:\n # \u751f\u6210\u5019\u9009\n candidates = []\n for sym, df in data.items():\n if d not in df.index: \n continue\n if not liquid_ok(df, d):\n continue\n row = df.loc[d]\n # \u8981\u6c42\u6307\u6807\u6709\u6548\n if np.any(pd.isna(row[[\"MA20\",\"MA60\",\"RSI14\",\"MOM126\",\"BB_UP\",\"ANNVOL20\"]])):\n continue\n if pass_screen(row):\n candidates.append((sym, row[\"MOM126\"], row[\"ANNVOL20\"]))\n # \u6392\u5e8f\u4e0e\u622a\u65ad\n candidates.sort(key=lambda x: x[1], reverse=True)\n picks = candidates[:MAX_POS]\n\n # \u8ba1\u7b97\u76ee\u6807\u6743\u91cd\uff08\u6ce2\u52a8\u7387\u5012\u6570\uff09\n if picks:\n vols = np.array([max(1e-6, x[2]) for x in picks])\n inv = 1.0 \/ vols\n w_raw = inv \/ inv.sum()\n # \u5355\u7968\u4e0a\u9650\n w_capped = np.minimum(w_raw, MAX_W)\n w = w_capped \/ w_capped.sum()\n target = {sym: w[j] for j, (sym, _, _) in enumerate(picks)}\n else:\n target = {}\n\n # \u6b21\u65e5\u5f00\u76d8\u6267\u884c\n nd = cal[i+1]\n # \u6784\u5efa\u76ee\u6807\u5934\u5bf8\u4ef7\u503c\n target_value = {sym: equity * w for sym, w in target.items()}\n\n # \u5148\u5356\u51fa\u672a\u5728\u76ee\u6807\u5185\u6216\u8d85\u914d\u90e8\u5206\n for sym in list(positions.keys()):\n df = data.get(sym)\n if df is None or nd not in df.index or d not in df.index:\n continue\n prev_close = df.at[d, \"close\"]\n next_open = df.at[nd, \"open\"]\n # \u8dcc\u505c\u65e0\u6cd5\u5356\u51fa\uff08\u8fd1\u4f3c\uff09\n if next_open <= prev_close * (1 - 0.095):\n continue\n price = next_open * (1 - SLIPPAGE)\n cur_val = positions[sym] * price\n tgt_val = target_value.get(sym, 0.0)\n if cur_val > tgt_val + 1: # \u8d85\u914d\u6216\u4e0d\u5728\u76ee\u6807\n sell_val = cur_val - tgt_val\n shares = int(sell_val \/\/ price)\n if shares > 0:\n proceeds = shares * price * (1 - FEE_COMM - FEE_STAMP)\n positions[sym] -= shares\n if positions[sym] <= 0:\n positions.pop(sym, None)\n cash += proceeds\n\n # \u518d\u4e70\u5165\u4e0d\u8fbe\u6807\u6216\u65b0\u6807\u7684\n for sym, tgt_val in target_value.items():\n df = data.get(sym)\n if df is None or nd not in df.index or d not in df.index:\n continue\n prev_close = df.at[d, \"close\"]\n next_open = df.at[nd, \"open\"]\n # \u6da8\u505c\u65e0\u6cd5\u4e70\u5165\uff08\u8fd1\u4f3c\uff09\n if next_open >= prev_close * (1 + 0.095):\n continue\n price = next_open * (1 + SLIPPAGE)\n cur_shares = positions.get(sym, 0)\n cur_val = cur_shares * price\n buy_val = max(0.0, tgt_val - cur_val)\n shares = int(buy_val \/\/ price)\n if shares > 0 and cash > shares * price * (1 + FEE_COMM):\n cost = shares * price * (1 + FEE_COMM)\n cash -= cost\n positions[sym] = cur_shares + shares\n\n nav = pd.Series({d: v for d, v in nav_series}).sort_index()\n dd = pd.Series({d: v for d, v in dd_series}).sort_index()\n ret = nav.pct_change().fillna(0)\n stats = {\n \"CAGR\": (nav.iloc[-1] \/ nav.iloc[0]) ** (252\/len(nav)) - 1,\n \"Vol\": ret.std() * np.sqrt(252),\n \"Sharpe\": (ret.mean() \/ (ret.std() + 1e-9)) * np.sqrt(252),\n \"MaxDD\": dd.min()\n }\n return nav, dd, positions, stats\n\n# -----------------------\n# \u4eca\u65e5\u8c03\u4ed3\u8ba1\u5212\uff08\u5b9e\u7528\u65e5\u5e38\uff09\n# -----------------------\ndef today_rebalance_plan():\n today = pd.Timestamp(datetime.now().date())\n # \u82e5\u4eca\u5929\u4e0d\u662f\u4ea4\u6613\u65e5\u6216\u4e0d\u662f\u5468\u4e8c\/\u5468\u56db\uff0c\u76f4\u63a5\u63d0\u793a\n cal = get_trade_calendar((today - pd.Timedelta(days=10)).strftime(\"%Y-%m-%d\"),\n today.strftime(\"%Y-%m-%d\"))\n cal_idx = pd.DatetimeIndex(cal)\n if today not in cal_idx or today.weekday() not in REBAL_WEEKDAYS:\n print(\"\u4eca\u5929\u4e0d\u662f\u8ba1\u5212\u8c03\u4ed3\u65e5\uff08\u6216\u975e\u4ea4\u6613\u65e5\uff09\u3002\")\n return\n\n syms = get_hs300_symbols()\n plan_rows = []\n for sym in syms:\n try:\n df = get_hist(sym, (today - pd.Timedelta(days=400)).strftime(\"%Y-%m-%d\"),\n today.strftime(\"%Y-%m-%d\"))\n df = compute_indicators(df)\n if len(df) < 200 or today not in df.index:\n continue\n row = df.loc[today]\n # \u6d41\u52a8\u6027\n if \"volume\" in df:\n if df.loc[:today].tail(60)[\"volume\"].mean() < LIQ_VOL_TH:\n continue\n if np.any(pd.isna(row[[\"MA20\",\"MA60\",\"RSI14\",\"MOM126\",\"BB_UP\",\"ANNVOL20\"]])):\n continue\n if pass_screen(row):\n plan_rows.append({\n \"code\": sym,\n \"mom126\": row[\"MOM126\"],\n \"annvol20\": row[\"ANNVOL20\"],\n \"close\": row[\"close\"]\n })\n except Exception:\n pass\n time.sleep(0.05)\n\n if not plan_rows:\n print(\"\u4eca\u65e5\u65e0\u6807\u7684\u901a\u8fc7\u7b5b\u9009\u3002\")\n return\n\n dfp = pd.DataFrame(plan_rows).sort_values(\"mom126\", ascending=False).head(MAX_POS)\n inv = 1.0 \/ np.maximum(1e-6, dfp[\"annvol20\"].values)\n w_raw = inv \/ inv.sum()\n w_capped = np.minimum(w_raw, MAX_W)\n w = w_capped \/ w_capped.sum()\n dfp[\"target_weight\"] = w\n dfp.to_csv(f\"rebalance_plan_{today.strftime('%Y%m%d')}.csv\", index=False, encoding=\"utf-8-sig\")\n print(\"\u4eca\u65e5\u8ba1\u5212\uff08\u6b21\u65e5\u5f00\u76d8\u6267\u884c\uff0c\u6743\u91cd\u5df2\u622a\u9876\uff09\uff1a\")\n print(dfp[[\"code\",\"target_weight\",\"mom126\",\"annvol20\",\"close\"]])\n\n# -----------------------\n# \u4e3b\u51fd\u6570\uff1a\u56de\u6d4b + \u56fe\u5f62 + \u4eca\u65e5\u8ba1\u5212\n# -----------------------\nif __name__ == \"__main__\":\n print(\"\u83b7\u53d6\u6caa\u6df1300\u6210\u5206...\")\n symbols = get_hs300_symbols()\n print(f\"\u6210\u5206\u80a1\u6570\u91cf\uff1a{len(symbols)}\")\n\n print(\"\u5f00\u59cb\u56de\u6d4b\uff08\u8fd9\u53ef\u80fd\u9700\u8981\u51e0\u5206\u949f\uff09...\")\n nav, dd, positions, stats = backtest_portfolio(symbols, START_DATE, END_DATE, CASH_INIT)\n print(\"\u56de\u6d4b\u7edf\u8ba1\uff1a\")\n for k, v in stats.items():\n print(f\"{k}: {v:.4f}\")\n\n # \u7ed8\u5236\u51c0\u503c\u4e0e\u56de\u64a4\n fig, ax = plt.subplots(2, 1, figsize=(10, 6), sharex=True,\n gridspec_kw={\"height_ratios\":[3,1]})\n nav_norm = nav \/ nav.iloc[0]\n ax[0].plot(nav_norm.index, nav_norm.values, label=\"Portfolio\")\n ax[0].set_title(\"\u7ec4\u5408\u51c0\u503c\uff08\u5f52\u4e00\uff09\")\n ax[0].legend()\n ax[0].grid(True, alpha=0.3)\n ax[1].fill_between(dd.index, dd.values, 0, color=\"red\", alpha=0.3)\n ax[1].set_title(\"\u56de\u64a4\")\n ax[1].grid(True, alpha=0.3)\n plt.tight_layout()\n plt.savefig(\"backtest_nav_drawdown.png\", dpi=150)\n plt.show()\n\n # \u5f53\u65e5\u8c03\u4ed3\u8ba1\u5212\uff08\u5728 CST \u4e0b\u5348 15:10 \u540e\u8fd0\u884c\u66f4\u5408\u9002\uff09\n today_rebalance_plan()<\/code><\/pre>\n\n\n\n
\n\n\n\n\u5982\u4f55\u6bcf\u5929\u4e00\u952e\u8dd1<\/h2>\n\n\n\n\n- \u65f6\u95f4\u70b9:<\/strong> \u6ca7\u5dde\u672c\u5730\u65f6\u95f4\u5efa\u8bae\u5728\u4ea4\u6613\u65e5 15:10 \u4e4b\u540e\u8fd0\u884c\uff0c\u4fdd\u8bc1\u5f53\u65e5\u6536\u76d8\u6570\u636e\u53ef\u7528\u3002<\/li>\n\n\n\n
- \u547d\u4ee4\u884c:<\/strong><\/li>\n\n\n\n
- \u8fd0\u884c:<\/strong> python run_quant.py<\/li>\n\n\n\n
- \u8f93\u51fa:<\/strong> backtest_nav_drawdown.png\u3001rebalance_plan_YYYYMMDD.csv\uff0c\u5e76\u5728\u63a7\u5236\u53f0\u6253\u5370\u7edf\u8ba1\u4e0e\u5f53\u65e5\u8ba1\u5212\u3002<\/li>\n\n\n\n
- \u5b9a\u65f6\u4efb\u52a1:<\/strong><\/li>\n\n\n\n
- Linux crontab:<\/strong> 10 15 * * 1-5 \/usr\/bin\/python3 \/path\/run_quant.py >> \/path\/log.txt 2>&1<\/li>\n\n\n\n
- Windows \u4efb\u52a1\u8ba1\u5212:<\/strong> \u8bbe\u4e3a\u5de5\u4f5c\u65e5 15:10 \u89e6\u53d1\u3002<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n\u53ef\u8c03\u53c2\u6570\u4e0e\u6269\u5c55<\/h2>\n\n\n\n\n- \u53ef\u8c03\u53c2\u6570:<\/strong><\/li>\n\n\n\n
- MAX_POS\/MAX_W:<\/strong> \u63a7\u5236\u96c6\u4e2d\u5ea6\u4e0e\u98ce\u9669\u3002<\/li>\n\n\n\n
- LIQ_VOL_TH:<\/strong> \u63d0\u9ad8\u95e8\u69db\u53ef\u8fdb\u4e00\u6b65\u964d\u4f4e\u6d41\u52a8\u6027\u98ce\u9669\u3002<\/li>\n\n\n\n
- \u8d39\u7387\/\u6ed1\u70b9:<\/strong> \u6309\u4f60\u7684\u5238\u5546\u8d39\u7387\u4e0e\u6210\u4ea4\u4f53\u9a8c\u5fae\u8c03\u3002<\/li>\n\n\n\n
- \u98ce\u63a7\u589e\u5f3a:<\/strong><\/li>\n\n\n\n
- \u6b62\u635f\/\u8ddf\u8e2a\u6b62\u76c8:<\/strong> \u4ee5 ATR \u4e3a\u5355\u4f4d\uff0c\u5982\u4ef7\u683c\u8dcc\u7834 MA60 \u6216 3\u00d7ATR \u8dcc\u5e45\u5e73\u4ed3\u3002<\/li>\n\n\n\n
- \u6da8\u8dcc\u505c\u7ec6\u5316:<\/strong> \u79d1\u521b\/\u521b\u4e1a\u677f 20% \u89c4\u5219\u53ef\u6309\u80a1\u7968\u677f\u5757\u8c03\u6574\u9608\u503c\u3002<\/li>\n\n\n\n
- \u57fa\u51c6\u6bd4\u8f83:<\/strong> \u53e0\u52a0\u6caa\u6df1300\u6307\u6570\u51c0\u503c\uff0c\u8ba1\u7b97\u8d85\u989d\u4e0e\u4fe1\u606f\u6bd4\u7387\u3002<\/li>\n\n\n\n
- \u7814\u7a76\u7ef4\u5ea6:<\/strong><\/li>\n\n\n\n
- \u6a2a\u622a\u9762\u591a\u56e0\u5b50:<\/strong> \u4ee5\u52a8\u91cf\u3001\u6ce2\u52a8\u3001\u4f30\u503c\uff08PE\/PB\uff0c\u9700\u989d\u5916\u6570\u636e\uff09\u505a\u6253\u5206\uff0c\u5468\u4e8c\/\u5468\u56db\u540c\u9891\u8c03\u4ed3\u3002<\/li>\n\n\n\n
- \u518d\u5e73\u8861\u9c81\u68d2\u6027:<\/strong> \u6539\u4e3a\u201c\u9608\u503c\u518d\u5e73\u8861\u201d\uff08\u504f\u79bb>25%\u624d\u8c03\u4ed3\uff09\u964d\u4f4e\u6362\u624b\u3002<\/li>\n<\/ul>\n\n\n\n
\u5982\u679c\u4f60\u60f3\u628a\u6807\u7684\u6c60\u6539\u4e3a\u4e2d\u8bc1500\u6216\u52a0\u5165\u884c\u4e1a\u4e2d\u6027\u7ea6\u675f\uff0c\u6216\u8005\u628a\u8c03\u4ed3\u65e5\u6539\u6210\u201c\u6bcf\u5468\u6700\u8fd1\u7684\u5468\u4e8c\u4e0e\u5468\u56db\uff0c\u5982\u9047\u8282\u5047\u65e5\u987a\u5ef6\u201d\uff0c\u6211\u53ef\u4ee5\u628a\u4e0a\u8ff0\u811a\u672c\u518d\u7ec6\u5316\u6210\u6a21\u5757\u5316\u7684\u7814\u7a76\u6846\u67b6\uff0c\u5e76\u52a0\u4e0a\u6027\u80fd\u5256\u6790\u4e0e\u7ed3\u679c\u7f13\u5b58\u6765\u52a0\u901f\u65e5\u5e38\u8dd1\u6279\u3002<\/p>\n\n\n\n
\u4ee5\u4e0b\u662f\u7a0b\u5e8f\u8fd0\u884c\u7ed3\u679c\u7684\u8be6\u7ec6\u89e3\u91ca\uff1a<\/p>\n\n\n\n
\n- \u6570\u636e\u83b7\u53d6\u4e0e\u56de\u6d4b\u6982\u51b5
\u6caa\u6df1300\u6210\u5206\u80a1\u6570\u91cf\uff1a282\uff1a\u6210\u529f\u83b7\u53d6\u4e86\u5f53\u524d\u6caa\u6df1300\u6307\u6570\u7684282\u53ea\u6210\u5206\u80a1
\u56de\u6d4b\u8017\u65f6\u63d0\u793a\uff1a\u7b56\u7565\u56de\u6d4b\u9700\u8981\u4e00\u5b9a\u8ba1\u7b97\u65f6\u95f4\uff0c\u7b26\u5408\u9884\u671f<\/li>\n\n\n\n - \u6838\u5fc3\u56de\u6d4b\u6307\u6807\u89e3\u8bfb
| \u6307\u6807 | \u6570\u503c | \u542b\u4e49\u89e3\u91ca | \u7b56\u7565\u8bc4\u4f30 |
|——–|———|———————————–|—————————|
| CAGR | -0.0900 | \u590d\u5408\u5e74\u5316\u589e\u957f\u7387\uff1a-9.00% | \u7b56\u7565\u6574\u4f53\u5e74\u5316\u4e8f\u635f9% |
| Vol | 0.2348 | \u6ce2\u52a8\u7387\uff1a23.48% | \u6536\u76ca\u6ce2\u52a8\u8f83\u5927 |
| Sharpe | -0.2843 | \u590f\u666e\u6bd4\u7387\uff1a-0.28 | \u98ce\u9669\u8c03\u6574\u540e\u6536\u76ca\u4e3a\u8d1f\uff0c\u8868\u73b0\u5f31\u4e8e\u65e0\u98ce\u9669\u8d44\u4ea7 |
| MaxDD | -0.3505 | \u6700\u5927\u56de\u64a4\uff1a-35.05% | \u7b56\u7565\u5386\u53f2\u6700\u5927\u4e8f\u635f\u5e45\u5ea6\u4e3a35% |<\/li>\n\n\n\n - \u4eca\u65e5\u6295\u8d44\u8ba1\u5212\uff08\u6b21\u65e5\u5f00\u76d8\u6267\u884c\uff09
| \u4ee3\u7801 | \u76ee\u6807\u6743\u91cd | 126\u5929\u52a8\u91cf(mom126) | 20\u5929\u5e74\u5316\u6ce2\u52a8\u7387(annvol20) | \u6536\u76d8\u4ef7 |
|——–|———-|——————-|————————–|——–|
| 300394 | 0.2 | 0.6719 | 0.7191 | 107.87 |
| 603799 | 0.2 | 0.4765 | 0.4454 | 44.25 |
| 002463 | 0.2 | 0.4701 | 0.5822 | 55.35 |
| 002074 | 0.2 | 0.3858 | 0.2900 | 30.46 |
| 000617 | 0.2 | 0.3205 | 0.4965 | 8.90 |<\/li>\n<\/ol>\n\n\n\n\u8ba1\u5212\u53c2\u6570\u8bf4\u660e\uff1a
target_weight=0.2\uff1a\u91c7\u7528\u7b49\u6743\u91cd\u5206\u914d\u7b56\u7565\uff0c\u6bcf\u53ea\u80a1\u7968\u914d\u7f6e20%\u4ed3\u4f4d
mom126\uff1a126\u5929\u52a8\u91cf\u6307\u6807\uff08\u8d8a\u9ad8\u8868\u793a\u8fd1\u671f\u8d8b\u52bf\u8d8a\u5f3a\uff09
annvol20\uff1a20\u5929\u5e74\u5316\u6ce2\u52a8\u7387\uff08\u8861\u91cf\u77ed\u671f\u98ce\u9669\uff0c\u6570\u503c\u8d8a\u4f4e\u98ce\u9669\u76f8\u5bf9\u8d8a\u5c0f\uff09<\/p>\n\n\n\n
\n- \u7b56\u7565\u8868\u73b0\u8bc4\u4f30
\u98ce\u9669\u6536\u76ca\u7279\u5f81\uff1a\u5f53\u524d\u7b56\u7565\u5448\u73b0\u8d1f\u6536\u76ca\u3001\u9ad8\u6ce2\u52a8\u7279\u5f81\uff0c\u590f\u666e\u6bd4\u7387\u4e3a\u8d1f\u8868\u660e\u7b56\u7565\u672a\u80fd\u6709\u6548\u521b\u9020\u8d85\u989d\u6536\u76ca
\u6700\u5927\u56de\u64a4\u98ce\u9669\uff1a35.05%\u7684\u6700\u5927\u56de\u64a4\u9700\u8981\u8b66\u60d5\uff0c\u53ef\u80fd\u8d85\u51fa\u591a\u6570\u6295\u8d44\u8005\u7684\u98ce\u9669\u627f\u53d7\u80fd\u529b
\u6301\u4ed3\u7b56\u7565\uff1a\u9009\u62e9\u4e865\u53ea\u52a8\u91cf\u7279\u5f81\u8f83\u5f3a\u7684\u80a1\u7968\u8fdb\u884c\u7b49\u6743\u91cd\u914d\u7f6e\uff0c\u517c\u987e\u4e86\u52a8\u91cf\u56e0\u5b50\u548c\u6ce2\u52a8\u7387\u63a7\u5236<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"A\u80a1\u53cc\u5468\u8c03\u4ed3\uff1a\u4e00\u952e\u65e5\u5e38\u91cf\u5316\u811a\u672c\uff08AkShare\uff09 \u4f60\u8981\u7684\u662f\u80fd\u6bcf\u5929\u4e00\u952e\u8dd1\u3001\u4e24\u6b21\/\u5468 Continue Reading<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,24],"tags":[],"class_list":["post-6062","post","type-post","status-publish","format-standard","hentry","category-2","category-24"],"_links":{"self":[{"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=\/wp\/v2\/posts\/6062","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6062"}],"version-history":[{"count":4,"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=\/wp\/v2\/posts\/6062\/revisions"}],"predecessor-version":[{"id":6067,"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=\/wp\/v2\/posts\/6062\/revisions\/6067"}],"wp:attachment":[{"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6062"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6062"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/cnliutz.wicp.vip\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6062"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}