readme.md <\/p>\n\n\n\n
requirementst.txt<\/p>\n\n\n\n
\u6e90\u4ee3\u7801\uff1a<\/strong><\/p>\n\n\n\n readme.md requirementst.txt \u6e90\u4ee3\u7801\uff1a<\/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-6546","post","type-post","status-publish","format-standard","hentry","category-2","category-24"],"_links":{"self":[{"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=\/wp\/v2\/posts\/6546","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6546"}],"version-history":[{"count":1,"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=\/wp\/v2\/posts\/6546\/revisions"}],"predecessor-version":[{"id":6549,"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=\/wp\/v2\/posts\/6546\/revisions\/6549"}],"wp:attachment":[{"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6546"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6546"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/xc.ipyingshe.net:5347\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6546"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport talib as ta\nimport akshare as ak\nimport baostock as bs\nimport datetime\nfrom matplotlib.gridspec import GridSpec\n \n # \u8bbe\u7f6e\u4e2d\u6587\u663e\u793a\nplt.rcParams[\"font.family\"] = [\"SimHei\", \"Microsoft YaHei\", \"SimSun\", \"KaiTi\", \"FangSong\"]\nplt.rcParams[\"axes.unicode_minus\"] = False # \u6b63\u786e\u663e\u793a\u8d1f\u53f7\n\nclass StockTechnicalAnalyzer:\n def __init__(self, data_source='baostock'):\n \"\"\"\n \u521d\u59cb\u5316\u80a1\u7968\u6280\u672f\u5206\u6790\u5668\n data_source: \u6570\u636e\u6e90\uff0c\u53ef\u9009 'baostock' \u6216 'akshare'\n \"\"\"\n self.data_source = data_source\n self.data = None\n self.stock_code = None\n self.start_date = None\n self.end_date = None\n \n def get_stock_data(self, stock_code, start_date, end_date):\n \"\"\"\u4ece\u6307\u5b9a\u6570\u636e\u6e90\u83b7\u53d6\u80a1\u7968\u6570\u636e\"\"\"\n self.stock_code = stock_code\n self.start_date = start_date\n self.end_date = end_date\n \n print(f\"\u6b63\u5728\u4ece{self.data_source}\u83b7\u53d6{stock_code}\u4ece{start_date}\u5230{end_date}\u7684\u6570\u636e...\")\n \n if self.data_source == 'baostock':\n return self._get_baostock_data(stock_code, start_date, end_date)\n elif self.data_source == 'akshare':\n return self._get_akshare_data(stock_code, start_date, end_date)\n else:\n raise ValueError(\"\u6570\u636e\u6e90\u5fc5\u987b\u662f 'baostock' \u6216 'akshare'\")\n \n def _get_baostock_data(self, stock_code, start_date, end_date):\n \"\"\"\u4ecebaostock\u83b7\u53d6\u80a1\u7968\u6570\u636e\"\"\"\n # \u767b\u5f55baostock\n lg = bs.login()\n if lg.error_code != '0':\n print(f\"\u767b\u5f55\u5931\u8d25\uff1a{lg.error_msg}\")\n return None\n \n # \u83b7\u53d6\u80a1\u7968\u6570\u636e\n rs = bs.query_history_k_data_plus(\n stock_code,\n \"date,open,high,low,close,volume\",\n start_date=start_date,\n end_date=end_date,\n frequency=\"d\",\n adjustflag=\"2\" # \u524d\u590d\u6743\n )\n \n # \u5904\u7406\u6570\u636e\n data_list = []\n while (rs.error_code == '0') & rs.next():\n data_list.append(rs.get_row_data())\n \n # \u767b\u51fabaostock\n bs.logout()\n \n # \u8f6c\u6362\u4e3aDataFrame\u5e76\u5904\u7406\n if not data_list:\n print(\"\u6ca1\u6709\u83b7\u53d6\u5230\u6570\u636e\")\n return None\n \n df = pd.DataFrame(data_list, columns=rs.fields)\n # \u8f6c\u6362\u6570\u636e\u7c7b\u578b\n df['date'] = pd.to_datetime(df['date'])\n df['open'] = df['open'].astype(float)\n df['high'] = df['high'].astype(float)\n df['low'] = df['low'].astype(float)\n df['close'] = df['close'].astype(float)\n df['volume'] = df['volume'].astype(float)\n \n df.set_index('date', inplace=True)\n self.data = df\n print(f\"\u6210\u529f\u83b7\u53d6{len(df)}\u6761\u6570\u636e\")\n return df\n \n def _get_akshare_data(self, stock_code, start_date, end_date):\n \"\"\"\u4eceakshare\u83b7\u53d6\u80a1\u7968\u6570\u636e\"\"\"\n try:\n # \u5bf9\u4e8eA\u80a1\uff0cakshare\u7684\u4ee3\u7801\u683c\u5f0f\u4e3a 'sh600000' \u6216 'sz000000'\n # \u8f6c\u6362baostock\u683c\u5f0f\u5230akshare\u683c\u5f0f\n if stock_code.startswith('sh.'):\n ak_code = 'sh' + stock_code[3:]\n elif stock_code.startswith('sz.'):\n ak_code = 'sz' + stock_code[3:]\n else:\n ak_code = stock_code\n \n # \u83b7\u53d6\u80a1\u7968\u6570\u636e\n df = ak.stock_zh_a_daily(symbol=ak_code, start_date=start_date.replace('-', ''), \n end_date=end_date.replace('-', ''), adjust=\"qfq\")\n \n # \u91cd\u547d\u540d\u5217\u4ee5\u7edf\u4e00\u683c\u5f0f\n if not df.empty:\n df['date'] = pd.to_datetime(df['date'])\n df.set_index('date', inplace=True)\n self.data = df\n print(f\"\u6210\u529f\u83b7\u53d6{len(df)}\u6761\u6570\u636e\")\n return df\n else:\n print(\"\u6ca1\u6709\u83b7\u53d6\u5230\u6570\u636e\")\n return None\n except Exception as e:\n print(f\"\u83b7\u53d6\u6570\u636e\u65f6\u51fa\u9519: {str(e)}\")\n return None\n \n def calculate_indicators(self, rsi_period=14, macd_fast=12, macd_slow=26, macd_signal=9,\n bb_period=20, bb_std=2, atr_period=14, cci_period=14, \n roc_period=12, wr_period=14, bias_period=6):\n \"\"\"\u4f7f\u7528TA-Lib\u8ba1\u7b97\u5404\u79cd\u6280\u672f\u6307\u6807\"\"\"\n if self.data is None:\n print(\"\u8bf7\u5148\u83b7\u53d6\u80a1\u7968\u6570\u636e\")\n return None\n \n df = self.data.copy()\n \n # \u8ba1\u7b97RSI\n df['rsi'] = ta.RSI(df['close'], timeperiod=rsi_period)\n \n # \u8ba1\u7b97MACD\n df['macd'], df['macd_signal'], df['macd_hist'] = ta.MACD(\n df['close'], fastperiod=macd_fast, slowperiod=macd_slow, signalperiod=macd_signal\n )\n \n # \u8ba1\u7b97\u5e03\u6797\u5e26\n df['bb_upper'], df['bb_middle'], df['bb_lower'] = ta.BBANDS(\n df['close'], timeperiod=bb_period, nbdevup=bb_std, nbdevdn=bb_std\n )\n \n # \u8ba1\u7b97ATR\n df['atr'] = ta.ATR(df['high'], df['low'], df['close'], timeperiod=atr_period)\n df['atr_pct'] = df['atr'] \/ df['close'] * 100\n \n # \u8ba1\u7b97MA\n df['ma5'] = ta.MA(df['close'], timeperiod=5)\n df['ma10'] = ta.MA(df['close'], timeperiod=10)\n df['ma20'] = ta.MA(df['close'], timeperiod=20)\n df['ma60'] = ta.MA(df['close'], timeperiod=60)\n \n # \u8ba1\u7b97\u6210\u4ea4\u91cfMA\n df['volume_ma5'] = ta.MA(df['volume'], timeperiod=5)\n df['volume_ma10'] = ta.MA(df['volume'], timeperiod=10)\n \n # \u8ba1\u7b97KDJ\n df['k'], df['d'] = ta.STOCH(df['high'], df['low'], df['close'])\n df['j'] = 3 * df['k'] - 2 * df['d']\n \n # \u8ba1\u7b97OBV\n df['obv'] = ta.OBV(df['close'], df['volume'])\n \n # \u8ba1\u7b97CCI (\u987a\u52bf\u6307\u6807)\n df['cci'] = ta.CCI(df['high'], df['low'], df['close'], timeperiod=cci_period)\n \n # \u8ba1\u7b97ROC (\u53d8\u5316\u7387)\n df['roc'] = ta.ROC(df['close'], timeperiod=roc_period)\n \n # \u8ba1\u7b97W&R (\u5a01\u5ec9\u6307\u6807)\n df['wr'] = -100 * ((df['high'].rolling(window=wr_period).max() - df['close']) \/ \n (df['high'].rolling(window=wr_period).max() - df['low'].rolling(window=wr_period).min()))\n \n # \u8ba1\u7b97BIAS (\u4e56\u79bb\u7387)\n df['bias'] = (df['close'] - df['close'].rolling(window=bias_period).mean()) \/ \\\n df['close'].rolling(window=bias_period).mean() * 100\n \n # \u8ba1\u7b97ENE\u6307\u6807 (\u8f68\u9053\u7ebf)\n df['ene_middle'] = df['close'].rolling(window=10).mean()\n df['ene_upper'] = df['ene_middle'] * 1.1\n df['ene_lower'] = df['ene_middle'] * 0.9\n \n # \u8ba1\u7b97VI (\u6ce2\u52a8\u7387\u6307\u6807) - \u57fa\u4e8e\u771f\u5b9e\u6ce2\u5e45\u7684\u79fb\u52a8\u5e73\u5747\n df['vi_positive'] = ta.SMA(df['high'] - df['open'], timeperiod=14)\n df['vi_negative'] = ta.SMA(df['open'] - df['low'], timeperiod=14)\n \n # \u8ba1\u7b97DMI\u6307\u6807\n df['adx'] = ta.ADX(df['high'], df['low'], df['close'], timeperiod=14)\n df['plus_di'] = ta.PLUS_DI(df['high'], df['low'], df['close'], timeperiod=14)\n df['minus_di'] = ta.MINUS_DI(df['high'], df['low'], df['close'], timeperiod=14)\n \n # \u8ba1\u7b97PSY (\u5fc3\u7406\u7ebf)\n df['price_change'] = df['close'].diff(1)\n df['positive_days'] = df['price_change'].apply(lambda x: 1 if x > 0 else 0)\n df['psy'] = df['positive_days'].rolling(window=12).sum() \/ 12 * 100\n \n # \u6e05\u7406\u4e34\u65f6\u5217\n if 'price_change' in df.columns:\n df.drop('price_change', axis=1, inplace=True)\n if 'positive_days' in df.columns:\n df.drop('positive_days', axis=1, inplace=True)\n \n self.data = df\n return df\n \n def generate_signals(self):\n \"\"\"\u57fa\u4e8e\u6280\u672f\u6307\u6807\u751f\u6210\u4ea4\u6613\u4fe1\u53f7\"\"\"\n if self.data is None:\n print(\"\u8bf7\u5148\u83b7\u53d6\u80a1\u7968\u6570\u636e\u5e76\u8ba1\u7b97\u6307\u6807\")\n return None\n \n df = self.data.copy()\n df['signal'] = 0 # 0\u8868\u793a\u65e0\u4fe1\u53f7\uff0c1\u8868\u793a\u4e70\u5165\uff0c-1\u8868\u793a\u5356\u51fa\n \n # RSI\u8d85\u4e70\u8d85\u5356\u4fe1\u53f7\n df.loc[df['rsi'] < 30, 'signal'] += 1 # RSI<30 \u8003\u8651\u4e70\u5165\n df.loc[df['rsi'] > 70, 'signal'] += -1 # RSI>70 \u8003\u8651\u5356\u51fa\n \n # MACD\u91d1\u53c9\u6b7b\u53c9\u4fe1\u53f7\n macd_cross_up = (df['macd'] > df['macd_signal']) & (df['macd'].shift(1) <= df['macd_signal'].shift(1))\n macd_cross_down = (df['macd'] < df['macd_signal']) & (df['macd'].shift(1) >= df['macd_signal'].shift(1))\n df.loc[macd_cross_up, 'signal'] += 1 # MACD\u91d1\u53c9 \u4e70\u5165\u4fe1\u53f7\n df.loc[macd_cross_down, 'signal'] += -1 # MACD\u6b7b\u53c9 \u5356\u51fa\u4fe1\u53f7\n \n # \u5e03\u6797\u5e26\u7a81\u7834\u4fe1\u53f7\n bb_upper_break = (df['close'] > df['bb_upper']) & (df['close'].shift(1) <= df['bb_upper'].shift(1))\n bb_lower_break = (df['close'] < df['bb_lower']) & (df['close'].shift(1) >= df['bb_lower'].shift(1))\n df.loc[bb_upper_break, 'signal'] += 1 # \u7a81\u7834\u4e0a\u8f68 \u4e70\u5165\u4fe1\u53f7\n df.loc[bb_lower_break, 'signal'] += -1 # \u8dcc\u7834\u4e0b\u8f68 \u5356\u51fa\u4fe1\u53f7\n \n # \u7b80\u5316\u4fe1\u53f7\uff1a>0\u4e3a\u4e70\u5165\uff0c<0\u4e3a\u5356\u51fa\n df['final_signal'] = 0\n df.loc[df['signal'] > 0, 'final_signal'] = 1\n df.loc[df['signal'] < 0, 'final_signal'] = -1\n \n self.data = df\n return df\n \n def plot_moving_averages(self):\n \"\"\"\u5355\u72ec\u7ed8\u5236\u79fb\u52a8\u5e73\u5747\u7ebf\u56fe\u8868\"\"\"\n if self.data is None:\n print(\"\u8bf7\u5148\u83b7\u53d6\u80a1\u7968\u6570\u636e\u5e76\u8ba1\u7b97\u6307\u6807\")\n return None\n \n df = self.data.dropna()\n \n # \u521b\u5efa\u7b2c\u4e8c\u4e2a\u56fe\u5f62\n fig = plt.figure(2, figsize=(16, 8))\n fig.clf() # \u6e05\u9664\u56fe\u5f62\u5185\u5bb9\u4f46\u4fdd\u7559\u56fe\u5f62\u5b9e\u4f8b\n \n # \u7ed8\u5236\u4ef7\u683c\u548c\u79fb\u52a8\u5e73\u5747\u7ebf\n plt.plot(df.index, df['close'], label='\u6536\u76d8\u4ef7', linewidth=2)\n plt.plot(df.index, df['ma5'], label='MA5', alpha=0.7)\n plt.plot(df.index, df['ma10'], label='MA10', alpha=0.7)\n plt.plot(df.index, df['ma20'], label='MA20', alpha=0.7)\n plt.plot(df.index, df['ma60'], label='MA60', alpha=0.7)\n \n # \u5e03\u6797\u5e26\n plt.plot(df.index, df['bb_upper'], 'r--', label='\u4e0a\u8f68', alpha=0.6)\n plt.plot(df.index, df['bb_middle'], 'g--', label='\u4e2d\u8f68', alpha=0.6)\n plt.plot(df.index, df['bb_lower'], 'r--', label='\u4e0b\u8f68', alpha=0.6)\n plt.fill_between(df.index, df['bb_upper'], df['bb_lower'], alpha=0.1, color='gray')\n \n # ENE\u8f68\u9053\u7ebf\n plt.plot(df.index, df['ene_upper'], 'c-.', label='ENE\u4e0a\u8f68', alpha=0.8)\n plt.plot(df.index, df['ene_middle'], 'm-.', label='ENE\u4e2d\u8f68', alpha=0.8)\n plt.plot(df.index, df['ene_lower'], 'c-.', label='ENE\u4e0b\u8f68', alpha=0.8)\n \n # \u4ea4\u6613\u4fe1\u53f7\n if 'final_signal' in df.columns:\n buy_signals = df[df['final_signal'] == 1]\n sell_signals = df[df['final_signal'] == -1]\n plt.scatter(buy_signals.index, buy_signals['close'], marker='^', color='g', \n label='\u4e70\u5165\u4fe1\u53f7', s=100, zorder=3)\n plt.scatter(sell_signals.index, sell_signals['close'], marker='v', color='r', \n label='\u5356\u51fa\u4fe1\u53f7', s=100, zorder=3)\n \n plt.title(f'{self.stock_code} \u4ef7\u683c\u4e0e\u79fb\u52a8\u5e73\u5747\u7ebf ({self.start_date} \u81f3 {self.end_date})')\n plt.ylabel('\u4ef7\u683c')\n plt.grid(True, alpha=0.3)\n plt.legend()\n \n # \u8bbe\u7f6ex\u8f74\u65e5\u671f\u683c\u5f0f\n plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=3))\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n plt.xticks(rotation=45)\n \n plt.tight_layout()\n plt.show(block=True) # \u963b\u585e\u663e\u793a\uff0c\u7b49\u5f85\u7528\u6237\u5173\u95ed\n \n return fig\n \n def plot_all_indicators(self):\n \"\"\"\u7ed8\u5236\u6240\u6709\u6280\u672f\u6307\u6807\u56fe\u8868\uff08\u4e0d\u542b\u5747\u7ebf\u56fe\uff09\"\"\"\n if self.data is None:\n print(\"\u8bf7\u5148\u83b7\u53d6\u80a1\u7968\u6570\u636e\u5e76\u8ba1\u7b97\u6307\u6807\")\n return None\n \n df = self.data.dropna() # \u53bb\u9664NaN\u503c\n \n # \u521b\u5efa\u7b2c\u4e09\u4e2a\u56fe\u5f62\n fig = plt.figure(3, figsize=(16, 30))\n fig.clf() # \u6e05\u9664\u56fe\u5f62\u5185\u5bb9\u4f46\u4fdd\u7559\u56fe\u5f62\u5b9e\u4f8b\n gs = GridSpec(9, 1, height_ratios=[1, 2, 2, 2, 2, 2, 2, 2, 2])\n \n # 1. \u4ef7\u683c\n ax1 = plt.subplot(gs[0])\n ax1.plot(df.index, df['close'], label='\u6536\u76d8\u4ef7', linewidth=2, color='blue')\n ax1.set_title(f'{self.stock_code} \u4ef7\u683c')\n ax1.set_ylabel('\u4ef7\u683c')\n ax1.grid(True, alpha=0.3)\n \n # 2. \u6210\u4ea4\u91cf\n ax2 = plt.subplot(gs[1], sharex=ax1)\n ax2.bar(df.index, df['volume'], label='\u6210\u4ea4\u91cf', alpha=0.6, color='gray')\n ax2.plot(df.index, df['volume_ma5'], label='\u6210\u4ea4\u91cfMA5', color='blue')\n ax2.plot(df.index, df['volume_ma10'], label='\u6210\u4ea4\u91cfMA10', color='red')\n ax2.set_ylabel('\u6210\u4ea4\u91cf')\n ax2.legend()\n ax2.grid(True, alpha=0.3)\n \n # 3. RSI\u6307\u6807\n ax3 = plt.subplot(gs[2], sharex=ax1)\n ax3.plot(df.index, df['rsi'], label=f'RSI', color='purple', linewidth=2)\n ax3.axhline(70, color='r', linestyle='--', alpha=0.7, label='\u8d85\u4e70\u7ebf(70)')\n ax3.axhline(30, color='g', linestyle='--', alpha=0.7, label='\u8d85\u5356\u7ebf(30)')\n ax3.axhline(50, color='b', linestyle='--', alpha=0.5)\n ax3.set_ylabel('RSI\u503c')\n ax3.set_ylim(0, 100)\n ax3.legend()\n ax3.grid(True, alpha=0.3)\n \n # 4. MACD\u6307\u6807\n ax4 = plt.subplot(gs[3], sharex=ax1)\n ax4.plot(df.index, df['macd'], label='MACD', color='blue')\n ax4.plot(df.index, df['macd_signal'], label='\u4fe1\u53f7\u7ebf', color='red')\n ax4.bar(df.index, df['macd_hist'], label='\u67f1\u72b6\u56fe', alpha=0.5, color='gray')\n ax4.axhline(0, color='black', linestyle='-', alpha=0.3)\n ax4.set_ylabel('MACD\u503c')\n ax4.legend()\n ax4.grid(True, alpha=0.3)\n \n # 5. KDJ\u6307\u6807\n ax5 = plt.subplot(gs[4], sharex=ax1)\n ax5.plot(df.index, df['k'], label='K\u7ebf', color='blue')\n ax5.plot(df.index, df['d'], label='D\u7ebf', color='red')\n ax5.plot(df.index, df['j'], label='J\u7ebf', color='green')\n ax5.axhline(80, color='r', linestyle='--', alpha=0.7, label='\u8d85\u4e70\u7ebf(80)')\n ax5.axhline(20, color='g', linestyle='--', alpha=0.7, label='\u8d85\u5356\u7ebf(20)')\n ax5.set_ylabel('KDJ\u503c')\n ax5.set_ylim(0, 100)\n ax5.legend()\n ax5.grid(True, alpha=0.3)\n \n # 6. CCI\u6307\u6807\n ax6 = plt.subplot(gs[5], sharex=ax1)\n ax6.plot(df.index, df['cci'], label='CCI', color='orange', linewidth=2)\n ax6.axhline(100, color='r', linestyle='--', alpha=0.7, label='\u8d85\u4e70\u7ebf(100)')\n ax6.axhline(-100, color='g', linestyle='--', alpha=0.7, label='\u8d85\u5356\u7ebf(-100)')\n ax6.axhline(0, color='b', linestyle='--', alpha=0.5)\n ax6.set_ylabel('CCI\u503c')\n ax6.legend()\n ax6.grid(True, alpha=0.3)\n \n # 7. W&R\u548cBIAS\u6307\u6807\n ax7 = plt.subplot(gs[6], sharex=ax1)\n \n # W&R\n color1 = 'tab:red'\n ax7.set_ylabel('W&R(%)', color=color1)\n ax7.plot(df.index, df['wr'], color=color1, label='W&R')\n ax7.axhline(-20, color='r', linestyle='--', alpha=0.7, label='\u8d85\u4e70\u7ebf(-20)')\n ax7.axhline(-80, color='g', linestyle='--', alpha=0.7, label='\u8d85\u5356\u7ebf(-80)')\n ax7.tick_params(axis='y', labelcolor=color1)\n ax7.grid(True, alpha=0.3)\n \n # BIAS\n ax8 = ax7.twinx()\n color2 = 'tab:blue'\n ax8.set_ylabel('BIAS(%)', color=color2)\n ax8.plot(df.index, df['bias'], color=color2, label='BIAS')\n ax8.tick_params(axis='y', labelcolor=color2)\n \n # \u5408\u5e76\u56fe\u4f8b\n lines1, labels1 = ax7.get_legend_handles_labels()\n lines2, labels2 = ax8.get_legend_handles_labels()\n ax7.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n \n # 8. VI\u6307\u6807\n ax9 = plt.subplot(gs[7], sharex=ax1)\n ax9.plot(df.index, df['vi_positive'], 'g-', label='VI+', alpha=0.7)\n ax9.plot(df.index, df['vi_negative'], 'r-', label='VI-', alpha=0.7)\n ax9.set_ylabel('VI\u503c')\n ax9.legend()\n ax9.grid(True, alpha=0.3)\n \n # 9. OBV\u6307\u6807\n ax10 = plt.subplot(gs[8], sharex=ax1)\n ax10.set_ylabel('OBV')\n ax10.plot(df.index, df['obv'], color='tab:blue', label='OBV')\n ax10.tick_params(axis='y')\n ax10.legend()\n ax10.grid(True, alpha=0.3)\n \n # \u8bbe\u7f6ex\u8f74\u65e5\u671f\u683c\u5f0f\n ax10.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\n ax10.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n plt.xticks(rotation=45)\n \n plt.suptitle(f'{self.stock_code} \u6280\u672f\u6307\u6807\u5206\u6790 ({self.start_date} \u81f3 {self.end_date})', fontsize=16)\n plt.tight_layout()\n plt.subplots_adjust(top=0.97)\n plt.show(block=True) # \u963b\u585e\u663e\u793a\uff0c\u7b49\u5f85\u7528\u6237\u5173\u95ed\n \n return fig\n \n def plot_simplified(self):\n \"\"\"\u7ed8\u5236\u7b80\u5316\u7248\u56fe\u8868\uff08\u4ef7\u683c\u3001RSI\u3001MACD\uff09\"\"\"\n if self.data is None:\n print(\"\u8bf7\u5148\u83b7\u53d6\u80a1\u7968\u6570\u636e\u5e76\u8ba1\u7b97\u6307\u6807\")\n return None\n \n df = self.data.dropna()\n \n # \u76f4\u63a5\u521b\u5efa\u65b0\u56fe\u5f62\uff0c\u786e\u4fdd\u662f\u7b2c\u4e00\u4e2a\u663e\u793a\u7684\u56fe\u8868\n fig = plt.figure(1, figsize=(16, 15))\n fig.clf() # \u6e05\u9664\u56fe\u5f62\u5185\u5bb9\u4f46\u4fdd\u7559\u56fe\u5f62\u5b9e\u4f8b\n ax1 = plt.subplot(3, 1, 1)\n ax2 = plt.subplot(3, 1, 2, sharex=ax1)\n ax3 = plt.subplot(3, 1, 3, sharex=ax1)\n \n # 1. \u4ef7\u683c\u548c\u5e03\u6797\u5e26\n ax1.plot(df.index, df['close'], label='\u6536\u76d8\u4ef7', linewidth=2)\n ax1.plot(df.index, df['bb_upper'], 'r--', label='\u4e0a\u8f68')\n ax1.plot(df.index, df['bb_middle'], 'g--', label='\u4e2d\u8f68')\n ax1.plot(df.index, df['bb_lower'], 'r--', label='\u4e0b\u8f68')\n ax1.fill_between(df.index, df['bb_upper'], df['bb_lower'], alpha=0.1, color='gray')\n \n if 'final_signal' in df.columns:\n buy_signals = df[df['final_signal'] == 1]\n sell_signals = df[df['final_signal'] == -1]\n ax1.scatter(buy_signals.index, buy_signals['close'], marker='^', color='g', \n label='\u4e70\u5165\u4fe1\u53f7', s=100, zorder=3)\n ax1.scatter(sell_signals.index, sell_signals['close'], marker='v', color='r', \n label='\u5356\u51fa\u4fe1\u53f7', s=100, zorder=3)\n \n ax1.set_title(f'{self.stock_code} \u4ef7\u683c\u4e0e\u5e03\u6797\u5e26')\n ax1.set_ylabel('\u4ef7\u683c')\n ax1.legend()\n ax1.grid(True, alpha=0.3)\n \n # 2. RSI\n ax2.plot(df.index, df['rsi'], label='RSI', color='purple', linewidth=2)\n ax2.axhline(70, color='r', linestyle='--', alpha=0.7)\n ax2.axhline(30, color='g', linestyle='--', alpha=0.7)\n ax2.axhline(50, color='b', linestyle='--', alpha=0.5)\n ax2.set_ylabel('RSI\u503c')\n ax2.set_ylim(0, 100)\n ax2.legend()\n ax2.grid(True, alpha=0.3)\n \n # 3. MACD\n ax3.plot(df.index, df['macd'], label='MACD', color='blue')\n ax3.plot(df.index, df['macd_signal'], label='\u4fe1\u53f7\u7ebf', color='red')\n ax3.bar(df.index, df['macd_hist'], label='\u67f1\u72b6\u56fe', alpha=0.5)\n ax3.axhline(0, color='black', linestyle='-', alpha=0.3)\n ax3.set_ylabel('MACD\u503c')\n ax3.legend()\n ax3.grid(True, alpha=0.3)\n \n # \u8bbe\u7f6ex\u8f74\u65e5\u671f\u683c\u5f0f\n ax3.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\n ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n plt.xticks(rotation=45)\n \n plt.tight_layout()\n plt.show(block=True) # \u963b\u585e\u663e\u793a\uff0c\u7b49\u5f85\u7528\u6237\u5173\u95ed\n \n return fig\n\ndef main():\n # \u521b\u5efa\u5206\u6790\u5668\u5b9e\u4f8b\n analyzer = StockTechnicalAnalyzer(data_source='baostock') # \u53ef\u9009 'baostock' \u6216 'akshare'\n \n # \u8bbe\u7f6e\u80a1\u7968\u4ee3\u7801\u548c\u65e5\u671f\u8303\u56f4\n #stock_code = 'sh.600938' # \u4e2d\u56fd\u6d77\u6cb9\n stock_code = 'sz.000651' # \u683c\u529b\u7535\u5668\n end_date = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n start_date = (datetime.datetime.now() - datetime.timedelta(days=365*2)).strftime(\"%Y-%m-%d\") # \u8fc7\u53bb2\u5e74\n \n # \u83b7\u53d6\u6570\u636e\n data = analyzer.get_stock_data(stock_code, start_date, end_date)\n if data is None or len(data) < 60:\n print(\"\u6570\u636e\u4e0d\u8db3\uff0c\u65e0\u6cd5\u8fdb\u884c\u5206\u6790\")\n return\n \n # \u8ba1\u7b97\u6280\u672f\u6307\u6807\n analyzer.calculate_indicators()\n \n # \u751f\u6210\u4ea4\u6613\u4fe1\u53f7\n analyzer.generate_signals()\n \n # \u7ed8\u5236\u7b80\u5316\u56fe\u8868\n print(\"\u6b63\u5728\u7ed8\u5236\u7b80\u5316\u56fe\u8868...\")\n analyzer.plot_simplified()\n \n # \u5355\u72ec\u7ed8\u5236\u79fb\u52a8\u5e73\u5747\u7ebf\u56fe\u8868\n print(\"\u6b63\u5728\u7ed8\u5236\u79fb\u52a8\u5e73\u5747\u7ebf\u56fe\u8868...\")\n analyzer.plot_moving_averages()\n \n # \u7ed8\u5236\u5b8c\u6574\u56fe\u8868\uff08\u5305\u542b\u6240\u6709\u6307\u6807\uff0c\u4e0d\u542b\u8be6\u7ec6\u5747\u7ebf\uff09\n print(\"\u6b63\u5728\u7ed8\u5236\u5b8c\u6574\u6307\u6807\u56fe\u8868...\")\n analyzer.plot_all_indicators()\n \n # \u663e\u793a\u6700\u65b0\u7684\u6307\u6807\u503c\n latest_data = analyzer.data.iloc[-1]\n print(f\"\\n\u6700\u65b0\u6307\u6807\u503c\uff08{latest_data.name.date()}\uff09:\")\n print(f\"RSI(14): {latest_data['rsi']:.2f}\")\n print(f\"MACD: {latest_data['macd']:.4f}, \u4fe1\u53f7\u7ebf: {latest_data['macd_signal']:.4f}, \u67f1\u72b6\u56fe: {latest_data['macd_hist']:.4f}\")\n print(f\"KDJ: K={latest_data['k']:.2f}, D={latest_data['d']:.2f}, J={latest_data['j']:.2f}\")\n print(f\"ATR\u767e\u5206\u6bd4: {latest_data['atr_pct']:.2f}%\")\n print(f\"CCI: {latest_data['cci']:.2f}\")\n print(f\"W&R: {latest_data['wr']:.2f}%\")\n print(f\"BIAS: {latest_data['bias']:.2f}%\")\n print(f\"ROC: {latest_data['roc']:.2f}%\")\n print(f\"PSY: {latest_data['psy']:.2f}%\")\n print(f\"ENE: \u4e0a\u8f68={latest_data['ene_upper']:.2f}, \u4e2d\u8f68={latest_data['ene_middle']:.2f}, \u4e0b\u8f68={latest_data['ene_lower']:.2f}\")\n print(f\"VI: VI+={latest_data['vi_positive']:.2f}, VI-={latest_data['vi_negative']:.2f}\")\n\nif __name__ == \"__main__\":\n main()<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"