From 17ed403c3e96938092918dd8d16d24b958386e1a Mon Sep 17 00:00:00 2001 From: Cassianvale Date: Tue, 4 Mar 2025 13:01:38 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=94=AF=E6=8C=81=E8=87=AA=E5=AE=9A?= =?UTF-8?q?=E4=B9=89API?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- stock_analyzer.py | 687 ++++++++++++++-------------- templates/index.html | 1028 ++++++++++++++++++++++++------------------ web_server.py | 85 +++- 3 files changed, 1001 insertions(+), 799 deletions(-) diff --git a/stock_analyzer.py b/stock_analyzer.py index a88139f..105278d 100644 --- a/stock_analyzer.py +++ b/stock_analyzer.py @@ -1,343 +1,344 @@ -import pandas as pd -import numpy as np -from datetime import datetime, timedelta -import os -import requests -from typing import Dict, List, Optional, Tuple -from dotenv import load_dotenv - -class StockAnalyzer: - def __init__(self, initial_cash=1000000): - - # 加载环境变量 - load_dotenv() - - # 设置 Gemini API - self.API_URL = os.getenv('API_URL') - self.API_KEY = os.getenv('API_KEY') - - # 配置参数 - self.params = { - 'ma_periods': {'short': 5, 'medium': 20, 'long': 60}, - 'rsi_period': 14, - 'bollinger_period': 20, - 'bollinger_std': 2, - 'volume_ma_period': 20, - 'atr_period': 14 - } - - - def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None, ): - """获取股票数据""" - import akshare as ak - - if start_date is None: - start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d') - if end_date is None: - end_date = datetime.now().strftime('%Y%m%d') - - try: - # 根据市场类型获取数据 - if market_type == 'A': - df = ak.stock_zh_a_hist( - symbol=stock_code, - start_date=start_date, - end_date=end_date, - adjust="qfq" - ) - # A股数据列名映射 - elif market_type == 'HK': - df = ak.stock_hk_daily( - symbol=stock_code, - adjust="qfq" - ) - elif market_type == 'US': - df = ak.stock_us_hist( - symbol=stock_code, - start_date=start_date, - end_date=end_date, - adjust="qfq" - ) - # elif market_type == 'CRYPTO': - # df = ak.crypto_js_spot( - # symbol=stock_code - # ) - else: - raise ValueError(f"不支持的市场类型: {market_type}") - - # 重命名列名以匹配分析需求 - df = df.rename(columns={ - "日期": "date", - "开盘": "open", - "收盘": "close", - "最高": "high", - "最低": "low", - "成交量": "volume" - }) - - # 确保日期格式正确 - df['date'] = pd.to_datetime(df['date']) - - # 数据类型转换 - numeric_columns = ['open', 'close', 'high', 'low', 'volume'] - df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce') - - # 删除空值 - df = df.dropna() - - return df.sort_values('date') - - except Exception as e: - raise Exception(f"获取股票数据失败: {str(e)}") - - def calculate_ema(self, series, period): - """计算指数移动平均线""" - return series.ewm(span=period, adjust=False).mean() - - def calculate_rsi(self, series, period): - """计算RSI指标""" - delta = series.diff() - gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() - loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() - rs = gain / loss - return 100 - (100 / (1 + rs)) - - def calculate_macd(self, series): - """计算MACD指标""" - exp1 = series.ewm(span=12, adjust=False).mean() - exp2 = series.ewm(span=26, adjust=False).mean() - macd = exp1 - exp2 - signal = macd.ewm(span=9, adjust=False).mean() - hist = macd - signal - return macd, signal, hist - - def calculate_bollinger_bands(self, series, period, std_dev): - """计算布林带""" - middle = series.rolling(window=period).mean() - std = series.rolling(window=period).std() - upper = middle + (std * std_dev) - lower = middle - (std * std_dev) - return upper, middle, lower - - def calculate_atr(self, df, period): - """计算ATR指标""" - high = df['high'] - low = df['low'] - close = df['close'].shift(1) - - tr1 = high - low - tr2 = abs(high - close) - tr3 = abs(low - close) - - tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1) - return tr.rolling(window=period).mean() - - def calculate_indicators(self, df): - """计算技术指标""" - try: - # 计算移动平均线 - df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short']) - df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium']) - df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long']) - - # 计算RSI - df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period']) - - # 计算MACD - df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close']) - - # 计算布林带 - df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands( - df['close'], - self.params['bollinger_period'], - self.params['bollinger_std'] - ) - - # 成交量分析 - df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean() - df['Volume_Ratio'] = df['volume'] / df['Volume_MA'] - - # 计算ATR和波动率 - df['ATR'] = self.calculate_atr(df, self.params['atr_period']) - df['Volatility'] = df['ATR'] / df['close'] * 100 - - # 动量指标 - df['ROC'] = df['close'].pct_change(periods=10) * 100 - - return df - - except Exception as e: - print(f"计算技术指标时出错: {str(e)}") - raise - - def calculate_score(self, df): - """计算股票评分""" - try: - score = 0 - latest = df.iloc[-1] - - # 趋势得分 (30分) - if latest['MA5'] > latest['MA20']: - score += 15 - if latest['MA20'] > latest['MA60']: - score += 15 - - # RSI得分 (20分) - if 30 <= latest['RSI'] <= 70: - score += 20 - elif latest['RSI'] < 30: # 超卖 - score += 15 - - # MACD得分 (20分) - if latest['MACD'] > latest['Signal']: - score += 20 - - # 成交量得分 (30分) - if latest['Volume_Ratio'] > 1.5: - score += 30 - elif latest['Volume_Ratio'] > 1: - score += 15 - - return score - - except Exception as e: - print(f"计算评分时出错: {str(e)}") - raise - - def get_ai_analysis(self, df, stock_code): - """使用 OpenAI 进行 AI 分析""" - try: - recent_data = df.tail(14).to_dict('records') - - technical_summary = { - 'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward', - 'volatility': f"{df.iloc[-1]['Volatility']:.2f}%", - 'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing', - 'rsi_level': df.iloc[-1]['RSI'] - } - - prompt = f""" - 分析股票 {stock_code}： - - 技术指标概要： - {technical_summary} - - 近14日交易数据： - {recent_data} - - 请提供： - 1. 趋势分析（包含支撑位和压力位） - 2. 成交量分析及其含义 - 3. 风险评估（包含波动率分析） - 4. 短期和中期目标价位 - 5. 关键技术位分析 - 6. 具体交易建议（包含止损位） - - 请基于技术指标和市场动态进行分析，给出具体数据支持。 - """ - - # OpenAI API 调用 - api_urls = [ - f"{self.API_URL}/chat/completions", - f"{self.API_URL}/v1/chat/completions" - ] - - last_error = None - for api_url in api_urls: - try: - response = requests.post( - api_url, - headers={ - "Authorization": f"Bearer {self.API_KEY}", - "Content-Type": "application/json" - }, - json={ - "model": os.getenv('API_MODEL', 'gpt-3.5-turbo'), - "messages": [{"role": "user", "content": prompt}] - }, - timeout=30 - ) - - if response.status_code == 200: - return response.json()['choices'][0]['message']['content'] - else: - last_error = f"API 错误: {response.status_code} - {response.text}" - continue - - except Exception as e: - last_error = str(e) - continue - - print(f"AI 分析暂时无法使用: {last_error}") - return f"AI 分析暂时无法使用: {last_error}" - - except Exception as e: - print(f"AI 分析发生错误: {str(e)}") - return f"AI 分析过程中发生错误: {str(e)}" - - def get_recommendation(self, score): - """根据得分给出建议""" - if score >= 80: - return '强烈推荐买入' - elif score >= 60: - return '建议买入' - elif score >= 40: - return '观望' - elif score >= 20: - return '建议卖出' - else: - return '强烈建议卖出' - - def analyze_stock(self, stock_code, market_type='A'): - """分析单个股票""" - try: - # 获取股票数据 - df = self.get_stock_data(stock_code, market_type) - - # 计算技术指标 - df = self.calculate_indicators(df) - - # 评分系统 - score = self.calculate_score(df) - - # 获取最新数据 - latest = df.iloc[-1] - prev = df.iloc[-2] - - # 生成报告（保持原有格式） - report = { - 'stock_code': stock_code, - 'analysis_date': datetime.now().strftime('%Y-%m-%d'), - 'score': score, - 'price': latest['close'], - 'price_change': (latest['close'] - prev['close']) / prev['close'] * 100, - 'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN', - 'rsi': latest['RSI'], - 'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL', - 'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL', - 'recommendation': self.get_recommendation(score), - 'ai_analysis': self.get_ai_analysis(df, stock_code) - } - - return report - - except Exception as e: - print(f"分析股票时出错: {str(e)}") - raise - - def scan_market(self, stock_list, min_score=60, market_type='A'): - """扫描市场，寻找符合条件的股票""" - recommendations = [] - - for stock_code in stock_list: - try: - report = self.analyze_stock(stock_code, market_type) - if report['score'] >= min_score: - recommendations.append(report) - except Exception as e: - print(f"分析股票 {stock_code} 时出错: {str(e)}") - continue - - # 按得分排序 - recommendations.sort(key=lambda x: x['score'], reverse=True) - return recommendations +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +import os +import requests +from typing import Dict, List, Optional, Tuple +from dotenv import load_dotenv + +class StockAnalyzer: + def __init__(self, initial_cash=1000000, custom_api_url=None, custom_api_key=None, custom_api_model=None): + + # 加载环境变量 + load_dotenv() + + # 设置 API 配置，优先使用自定义配置，否则使用环境变量 + self.API_URL = custom_api_url or os.getenv('API_URL') + self.API_KEY = custom_api_key or os.getenv('API_KEY') + self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo') + + # 配置参数 + self.params = { + 'ma_periods': {'short': 5, 'medium': 20, 'long': 60}, + 'rsi_period': 14, + 'bollinger_period': 20, + 'bollinger_std': 2, + 'volume_ma_period': 20, + 'atr_period': 14 + } + + + def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None, ): + """获取股票数据""" + import akshare as ak + + if start_date is None: + start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d') + if end_date is None: + end_date = datetime.now().strftime('%Y%m%d') + + try: + # 根据市场类型获取数据 + if market_type == 'A': + df = ak.stock_zh_a_hist( + symbol=stock_code, + start_date=start_date, + end_date=end_date, + adjust="qfq" + ) + # A股数据列名映射 + elif market_type == 'HK': + df = ak.stock_hk_daily( + symbol=stock_code, + adjust="qfq" + ) + elif market_type == 'US': + df = ak.stock_us_hist( + symbol=stock_code, + start_date=start_date, + end_date=end_date, + adjust="qfq" + ) + # elif market_type == 'CRYPTO': + # df = ak.crypto_js_spot( + # symbol=stock_code + # ) + else: + raise ValueError(f"不支持的市场类型: {market_type}") + + # 重命名列名以匹配分析需求 + df = df.rename(columns={ + "日期": "date", + "开盘": "open", + "收盘": "close", + "最高": "high", + "最低": "low", + "成交量": "volume" + }) + + # 确保日期格式正确 + df['date'] = pd.to_datetime(df['date']) + + # 数据类型转换 + numeric_columns = ['open', 'close', 'high', 'low', 'volume'] + df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce') + + # 删除空值 + df = df.dropna() + + return df.sort_values('date') + + except Exception as e: + raise Exception(f"获取股票数据失败: {str(e)}") + + def calculate_ema(self, series, period): + """计算指数移动平均线""" + return series.ewm(span=period, adjust=False).mean() + + def calculate_rsi(self, series, period): + """计算RSI指标""" + delta = series.diff() + gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() + rs = gain / loss + return 100 - (100 / (1 + rs)) + + def calculate_macd(self, series): + """计算MACD指标""" + exp1 = series.ewm(span=12, adjust=False).mean() + exp2 = series.ewm(span=26, adjust=False).mean() + macd = exp1 - exp2 + signal = macd.ewm(span=9, adjust=False).mean() + hist = macd - signal + return macd, signal, hist + + def calculate_bollinger_bands(self, series, period, std_dev): + """计算布林带""" + middle = series.rolling(window=period).mean() + std = series.rolling(window=period).std() + upper = middle + (std * std_dev) + lower = middle - (std * std_dev) + return upper, middle, lower + + def calculate_atr(self, df, period): + """计算ATR指标""" + high = df['high'] + low = df['low'] + close = df['close'].shift(1) + + tr1 = high - low + tr2 = abs(high - close) + tr3 = abs(low - close) + + tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1) + return tr.rolling(window=period).mean() + + def calculate_indicators(self, df): + """计算技术指标""" + try: + # 计算移动平均线 + df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short']) + df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium']) + df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long']) + + # 计算RSI + df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period']) + + # 计算MACD + df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close']) + + # 计算布林带 + df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands( + df['close'], + self.params['bollinger_period'], + self.params['bollinger_std'] + ) + + # 成交量分析 + df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean() + df['Volume_Ratio'] = df['volume'] / df['Volume_MA'] + + # 计算ATR和波动率 + df['ATR'] = self.calculate_atr(df, self.params['atr_period']) + df['Volatility'] = df['ATR'] / df['close'] * 100 + + # 动量指标 + df['ROC'] = df['close'].pct_change(periods=10) * 100 + + return df + + except Exception as e: + print(f"计算技术指标时出错: {str(e)}") + raise + + def calculate_score(self, df): + """计算股票评分""" + try: + score = 0 + latest = df.iloc[-1] + + # 趋势得分 (30分) + if latest['MA5'] > latest['MA20']: + score += 15 + if latest['MA20'] > latest['MA60']: + score += 15 + + # RSI得分 (20分) + if 30 <= latest['RSI'] <= 70: + score += 20 + elif latest['RSI'] < 30: # 超卖 + score += 15 + + # MACD得分 (20分) + if latest['MACD'] > latest['Signal']: + score += 20 + + # 成交量得分 (30分) + if latest['Volume_Ratio'] > 1.5: + score += 30 + elif latest['Volume_Ratio'] > 1: + score += 15 + + return score + + except Exception as e: + print(f"计算评分时出错: {str(e)}") + raise + + def get_ai_analysis(self, df, stock_code): + """使用 OpenAI 进行 AI 分析""" + try: + recent_data = df.tail(14).to_dict('records') + + technical_summary = { + 'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward', + 'volatility': f"{df.iloc[-1]['Volatility']:.2f}%", + 'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing', + 'rsi_level': df.iloc[-1]['RSI'] + } + + prompt = f""" + 分析股票 {stock_code}： + + 技术指标概要： + {technical_summary} + + 近14日交易数据： + {recent_data} + + 请提供： + 1. 趋势分析（包含支撑位和压力位） + 2. 成交量分析及其含义 + 3. 风险评估（包含波动率分析） + 4. 短期和中期目标价位 + 5. 关键技术位分析 + 6. 具体交易建议（包含止损位） + + 请基于技术指标和市场动态进行分析，给出具体数据支持。 + """ + + # OpenAI API 调用 + api_urls = [ + f"{self.API_URL}/chat/completions", + f"{self.API_URL}/v1/chat/completions" + ] + + last_error = None + for api_url in api_urls: + try: + response = requests.post( + api_url, + headers={ + "Authorization": f"Bearer {self.API_KEY}", + "Content-Type": "application/json" + }, + json={ + "model": self.API_MODEL, + "messages": [{"role": "user", "content": prompt}] + }, + timeout=30 + ) + + if response.status_code == 200: + return response.json()['choices'][0]['message']['content'] + else: + last_error = f"API 错误: {response.status_code} - {response.text}" + continue + + except Exception as e: + last_error = str(e) + continue + + print(f"AI 分析暂时无法使用: {last_error}") + return f"AI 分析暂时无法使用: {last_error}" + + except Exception as e: + print(f"AI 分析发生错误: {str(e)}") + return f"AI 分析过程中发生错误: {str(e)}" + + def get_recommendation(self, score): + """根据得分给出建议""" + if score >= 80: + return '强烈推荐买入' + elif score >= 60: + return '建议买入' + elif score >= 40: + return '观望' + elif score >= 20: + return '建议卖出' + else: + return '强烈建议卖出' + + def analyze_stock(self, stock_code, market_type='A'): + """分析单个股票""" + try: + # 获取股票数据 + df = self.get_stock_data(stock_code, market_type) + + # 计算技术指标 + df = self.calculate_indicators(df) + + # 评分系统 + score = self.calculate_score(df) + + # 获取最新数据 + latest = df.iloc[-1] + prev = df.iloc[-2] + + # 生成报告（保持原有格式） + report = { + 'stock_code': stock_code, + 'analysis_date': datetime.now().strftime('%Y-%m-%d'), + 'score': score, + 'price': latest['close'], + 'price_change': (latest['close'] - prev['close']) / prev['close'] * 100, + 'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN', + 'rsi': latest['RSI'], + 'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL', + 'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL', + 'recommendation': self.get_recommendation(score), + 'ai_analysis': self.get_ai_analysis(df, stock_code) + } + + return report + + except Exception as e: + print(f"分析股票时出错: {str(e)}") + raise + + def scan_market(self, stock_list, min_score=60, market_type='A'): + """扫描市场，寻找符合条件的股票""" + recommendations = [] + + for stock_code in stock_list: + try: + report = self.analyze_stock(stock_code, market_type) + if report['score'] >= min_score: + recommendations.append(report) + except Exception as e: + print(f"分析股票 {stock_code} 时出错: {str(e)}") + continue + + # 按得分排序 + recommendations.sort(key=lambda x: x['score'], reverse=True) + return recommendations diff --git a/templates/index.html b/templates/index.html index aee282d..3074210 100644 --- a/templates/index.html +++ b/templates/index.html @@ -1,451 +1,579 @@ - 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+
+
+ + + + + + + + + \ No newline at end of file diff --git a/web_server.py b/web_server.py index b6152fe..dc54012 100644 --- a/web_server.py +++ b/web_server.py @@ -3,6 +3,8 @@ from stock_analyzer import StockAnalyzer from us_stock_service import USStockService import threading import os +import traceback +import requests app = Flask(__name__) analyzer = StockAnalyzer() @@ -11,7 +13,14 @@ us_stock_service = USStockService() @app.route('/') def index(): announcement = os.getenv('ANNOUNCEMENT_TEXT') or None - return render_template('index.html', announcement=announcement) + # 获取默认API配置信息 + default_api_url = os.getenv('API_URL', '') + default_api_model = os.getenv('API_MODEL', 'gpt-3.5-turbo') + # 不传递API_KEY到前端，出于安全考虑 + return render_template('index.html', + announcement=announcement, + default_api_url=default_api_url, + default_api_model=default_api_model) @app.route('/analyze', methods=['POST']) def analyze(): @@ -20,13 +29,26 @@ def analyze(): stock_codes = data.get('stock_codes', []) market_type = data.get('market_type', 'A') + # 获取自定义API配置 + custom_api_url = data.get('api_url') + custom_api_key = data.get('api_key') + custom_api_model = data.get('api_model') + + # 创建新的分析器实例，使用自定义配置 + custom_analyzer = StockAnalyzer( + custom_api_url=custom_api_url, + custom_api_key=custom_api_key, + custom_api_model=custom_api_model + ) + if not stock_codes: return jsonify({'error': '请输入代码'}), 400 results = [] for stock_code in stock_codes: try: - result = analyzer.analyze_stock(stock_code.strip(), market_type) + # 使用自定义配置的分析器 + result = custom_analyzer.analyze_stock(stock_code.strip(), market_type) results.append(result) except Exception as e: print(f"分析股票 {stock_code} 失败: {str(e)}") @@ -55,8 +77,59 @@ def search_us_stocks(): print(f"搜索美股代码时出错: {str(e)}") return jsonify({'error': str(e)}), 500 +@app.route('/test_api_connection', methods=['POST']) +def test_api_connection(): + """测试API连接""" + try: + data = request.json + api_url = data.get('api_url') + api_key = data.get('api_key') + api_model = data.get('api_model') + + if not api_url: + return jsonify({'error': '请提供API URL'}), 400 + + if not api_key: + return jsonify({'error': '请提供API Key'}), 400 + + # 构建API URL + test_url = api_url + if not (api_url.endswith('/chat/completions') or api_url.endswith('/v1/chat/completions')): + if api_url.endswith('/v1'): + test_url = f"{api_url}/chat/completions" + elif api_url.endswith('/'): + test_url = f"{api_url}chat/completions" + else: + test_url = f"{api_url}/v1/chat/completions" + + # 发送测试请求 + response = requests.post( + test_url, + headers={ + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json" + }, + json={ + "model": api_model or "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "Hello, this is a test message. Please respond with 'API connection successful'."} + ], + "max_tokens": 20 + }, + timeout=10 + ) + + # 检查响应 + if response.status_code == 200: + return jsonify({'success': True, 'message': '连接成功'}) + else: + error_message = response.json().get('error', {}).get('message', '未知错误') + return jsonify({'success': False, 'message': f'连接失败: {error_message}', 'status_code': response.status_code}), 400 + + except requests.exceptions.RequestException as e: + return jsonify({'success': False, 'message': f'请求错误: {str(e)}'}), 400 + except Exception as e: + return jsonify({'success': False, 'message': f'测试连接时出错: {str(e)}'}), 500 + if __name__ == '__main__': - app.run(host='0.0.0.0', port=8888, debug=True) - - - \ No newline at end of file + app.run(host='0.0.0.0', port=8888, debug=True) \ No newline at end of file