升级为本地数据分析版

app.py

@@ -1,54 +1,150 @@
 import gradio as gr
-import yfinance as yf
 import pandas as pd
 import plotly.graph_objects as go
 from prophet import Prophet
+import os
+
+
+DATA_DIR = "data"
+available_tickers = {}
+
+
+if os.path.exists(DATA_DIR):
+    for filename in os.listdir(DATA_DIR):
+        if filename.endswith(".csv"):
+            ticker_name = filename.replace(".csv", "").upper()
+            file_path = os.path.join(DATA_DIR, filename)
+            try:
+                # 尝试不同编码读取
+                for encoding in ['utf-8', 'gbk', 'latin1']:
+                    try:
+                        df = pd.read_csv(file_path, encoding=encoding)
+                        break
+                    except:
+                        continue
+                
+                # 识别日期列
+                date_cols = [col for col in df.columns if 'date' in col.lower() or 'time' in col.lower()]
+                if date_cols:
+                    df[date_cols[0]] = pd.to_datetime(df[date_cols[0]])
+                    df.set_index(date_cols[0], inplace=True)
+                
+                available_tickers[ticker_name] = {
+                    "file": file_path,
+                    "data": df
+                }
+                print(f"成功加载: {ticker_name}")
+            except Exception as e:
+                print(f"加载失败 {filename}: {str(e)}")
+else:
+    print(f"警告: 数据目录不存在 - {DATA_DIR}")
 
 def analyze_stock(ticker):
-    stock = yf.Ticker(ticker)
-    hist = stock.history(period="1y")
-    if hist.empty:
-        return "股票代码无效，请检查后重试（示例：AAPL, TSLA, 600036.SS）", None, None
-    
-    fig = go.Figure(data=[go.Candlestick(x=hist.index,
-                open=hist['Open'],
-                high=hist['High'],
-                low=hist['Low'],
-                close=hist['Close'])])
-    fig.update_layout(title=f"{ticker} 股票K线图", xaxis_title="日期", yaxis_title="价格")
-    
-    df = hist[['Close']].reset_index()
-    df.columns = ['ds', 'y']
-    model = Prophet(daily_seasonality=True, yearly_seasonality=True)
-    model.fit(df)
-    future = model.make_future_dataframe(periods=7)
-    forecast = model.predict(future)
-    
-    fig2 = go.Figure()
-    fig2.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='历史价格'))
-    fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='预测价格'))
-    fig2.update_layout(title=f"{ticker} 7天价格预测", xaxis_title="日期", yaxis_title="价格")
-    
-    hist['MA20'] = hist['Close'].rolling(20).mean()
-    current_price = hist['Close'].iloc[-1]
-    ma20 = hist['MA20'].iloc[-1]
-    signal = "📈 看涨" if current_price > ma20 else "📉 看跌"
-    
-    result_text = f"当前价格: ${current_price:.2f}\n20日均线: ${ma20:.2f}\n信号: {signal}"
-    return result_text, fig, fig2
-
-with gr.Blocks(title="股票AI分析") as demo:
-    gr.Markdown("# 📈 股票AI分析系统")
-    gr.Markdown("输入股票代码（美股直接输入，A股加.SS后缀，如`600036.SS`）")
-    
-    ticker_input = gr.Textbox(label="股票代码", value="AAPL")
-    analyze_btn = gr.Button("分析", variant="primary")
+    if not any(available_tickers):
+        return "❌ 错误: 没有找到任何数据文件。请检查data/目录", None, None
+    
+   
+    ticker_upper = ticker.upper()
+    matched_ticker = None
+    
+  
+    if ticker_upper in available_tickers:
+        matched_ticker = ticker_upper
+    else:
+  
+        for name in available_tickers.keys():
+            if ticker_upper in name or name in ticker_upper:
+                matched_ticker = name
+                break
+    
+    if not matched_ticker:
+        return f"❌ 未找到匹配的数据: {ticker}\n可用数据: {', '.join(available_tickers.keys())}", None, None
+    
+    try:
+        hist = available_tickers[matched_ticker]["data"]
+        
+     
+        required_cols = ['Open', 'High', 'Low', 'Close']
+        if not all(col in hist.columns for col in required_cols):
+            return f"❌ 数据格式错误: 缺少必要列。请确保CSV包含: {', '.join(required_cols)}", None, None
+        
+    
+        fig = go.Figure(data=[go.Candlestick(x=hist.index,
+                    open=hist['Open'],
+                    high=hist['High'],
+                    low=hist['Low'],
+                    close=hist['Close'])])
+        fig.update_layout(title=f"{matched_ticker} 股票K线图 (本地数据)", xaxis_title="日期", yaxis_title="价格")
+        
+ 
+        df = hist[['Close']].reset_index()
+        df.columns = ['ds', 'y']
+        model = Prophet(daily_seasonality=True, yearly_seasonality=True)
+        model.fit(df)
+        future = model.make_future_dataframe(periods=30)  # 30天预测
+        forecast = model.predict(future)
+        
+        fig2 = go.Figure()
+        fig2.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='历史价格'))
+        fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='预测价格'))
+        fig2.update_layout(title=f"{matched_ticker} 30天价格预测", xaxis_title="日期", yaxis_title="价格")
+        
+     
+        hist['MA20'] = hist['Close'].rolling(20).mean()
+        hist['MA50'] = hist['Close'].rolling(50).mean()
+        current_price = hist['Close'].iloc[-1]
+        ma20 = hist['MA20'].iloc[-1]
+        ma50 = hist['MA50'].iloc[-1]
+        
+ 
+        if current_price > ma20 > ma50:
+            signal = "📈 强烈看涨 (黄金交叉)"
+        elif current_price > ma20:
+            signal = "📈 看涨"
+        elif current_price < ma20 < ma50:
+            signal = "📉 强烈看跌 (死亡交叉)"
+        else:
+            signal = "🔄 震荡"
+        
+        result_text = (
+            f"📊 {matched_ticker} 分析结果\n"
+            f"💰 当前价格: ${current_price:.2f}\n"
+            f"📈 20日均线: ${ma20:.2f}\n"
+            f"📉 50日均线: ${ma50:.2f}\n"
+            f"🎯 信号: {signal}\n"
+            f"💾 数据来源: 本地文件 ({len(hist)} 条记录)"
+        )
+        
+        return result_text, fig, fig2
+        
+    except Exception as e:
+        return f"❌ 分析错误: {str(e)}", None, None
+
+def list_available_data():
+    if not available_tickers:
+        return "暂无可用数据文件"
+    return "可用数据: " + ", ".join(available_tickers.keys())
+
+with gr.Blocks(title="股票AI分析 (本地数据版)") as demo:
+    gr.Markdown("# 📈 股票AI分析系统 (本地历史数据版)")
+    gr.Markdown("✅ 优势: 无需网络，数据稳定，适合中长期分析")
+    
+    data_status = gr.Textbox(label="数据状态", value=list_available_data(), interactive=False)
+    
+    with gr.Row():
+        ticker_input = gr.Textbox(label="股票代码/名称", value="EURUSD")
+        analyze_btn = gr.Button("分析", variant="primary")
     
     signal_output = gr.Textbox(label="分析结果")
-    kchart = gr.Plot(label="K线图")
-    pred_chart = gr.Plot(label="价格预测")
+    with gr.Row():
+        kchart = gr.Plot(label="K线图")
+        pred_chart = gr.Plot(label="价格预测")
     
-    gr.Markdown("### 示例代码:\n- 美股: `AAPL`, `TSLA`, `GOOGL`\n- A股: `600036.SS`, `000001.SZ`\n- 港股: `00700.HK`")
+    gr.Markdown("### 使用指南:\n"
+               "1. 输入货币对名称（例如: EURUSD）\n"
+               "2. 系统自动从本地数据加载\n"
+               "3. 查看技术分析和30天预测\n"
+               "4. 定期更新data/目录中的CSV文件")
     
     analyze_btn.click(
         fn=analyze_stock,

requirements.txt

@@ -1,5 +1,4 @@
 gradio==4.29.0
-yfinance==0.2.37
 pandas==2.2.2
 numpy==1.26.4
 plotly==5.22.0