From 35d35f740f85707d9a16a699aafbf72356383f65 Mon Sep 17 00:00:00 2001 From: Charles Date: Tue, 26 Aug 2025 17:13:18 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0=E8=AF=A6=E7=BB=86?= =?UTF-8?q?=E7=9A=84=E8=B0=83=E8=AF=95=E4=BF=A1=E6=81=AF=EF=BC=8C=E6=98=BE?= =?UTF-8?q?=E7=A4=BA=E4=BB=B7=E6=A0=BC=E8=BD=AC=E6=8D=A2=E7=9A=84=E6=AF=8F?= =?UTF-8?q?=E4=B8=80=E6=AD=A5=E6=93=8D=E4=BD=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- webui/app.py | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/webui/app.py b/webui/app.py index b478cd8..5f36971 100644 --- a/webui/app.py +++ b/webui/app.py @@ -124,27 +124,44 @@ def load_data_file(file_path): def convert_price_to_returns(df, price_cols=['open', 'high', 'low', 'close']): """将绝对价格转换为相对变化率，解决Kronos模型的连续性问题""" + print(f"[DEBUG] convert_price_to_returns: 开始转换，输入数据形状: {df.shape}") + print(f"[DEBUG] convert_price_to_returns: 输入数据前3行:") + print(df[price_cols].head(3)) + returns_df = df.copy() # 计算相对变化率 (pct_change) for col in price_cols: if col in returns_df.columns: + print(f"[DEBUG] convert_price_to_returns: 转换列 {col}") returns_df[col] = returns_df[col].pct_change() # 第一行会是NaN，用0填充 returns_df = returns_df.fillna(0) + print(f"[DEBUG] convert_price_to_returns: 转换完成，输出数据前3行:") + print(returns_df[price_cols].head(3)) + return returns_df def convert_returns_to_price(returns_df, initial_prices, price_cols=['open', 'high', 'low', 'close']): """将相对变化率转换回绝对价格""" + print(f"[DEBUG] convert_returns_to_price: 开始转换，输入数据形状: {returns_df.shape}") + print(f"[DEBUG] convert_returns_to_price: 初始价格: {initial_prices}") + print(f"[DEBUG] convert_returns_to_price: 输入相对变化率前3行:") + print(returns_df[price_cols].head(3)) + price_df = returns_df.copy() for col in price_cols: if col in price_df.columns: + print(f"[DEBUG] convert_returns_to_price: 转换列 {col}") # 从初始价格开始，逐步计算绝对价格 price_df[col] = (1 + returns_df[col]) * initial_prices[col] + print(f"[DEBUG] convert_returns_to_price: 转换完成，输出绝对价格前3行:") + print(price_df[price_cols].head(3)) + return price_df def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params): @@ -495,12 +512,16 @@ def predict(): y_timestamp = df.iloc[lookback:lookback+pred_len]['timestamps'] prediction_type = "Kronos模型预测 (最新数据)" + print(f"[DEBUG] 步骤1: 检查并转换时间戳格式...") # 确保时间戳是Series格式，不是DatetimeIndex，避免Kronos模型的.dt属性错误 if isinstance(x_timestamp, pd.DatetimeIndex): + print(f"[DEBUG] 将x_timestamp从DatetimeIndex转换为Series") x_timestamp = pd.Series(x_timestamp, name='timestamps') if isinstance(y_timestamp, pd.DatetimeIndex): + print(f"[DEBUG] 将y_timestamp从DatetimeIndex转换为Series") y_timestamp = pd.Series(y_timestamp, name='timestamps') + print(f"[DEBUG] 步骤2: 准备价格转换，保存初始价格...") # 解决Kronos模型连续性问题：将绝对价格转换为相对变化率 original_x_df = x_df.copy() initial_prices = { @@ -509,10 +530,15 @@ def predict(): 'low': x_df['low'].iloc[0], 'close': x_df['close'].iloc[0] } + print(f"[DEBUG] 初始价格: {initial_prices}") + print(f"[DEBUG] 步骤3: 将绝对价格转换为相对变化率...") # 转换为相对变化率 x_df_returns = convert_price_to_returns(x_df, ['open', 'high', 'low', 'close']) + print(f"[DEBUG] 转换完成，输入数据前5行相对变化率:") + print(x_df_returns[['open', 'high', 'low', 'close']].head()) + print(f"[DEBUG] 步骤4: 使用转换后的数据进行Kronos模型预测...") # 使用转换后的数据进行预测 pred_df_returns = predictor.predict( df=x_df_returns, @@ -523,15 +549,27 @@ def predict(): top_p=top_p, sample_count=sample_count ) + print(f"[DEBUG] 预测完成，预测结果前5行相对变化率:") + print(pred_df_returns[['open', 'high', 'low', 'close']].head()) + print(f"[DEBUG] 步骤5: 将预测结果转换回绝对价格...") # 将预测结果转换回绝对价格 pred_df = convert_returns_to_price(pred_df_returns, initial_prices, ['open', 'high', 'low', 'close']) + print(f"[DEBUG] 转换完成，预测结果前5行绝对价格:") + print(pred_df[['open', 'high', 'low', 'close']].head()) + print(f"[DEBUG] 步骤6: 处理volume和amount列...") # 保持volume列不变（如果存在） if 'volume' in pred_df.columns: pred_df['volume'] = pred_df_returns['volume'] + print(f"[DEBUG] 已复制volume列") if 'amount' in pred_df.columns: pred_df['amount'] = pred_df_returns['amount'] + print(f"[DEBUG] 已复制amount列") + + print(f"[DEBUG] 步骤7: 检查最终预测结果...") + print(f"[DEBUG] 最终预测结果形状: {pred_df.shape}") + print(f"[DEBUG] 最终预测结果列: {list(pred_df.columns)}") except Exception as e: return jsonify({'error': f'Kronos模型预测失败: {str(e)}'}), 500