import os import pandas as pd import numpy as np import json import plotly.graph_objects as go import plotly.utils from flask import Flask, render_template, request, jsonify from flask_cors import CORS import sys import warnings import datetime import baostock as bs import re warnings.filterwarnings('ignore') # Add project root directory to path sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) try: from model import Kronos, KronosTokenizer, KronosPredictor MODEL_AVAILABLE = True except ImportError: MODEL_AVAILABLE = False print("Warning: Kronos model cannot be imported, will use simulated data for demonstration") app = Flask(__name__) CORS(app) # Global variables to store models tokenizer = None model = None predictor = None # 获取webui目录的路径 WEBUI_DIR = os.path.dirname(os.path.abspath(__file__)) # 获取项目根目录（webui的父目录） BASE_DIR = os.path.dirname(WEBUI_DIR) AVAILABLE_MODELS = { 'kronos-mini': { 'name': 'Kronos-mini', 'model_id': os.path.join(BASE_DIR, 'models', 'Kronos-mini'), 'tokenizer_id': os.path.join(BASE_DIR, 'models', 'Kronos-Tokenizer-base'), 'context_length': 2048, 'params': '4.1M', 'description': '轻量级模型，适合快速预测' }, 'kronos-small': { 'name': 'Kronos-small', 'model_id': os.path.join(BASE_DIR, 'models', 'NeoQuasarKronos-small'), 'tokenizer_id': os.path.join(BASE_DIR, 'models', 'Kronos-Tokenizer-base'), 'context_length': 512, 'params': '24.7M', 'description': '小型模型，平衡性能和速度' }, 'kronos-base': { 'name': 'Kronos-base', 'model_id': os.path.join(BASE_DIR, 'models', 'NeoQuasarKronos-base'), 'tokenizer_id': os.path.join(BASE_DIR, 'models', 'Kronos-Tokenizer-base'), 'context_length': 512, 'params': '102.3M', 'description': '基础模型，提供更好的预测质量' } } # Available model configurations # AVAILABLE_MODELS = { # 'kronos-mini': { # 'name': 'Kronos-mini', # 'model_id': 'models/Kronos-mini', # 本地路径 # 'tokenizer_id': 'models/Kronos-Tokenizer-base', # 本地路径 # 'context_length': 2048, # 'params': '4.1M', # 'description': '轻量级模型，适合快速预测' # }, # 'kronos-small': { # 'name': 'Kronos-small', # 'model_id': 'models/NeoQuasarKronos-small', # 本地路径 # 'tokenizer_id': 'models/Kronos-Tokenizer-base', # 本地路径 # 'context_length': 512, # 'params': '24.7M', # 'description': '小型模型，平衡性能和速度' # }, # 'kronos-base': { # 'name': 'Kronos-base', # 'model_id': 'models/NeoQuasarKronos-base', # 本地路径 # 'tokenizer_id': 'models/Kronos-Tokenizer-base', # 本地路径 # 'context_length': 512, # 'params': '102.3M', # 'description': '基础模型，提供更好的预测质量' # } # } def load_data_files(): """Scan data directory and return available data files""" data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data') data_files = [] if os.path.exists(data_dir): for file in os.listdir(data_dir): if file.endswith(('.csv', '.feather')): file_path = os.path.join(data_dir, file) file_size = os.path.getsize(file_path) data_files.append({ 'name': file, 'path': file_path, 'size': f"{file_size / 1024:.1f} KB" if file_size < 1024*1024 else f"{file_size / (1024*1024):.1f} MB" }) return data_files def load_data_file(file_path): """Load data file""" try: if file_path.endswith('.csv'): df = pd.read_csv(file_path) elif file_path.endswith('.feather'): df = pd.read_feather(file_path) else: return None, "Unsupported file format" # Check required columns required_cols = ['open', 'high', 'low', 'close'] if not all(col in df.columns for col in required_cols): return None, f"Missing required columns: {required_cols}" # Process timestamp column if 'timestamps' in df.columns: df['timestamps'] = pd.to_datetime(df['timestamps']) elif 'timestamp' in df.columns: df['timestamps'] = pd.to_datetime(df['timestamp']) elif 'date' in df.columns: # If column name is 'date', rename it to 'timestamps' df['timestamps'] = pd.to_datetime(df['date']) else: # If no timestamp column exists, create one df['timestamps'] = pd.date_range(start='2024-01-01', periods=len(df), freq='1H') # Ensure numeric columns are numeric type for col in ['open', 'high', 'low', 'close']: df[col] = pd.to_numeric(df[col], errors='coerce') # Process volume column (optional) if 'volume' in df.columns: df['volume'] = pd.to_numeric(df['volume'], errors='coerce') # Process amount column (optional, but not used for prediction) if 'amount' in df.columns: df['amount'] = pd.to_numeric(df['amount'], errors='coerce') # Remove rows containing NaN values df = df.dropna() return df, None except Exception as e: return None, f"Failed to load file: {str(e)}" def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params): """Save prediction results to file""" try: # Create prediction results directory results_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prediction_results') os.makedirs(results_dir, exist_ok=True) # Generate filename timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S') filename = f'prediction_{timestamp}.json' filepath = os.path.join(results_dir, filename) # Prepare data for saving save_data = { 'timestamp': datetime.datetime.now().isoformat(), 'file_path': file_path, 'prediction_type': prediction_type, 'prediction_params': prediction_params, 'input_data_summary': { 'rows': len(input_data), 'columns': list(input_data.columns), 'price_range': { 'open': {'min': float(input_data['open'].min()), 'max': float(input_data['open'].max())}, 'high': {'min': float(input_data['high'].min()), 'max': float(input_data['high'].max())}, 'low': {'min': float(input_data['low'].min()), 'max': float(input_data['low'].max())}, 'close': {'min': float(input_data['close'].min()), 'max': float(input_data['close'].max())} }, 'last_values': { 'open': float(input_data['open'].iloc[-1]), 'high': float(input_data['high'].iloc[-1]), 'low': float(input_data['low'].iloc[-1]), 'close': float(input_data['close'].iloc[-1]) } }, 'prediction_results': prediction_results, 'actual_data': actual_data, 'analysis': {} } # If actual data exists, perform comparison analysis if actual_data and len(actual_data) > 0: # Calculate continuity analysis if len(prediction_results) > 0 and len(actual_data) > 0: last_pred = prediction_results[0] # First prediction point first_actual = actual_data[0] # First actual point save_data['analysis']['continuity'] = { 'last_prediction': { 'open': last_pred['open'], 'high': last_pred['high'], 'low': last_pred['low'], 'close': last_pred['close'] }, 'first_actual': { 'open': first_actual['open'], 'high': first_actual['high'], 'low': first_actual['low'], 'close': first_actual['close'] }, 'gaps': { 'open_gap': abs(last_pred['open'] - first_actual['open']), 'high_gap': abs(last_pred['high'] - first_actual['high']), 'low_gap': abs(last_pred['low'] - first_actual['low']), 'close_gap': abs(last_pred['close'] - first_actual['close']) }, 'gap_percentages': { 'open_gap_pct': (abs(last_pred['open'] - first_actual['open']) / first_actual['open']) * 100, 'high_gap_pct': (abs(last_pred['high'] - first_actual['high']) / first_actual['high']) * 100, 'low_gap_pct': (abs(last_pred['low'] - first_actual['low']) / first_actual['low']) * 100, 'close_gap_pct': (abs(last_pred['close'] - first_actual['close']) / first_actual['close']) * 100 } } # Save to file with open(filepath, 'w', encoding='utf-8') as f: json.dump(save_data, f, indent=2, ensure_ascii=False) print(f"Prediction results saved to: {filepath}") return filepath except Exception as e: print(f"Failed to save prediction results: {e}") return None # def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0): # """Create prediction chart""" # # print(f"🔍 创建图表调试:") # print(f" 历史数据: {len(df) if df is not None else 0} 行") # print(f" 预测数据: {len(pred_df) if pred_df is not None else 0} 行") # print(f" 实际数据: {len(actual_df) if actual_df is not None else 0} 行") # # # 确保数据不为空 # if pred_df is None or len(pred_df) == 0: # print("⚠️ 警告: 预测数据为空!") # # 创建空图表 # fig = go.Figure() # fig.update_layout(title='No prediction data available') # return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder) # # # 其余代码保持不变... # # # Use specified historical data start position, not always from the beginning of df # if historical_start_idx + lookback + pred_len <= len(df): # # Display lookback historical points + pred_len prediction points starting from specified position # historical_df = df.iloc[historical_start_idx:historical_start_idx+lookback] # prediction_range = range(historical_start_idx+lookback, historical_start_idx+lookback+pred_len) # else: # # If data is insufficient, adjust to maximum available range # available_lookback = min(lookback, len(df) - historical_start_idx) # available_pred_len = min(pred_len, max(0, len(df) - historical_start_idx - available_lookback)) # historical_df = df.iloc[historical_start_idx:historical_start_idx+available_lookback] # prediction_range = range(historical_start_idx+available_lookback, historical_start_idx+available_lookback+available_pred_len) # # # Create chart # fig = go.Figure() # # # Add historical data (candlestick chart) # fig.add_trace(go.Candlestick( # x=historical_df['timestamps'] if 'timestamps' in historical_df.columns else historical_df.index, # open=historical_df['open'], # high=historical_df['high'], # low=historical_df['low'], # close=historical_df['close'], # name='Historical Data (400 data points)', # increasing_line_color='#26A69A', # decreasing_line_color='#EF5350' # )) # # # Add prediction data (candlestick chart) # if pred_df is not None and len(pred_df) > 0: # # Calculate prediction data timestamps - ensure continuity with historical data # if 'timestamps' in df.columns and len(historical_df) > 0: # # Start from the last timestamp of historical data, create prediction timestamps with the same time interval # last_timestamp = historical_df['timestamps'].iloc[-1] # time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1) # # pred_timestamps = pd.date_range( # start=last_timestamp + time_diff, # periods=len(pred_df), # freq=time_diff # ) # else: # # If no timestamps, use index # pred_timestamps = range(len(historical_df), len(historical_df) + len(pred_df)) # # fig.add_trace(go.Candlestick( # x=pred_timestamps, # open=pred_df['open'], # high=pred_df['high'], # low=pred_df['low'], # close=pred_df['close'], # name='Prediction Data (120 data points)', # increasing_line_color='#66BB6A', # decreasing_line_color='#FF7043' # )) # # # Add actual data for comparison (if exists) # if actual_df is not None and len(actual_df) > 0: # # Actual data should be in the same time period as prediction data # if 'timestamps' in df.columns: # # Actual data should use the same timestamps as prediction data to ensure time alignment # if 'pred_timestamps' in locals(): # actual_timestamps = pred_timestamps # else: # # If no prediction timestamps, calculate from the last timestamp of historical data # if len(historical_df) > 0: # last_timestamp = historical_df['timestamps'].iloc[-1] # time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1) # actual_timestamps = pd.date_range( # start=last_timestamp + time_diff, # periods=len(actual_df), # freq=time_diff # ) # else: # actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df)) # else: # actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df)) # # fig.add_trace(go.Candlestick( # x=actual_timestamps, # open=actual_df['open'], # high=actual_df['high'], # low=actual_df['low'], # close=actual_df['close'], # name='Actual Data (120 data points)', # increasing_line_color='#FF9800', # decreasing_line_color='#F44336' # )) # # # Update layout # fig.update_layout( # title='Kronos Financial Prediction Results - 400 Historical Points + 120 Prediction Points vs 120 Actual Points', # xaxis_title='Time', # yaxis_title='Price', # template='plotly_white', # height=600, # showlegend=True # ) # # # Ensure x-axis time continuity # if 'timestamps' in historical_df.columns: # # Get all timestamps and sort them # all_timestamps = [] # if len(historical_df) > 0: # all_timestamps.extend(historical_df['timestamps']) # if 'pred_timestamps' in locals(): # all_timestamps.extend(pred_timestamps) # if 'actual_timestamps' in locals(): # all_timestamps.extend(actual_timestamps) # # if all_timestamps: # all_timestamps = sorted(all_timestamps) # fig.update_xaxes( # range=[all_timestamps[0], all_timestamps[-1]], # rangeslider_visible=False, # type='date' # ) # # # 修改这一行： # # return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder) # # # 改为： # try: # chart_json = fig.to_json() # print(f"✅ 图表JSON序列化成功，长度: {len(chart_json)}") # return chart_json # except Exception as e: # print(f"❌ 图表序列化失败: {e}") # # 返回一个简单的错误图表 # error_fig = go.Figure() # error_fig.update_layout(title='Chart Rendering Error') # return error_fig.to_json() def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0): """Create prediction chart""" print(f"🔍 创建图表调试:") print(f" 历史数据: {len(df) if df is not None else 0} 行") print(f" 预测数据: {len(pred_df) if pred_df is not None else 0} 行") print(f" 实际数据: {len(actual_df) if actual_df is not None else 0} 行") # 确保数据不为空 if pred_df is None or len(pred_df) == 0: print("⚠️ 警告: 预测数据为空!") # 创建空图表 fig = go.Figure() fig.update_layout(title='No prediction data available') return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder) # Use specified historical data start position, not always from the beginning of df if historical_start_idx + lookback + pred_len <= len(df): # Display lookback historical points + pred_len prediction points starting from specified position historical_df = df.iloc[historical_start_idx:historical_start_idx + lookback] prediction_range = range(historical_start_idx + lookback, historical_start_idx + lookback + pred_len) else: # If data is insufficient, adjust to maximum available range available_lookback = min(lookback, len(df) - historical_start_idx) available_pred_len = min(pred_len, max(0, len(df) - historical_start_idx - available_lookback)) historical_df = df.iloc[historical_start_idx:historical_start_idx + available_lookback] prediction_range = range(historical_start_idx + available_lookback, historical_start_idx + available_lookback + available_pred_len) # Create chart fig = go.Figure() # Add historical data (candlestick chart) fig.add_trace(go.Candlestick( x=historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), open=historical_df['open'].tolist(), high=historical_df['high'].tolist(), low=historical_df['low'].tolist(), close=historical_df['close'].tolist(), name='Historical Data (400 data points)', increasing_line_color='#26A69A', decreasing_line_color='#EF5350' )) # Add prediction data (candlestick chart) if pred_df is not None and len(pred_df) > 0: # Calculate prediction data timestamps - ensure continuity with historical data if 'timestamps' in df.columns and len(historical_df) > 0: # Start from the last timestamp of historical data, create prediction timestamps with the same time interval last_timestamp = historical_df['timestamps'].iloc[-1] time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1) pred_timestamps = pd.date_range( start=last_timestamp + time_diff, periods=len(pred_df), freq=time_diff ) else: # If no timestamps, use index pred_timestamps = range(len(historical_df), len(historical_df) + len(pred_df)) fig.add_trace(go.Candlestick( x=pred_timestamps.tolist() if hasattr(pred_timestamps, 'tolist') else list(pred_timestamps), open=pred_df['open'].tolist(), high=pred_df['high'].tolist(), low=pred_df['low'].tolist(), close=pred_df['close'].tolist(), name='Prediction Data (120 data points)', increasing_line_color='#66BB6A', decreasing_line_color='#FF7043' )) # Add actual data for comparison (if exists) if actual_df is not None and len(actual_df) > 0: # Actual data should be in the same time period as prediction data if 'timestamps' in df.columns: # Actual data should use the same timestamps as prediction data to ensure time alignment if 'pred_timestamps' in locals(): actual_timestamps = pred_timestamps else: # If no prediction timestamps, calculate from the last timestamp of historical data if len(historical_df) > 0: last_timestamp = historical_df['timestamps'].iloc[-1] time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta( hours=1) actual_timestamps = pd.date_range( start=last_timestamp + time_diff, periods=len(actual_df), freq=time_diff ) else: actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df)) else: actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df)) fig.add_trace(go.Candlestick( x=actual_timestamps.tolist() if hasattr(actual_timestamps, 'tolist') else list(actual_timestamps), open=actual_df['open'].tolist(), high=actual_df['high'].tolist(), low=actual_df['low'].tolist(), close=actual_df['close'].tolist(), name='Actual Data (120 data points)', increasing_line_color='#FF9800', decreasing_line_color='#F44336' )) # Update layout fig.update_layout( title='Kronos Financial Prediction Results - 400 Historical Points + 120 Prediction Points vs 120 Actual Points', xaxis_title='Time', yaxis_title='Price', template='plotly_white', height=600, showlegend=True ) # Ensure x-axis time continuity if 'timestamps' in historical_df.columns: # Get all timestamps and sort them all_timestamps = [] if len(historical_df) > 0: all_timestamps.extend(historical_df['timestamps'].tolist()) if 'pred_timestamps' in locals(): all_timestamps.extend( pred_timestamps.tolist() if hasattr(pred_timestamps, 'tolist') else list(pred_timestamps)) if 'actual_timestamps' in locals(): all_timestamps.extend( actual_timestamps.tolist() if hasattr(actual_timestamps, 'tolist') else list(actual_timestamps)) if all_timestamps: all_timestamps = sorted(all_timestamps) fig.update_xaxes( range=[all_timestamps[0], all_timestamps[-1]], rangeslider_visible=False, type='date' ) # return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder) try: chart_json = fig.to_json() print(f"✅ 图表数据序列化完成，长度: {len(chart_json)}") return chart_json except Exception as e: print(f"❌ 图表序列化失败: {e}") error_fig = go.Figure() error_fig.update_layout(title='Chart Rendering Error') return error_fig.to_json() # 计算指标 def calculate_indicators(df): indicators = {} # 计算移动平均线 (MA) indicators['ma5'] = df['close'].rolling(window=5).mean() indicators['ma10'] = df['close'].rolling(window=10).mean() indicators['ma20'] = df['close'].rolling(window=20).mean() # 计算MACD exp12 = df['close'].ewm(span=12, adjust=False).mean() exp26 = df['close'].ewm(span=26, adjust=False).mean() indicators['macd'] = exp12 - exp26 indicators['signal'] = indicators['macd'].ewm(span=9, adjust=False).mean() indicators['macd_hist'] = indicators['macd'] - indicators['signal'] # 计算RSI delta = df['close'].diff() gain = (delta.where(delta > 0, 0)).rolling(window=14).mean() loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean() rs = gain / loss indicators['rsi'] = 100 - (100 / (1 + rs)) # 计算布林带 indicators['bb_mid'] = df['close'].rolling(window=20).mean() indicators['bb_std'] = df['close'].rolling(window=20).std() indicators['bb_upper'] = indicators['bb_mid'] + 2 * indicators['bb_std'] indicators['bb_lower'] = indicators['bb_mid'] - 2 * indicators['bb_std'] # 计算随机震荡指标 low_min = df['low'].rolling(window=14).min() high_max = df['high'].rolling(window=14).max() indicators['stoch_k'] = 100 * ((df['close'] - low_min) / (high_max - low_min)) indicators['stoch_d'] = indicators['stoch_k'].rolling(window=3).mean() # 滚动窗口均值策略 indicators['rwms_window'] = 90 indicators['rwms_mean'] = df['close'].rolling(window=90).mean() indicators['rwms_signal'] = (df['close'] > indicators['rwms_mean']).astype(int) # 三重指数平均（TRIX）策略 # 计算收盘价的EMA ema1 = df['close'].ewm(span=12, adjust=False).mean() # 计算EMA的EMA ema2 = ema1.ewm(span=12, adjust=False).mean() # 计算EMA的EMA的EMA ema3 = ema2.ewm(span=12, adjust=False).mean() # 计算TRIX indicators['trix'] = (ema3 - ema3.shift(1)) / ema3.shift(1) * 100 # 计算信号线 indicators['trix_signal'] = indicators['trix'].ewm(span=9, adjust=False).mean() return indicators # 创建图表 def create_technical_chart(df, pred_df, lookback, pred_len, diagram_type, actual_df=None, historical_start_idx=0): print(f" 🔍 数据内容: {len(df) if df is not None else 0} 行") print(f" 🔍 图表类型: {diagram_type}") # 数据范围 if historical_start_idx + lookback <= len(df): historical_df = df.iloc[historical_start_idx:historical_start_idx + lookback] else: available_lookback = min(lookback, len(df) - historical_start_idx) historical_df = df.iloc[historical_start_idx:historical_start_idx + available_lookback] # 计算指标 historical_indicators = calculate_indicators(historical_df) fig = go.Figure() # 成交量图表 if diagram_type == 'Volume Chart (VOL)': fig.add_trace(go.Bar( x = historical_df['timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_df['volume'].tolist() if 'volume' in historical_df.columns else [], name = 'Historical Volume', marker_color='#42A5F5' )) if actual_df is not None and len(actual_df) > 0 and 'volume' in actual_df.columns: if 'timestamps' in df.columns and len(historical_df) > 0: last_timestamp = historical_df['timestamps'].iloc[-1] time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta( hours=1) actual_timestamps = pd.date_range(start=last_timestamp + time_diff, periods=len(actual_df),freq=time_diff) else: actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df)) fig.add_trace(go.Bar( x = actual_timestamps.tolist() if hasattr(actual_timestamps, 'tolist') else list(actual_timestamps), y = actual_df['volume'].tolist(), name = 'Actual Volume', marker_color='#FF9800' )) fig.update_layout(yaxis_title='Volume') # 移动平均线 elif diagram_type == 'Moving Average (MA)': fig.add_trace(go.Scatter( x = historical_df['timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['ma5'], name='MA5', line=dict(color='#26A69A', width=1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['ma10'], name = 'MA10', line = dict(color = '#42A5F5', width = 1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['ma20'], name = 'MA20', line = dict(color = '#7E57C2', width = 1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_df['close'], name = 'Close Price', line = dict(color = '#212121', width = 1, dash = 'dash') )) fig.update_layout(yaxis_title = 'Price') # MACD指标 elif diagram_type == 'MACD Indicator (MACD)': fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['macd'], name = 'MACD', line = dict(color = '#26A69A', width = 1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['signal'], name = 'Signal', line = dict(color = '#EF5350', width = 1) )) fig.add_trace(go.Bar( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['macd_hist'], name = 'MACD Histogram', marker_color = '#42A5F5' )) # 零轴线 fig.add_hline(y = 0, line_dash = "dash", line_color = "gray") fig.update_layout(yaxis_title = 'MACD') # RSI指标 elif diagram_type == 'RSI Indicator (RSI)': fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['rsi'], name = 'RSI', line = dict(color = '#26A69A', width = 1) )) # 超买超卖线 fig.add_hline(y = 70, line_dash = "dash", line_color = "red", name = 'Overbought') fig.add_hline(y = 30, line_dash = "dash", line_color = "green", name = 'Oversold') fig.update_layout(yaxis_title = 'RSI', yaxis_range = [0, 100]) # 布林带 elif diagram_type == 'Bollinger Bands (BB)': fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['bb_upper'], name = 'Upper Band', line = dict(color = '#EF5350', width = 1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['bb_mid'], name = 'Middle Band (MA20)', line = dict(color = '#42A5F5', width = 1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['bb_lower'], name = 'Lower Band', line = dict(color = '#26A69A', width = 1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_df['close'], name = 'Close Price', line = dict(color = '#212121', width = 1) )) fig.update_layout(yaxis_title = 'Price') # 随机震荡指标 elif diagram_type == 'Stochastic Oscillator (STOCH)': fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['stoch_k'], name = '%K', line = dict(color = '#26A69A', width = 1) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['stoch_d'], name = '%D', line = dict(color = '#EF5350', width = 1) )) fig.add_hline(y = 80, line_dash = "dash", line_color = "red", name = 'Overbought') fig.add_hline(y = 20, line_dash = "dash", line_color = "green", name = 'Oversold') fig.update_layout(yaxis_title = 'Stochastic', yaxis_range = [0, 100]) # 滚动窗口均值策略 elif diagram_type == 'Rolling Window Mean Strategy': fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_df['close'], name = 'Close Price', line = dict(color = '#212121', width = 1.5) )) fig.add_trace(go.Scatter( x = historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y = historical_indicators['rwms_mean'], name = f'Rolling Mean ({historical_indicators["rwms_window"]} periods)', line = dict(color = '#42A5F5', width = 1.5, dash = 'dash') )) buy_signals = historical_df[historical_indicators['rwms_signal'] == 1] fig.add_trace(go.Scatter( x = buy_signals['timestamps'].tolist() if 'timestamps' in buy_signals.columns else buy_signals.index.tolist(), y = buy_signals['close'], mode = 'markers', name = 'Buy Signal', marker = dict(color = '#26A69A', size = 8, symbol = 'triangle-up') )) sell_signals = historical_df[historical_indicators['rwms_signal'] == 0] fig.add_trace(go.Scatter( x = sell_signals[ 'timestamps'].tolist() if 'timestamps' in sell_signals.columns else sell_signals.index.tolist(), y = sell_signals['close'], mode = 'markers', name = 'Sell Signal', marker = dict(color = '#EF5350', size = 8, symbol = 'triangle-down') )) fig.update_layout( yaxis_title = 'Price', title = f'Rolling Window Mean Strategy (Window Size: {historical_indicators["rwms_window"]})' ) # TRIX指标图表 elif diagram_type == 'TRIX Indicator (TRIX)': fig.add_trace(go.Scatter( x=historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y=historical_indicators['trix'], name='TRIX', line=dict(color='#26A69A', width=1) )) fig.add_trace(go.Scatter( x=historical_df[ 'timestamps'].tolist() if 'timestamps' in historical_df.columns else historical_df.index.tolist(), y=historical_indicators['trix_signal'], name='TRIX Signal', line=dict(color='#EF5350', width=1) )) fig.add_hline(y=0, line_dash="dash", line_color="gray") fig.update_layout( yaxis_title='TRIX (%)', title='Triple Exponential Average (TRIX) Strategy' ) # 布局设置 fig.update_layout( title = f'{diagram_type} - Technical Indicator (Real Data Only)', xaxis_title = 'Time', template = 'plotly_white', height = 400, showlegend = True, margin = dict(t = 50, b = 30) ) if 'timestamps' in historical_df.columns: all_timestamps = historical_df['timestamps'].tolist() if actual_df is not None and len(actual_df) > 0 and 'timestamps' in df.columns: if 'actual_timestamps' in locals(): all_timestamps.extend(actual_timestamps.tolist()) if all_timestamps: all_timestamps = sorted(all_timestamps) fig.update_xaxes( range=[all_timestamps[0], all_timestamps[-1]], rangeslider_visible=False, type='date' ) try: chart_json = fig.to_json() print(f"✅ 技术指标图表序列化完成，长度: {len(chart_json)}") return chart_json except Exception as e: print(f"❌ 技术指标图表序列化失败: {e}") error_fig = go.Figure() error_fig.update_layout(title='Chart Rendering Error') return error_fig.to_json() @app.route('/') def index(): """Home page""" return render_template('index.html') @app.route('/api/data-files') def get_data_files(): """Get available data file list""" data_files = load_data_files() return jsonify(data_files) @app.route('/api/load-data', methods=['POST']) def load_data(): """Load data file""" try: data = request.get_json() file_path = data.get('file_path') if not file_path: return jsonify({'error': 'File path cannot be empty'}), 400 df, error = load_data_file(file_path) if error: return jsonify({'error': error}), 400 # Detect data time frequency def detect_timeframe(df): if len(df) < 2: return "Unknown" time_diffs = [] for i in range(1, min(10, len(df))): # Check first 10 time differences diff = df['timestamps'].iloc[i] - df['timestamps'].iloc[i-1] time_diffs.append(diff) if not time_diffs: return "Unknown" # Calculate average time difference avg_diff = sum(time_diffs, pd.Timedelta(0)) / len(time_diffs) # Convert to readable format if avg_diff < pd.Timedelta(minutes=1): return f"{avg_diff.total_seconds():.0f} seconds" elif avg_diff < pd.Timedelta(hours=1): return f"{avg_diff.total_seconds() / 60:.0f} minutes" elif avg_diff < pd.Timedelta(days=1): return f"{avg_diff.total_seconds() / 3600:.0f} hours" else: return f"{avg_diff.days} days" # Return data information data_info = { 'rows': len(df), 'columns': list(df.columns), 'start_date': df['timestamps'].min().isoformat() if 'timestamps' in df.columns else 'N/A', 'end_date': df['timestamps'].max().isoformat() if 'timestamps' in df.columns else 'N/A', 'price_range': { 'min': float(df[['open', 'high', 'low', 'close']].min().min()), 'max': float(df[['open', 'high', 'low', 'close']].max().max()) }, 'prediction_columns': ['open', 'high', 'low', 'close'] + (['volume'] if 'volume' in df.columns else []), 'timeframe': detect_timeframe(df) } return jsonify({ 'success': True, 'data_info': data_info, 'message': f'Successfully loaded data, total {len(df)} rows' }) except Exception as e: return jsonify({'error': f'Failed to load data: {str(e)}'}), 500 # @app.route('/api/predict', methods=['POST']) # def predict(): # """Perform prediction""" # try: # data = request.get_json() # file_path = data.get('file_path') # lookback = int(data.get('lookback', 400)) # pred_len = int(data.get('pred_len', 120)) # # # Get prediction quality parameters # temperature = float(data.get('temperature', 1.0)) # top_p = float(data.get('top_p', 0.9)) # sample_count = int(data.get('sample_count', 1)) # # if not file_path: # return jsonify({'error': 'File path cannot be empty'}), 400 # # # Load data # df, error = load_data_file(file_path) # if error: # return jsonify({'error': error}), 400 # # if len(df) < lookback: # return jsonify({'error': f'Insufficient data length, need at least {lookback} rows'}), 400 # # # Perform prediction # if MODEL_AVAILABLE and predictor is not None: # try: # # Use real Kronos model # # Only use necessary columns: OHLCV, excluding amount # required_cols = ['open', 'high', 'low', 'close'] # if 'volume' in df.columns: # required_cols.append('volume') # # # Process time period selection # start_date = data.get('start_date') # # if start_date: # # Custom time period - fix logic: use data within selected window # start_dt = pd.to_datetime(start_date) # # # Find data after start time # mask = df['timestamps'] >= start_dt # time_range_df = df[mask] # # # Ensure sufficient data: lookback + pred_len # if len(time_range_df) < lookback + pred_len: # return jsonify({'error': f'Insufficient data from start time {start_dt.strftime("%Y-%m-%d %H:%M")}, need at least {lookback + pred_len} data points, currently only {len(time_range_df)} available'}), 400 # # # Use first lookback data points within selected window for prediction # x_df = time_range_df.iloc[:lookback][required_cols] # x_timestamp = time_range_df.iloc[:lookback]['timestamps'] # # # Use last pred_len data points within selected window as actual values # y_timestamp = time_range_df.iloc[lookback:lookback+pred_len]['timestamps'] # # # Calculate actual time period length # start_timestamp = time_range_df['timestamps'].iloc[0] # end_timestamp = time_range_df['timestamps'].iloc[lookback+pred_len-1] # time_span = end_timestamp - start_timestamp # # prediction_type = f"Kronos model prediction (within selected window: first {lookback} data points for prediction, last {pred_len} data points for comparison, time span: {time_span})" # else: # # Use latest data # x_df = df.iloc[:lookback][required_cols] # x_timestamp = df.iloc[:lookback]['timestamps'] # y_timestamp = df.iloc[lookback:lookback+pred_len]['timestamps'] # prediction_type = "Kronos model prediction (latest data)" # # # Ensure timestamps are Series format, not DatetimeIndex, to avoid .dt attribute error in Kronos model # if isinstance(x_timestamp, pd.DatetimeIndex): # x_timestamp = pd.Series(x_timestamp, name='timestamps') # if isinstance(y_timestamp, pd.DatetimeIndex): # y_timestamp = pd.Series(y_timestamp, name='timestamps') # # # # 在 pred_df = predictor.predict(...) 之前添加： # # print("🔍 调试预测输入:") # # print(f"x_df 类型: {type(x_df)}") # # print(f"x_df 形状: {x_df.shape}") # # print(f"x_df 列名: {x_df.columns.tolist()}") # # print(f"x_df 数据类型: {x_df.dtypes}") # # # # print(f"x_timestamp 类型: {type(x_timestamp)}") # # print(f"x_timestamp 长度: {len(x_timestamp)}") # # # # print(f"y_timestamp 类型: {type(y_timestamp)}") # # print(f"y_timestamp 长度: {len(y_timestamp)}") # # # # # 检查数据内容 # # print("x_df 前5行:") # # print(x_df.head()) # # # # # 在调用 predict 前确保数据格式正确 # # print(f"x_df 实际形状: {x_df.shape}") # 确认是 (400, 5) # # print(f"x_df 数值类型: {x_df.values.dtype}") # # # # # 确保没有隐藏的索引列 # # x_df_clean = x_df.reset_index(drop=True) # # print(f"重置索引后形状: {x_df_clean.shape}") # # # # # 在调用 predict 之前添加更详细的调试 # # print("🔍 深入调试 KronosPredictor:") # # # # # 检查 predictor 的属性 # # print(f"predictor 类型: {type(predictor)}") # # print(f"predictor 设备: {getattr(predictor, 'device', 'unknown')}") # # print(f"predictor max_context: {getattr(predictor, 'max_context', 'unknown')}") # # # # # 检查模型输入维度 # # if hasattr(predictor, 'model'): # # model = predictor.model # # print(f"模型参数示例:") # # for name, param in model.named_parameters(): # # if 'weight' in name and param.dim() == 2: # # print(f" {name}: {param.shape}") # # break # # # # # 尝试手动准备数据 # # try: # # # 将数据转换为 tensor 看看维度 # # import torch # # x_tensor = torch.tensor(x_df.values, dtype=torch.float32) # # print(f"Tensor 形状: {x_tensor.shape}") # # # # # 检查 tokenizer 的输入维度 # # if hasattr(predictor, 'tokenizer'): # # tokenizer = predictor.tokenizer # # print(f"tokenizer 输入维度: {getattr(tokenizer, 'd_in', 'unknown')}") # # # # except Exception as e: # # print(f"Tensor 转换错误: {e}") # # # # # 在 predict 调用前测试 tokenizer # # try: # # # 测试 tokenizer 是否能正确处理数据 # # test_data = x_df.values # (400, 5) # # print(f"测试数据形状: {test_data.shape}") # # # # # 尝试手动调用 tokenizer # # if hasattr(predictor.tokenizer, 'encode'): # # encoded = predictor.tokenizer.encode(test_data) # # print(f"Tokenized 数据形状: {encoded.shape}") # # else: # # print("Tokenizer 没有 encode 方法") # # # # except Exception as e: # # print(f"Tokenizer 测试错误: {e}") # # pred_df = predictor.predict( # df=x_df, # x_timestamp=x_timestamp, # y_timestamp=y_timestamp, # pred_len=pred_len, # T=temperature, # top_p=top_p, # sample_count=sample_count # ) # # except Exception as e: # return jsonify({'error': f'Kronos model prediction failed: {str(e)}'}), 500 # else: # return jsonify({'error': 'Kronos model not loaded, please load model first'}), 400 # # # Prepare actual data for comparison (if exists) # actual_data = [] # actual_df = None # # if start_date: # Custom time period # # Fix logic: use data within selected window # # Prediction uses first 400 data points within selected window # # Actual data should be last 120 data points within selected window # start_dt = pd.to_datetime(start_date) # # # Find data starting from start_date # mask = df['timestamps'] >= start_dt # time_range_df = df[mask] # # if len(time_range_df) >= lookback + pred_len: # # Get last 120 data points within selected window as actual values # actual_df = time_range_df.iloc[lookback:lookback+pred_len] # # for i, (_, row) in enumerate(actual_df.iterrows()): # actual_data.append({ # 'timestamp': row['timestamps'].isoformat(), # 'open': float(row['open']), # 'high': float(row['high']), # 'low': float(row['low']), # 'close': float(row['close']), # 'volume': float(row['volume']) if 'volume' in row else 0, # 'amount': float(row['amount']) if 'amount' in row else 0 # }) # else: # Latest data # # Prediction uses first 400 data points # # Actual data should be 120 data points after first 400 data points # if len(df) >= lookback + pred_len: # actual_df = df.iloc[lookback:lookback+pred_len] # for i, (_, row) in enumerate(actual_df.iterrows()): # actual_data.append({ # 'timestamp': row['timestamps'].isoformat(), # 'open': float(row['open']), # 'high': float(row['high']), # 'low': float(row['low']), # 'close': float(row['close']), # 'volume': float(row['volume']) if 'volume' in row else 0, # 'amount': float(row['amount']) if 'amount' in row else 0 # }) # # # Create chart - pass historical data start position # if start_date: # # Custom time period: find starting position of historical data in original df # start_dt = pd.to_datetime(start_date) # mask = df['timestamps'] >= start_dt # historical_start_idx = df[mask].index[0] if len(df[mask]) > 0 else 0 # else: # # Latest data: start from beginning # historical_start_idx = 0 # # chart_json = create_prediction_chart(df, pred_df, lookback, pred_len, actual_df, historical_start_idx) # # # Prepare prediction result data - fix timestamp calculation logic # if 'timestamps' in df.columns: # if start_date: # # Custom time period: use selected window data to calculate timestamps # start_dt = pd.to_datetime(start_date) # mask = df['timestamps'] >= start_dt # time_range_df = df[mask] # # if len(time_range_df) >= lookback: # # Calculate prediction timestamps starting from last time point of selected window # last_timestamp = time_range_df['timestamps'].iloc[lookback-1] # time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] # future_timestamps = pd.date_range( # start=last_timestamp + time_diff, # periods=pred_len, # freq=time_diff # ) # else: # future_timestamps = [] # else: # # Latest data: calculate from last time point of entire data file # last_timestamp = df['timestamps'].iloc[-1] # time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] # future_timestamps = pd.date_range( # start=last_timestamp + time_diff, # periods=pred_len, # freq=time_diff # ) # else: # future_timestamps = range(len(df), len(df) + pred_len) # # prediction_results = [] # for i, (_, row) in enumerate(pred_df.iterrows()): # prediction_results.append({ # 'timestamp': future_timestamps[i].isoformat() if i < len(future_timestamps) else f"T{i}", # 'open': float(row['open']), # 'high': float(row['high']), # 'low': float(row['low']), # 'close': float(row['close']), # 'volume': float(row['volume']) if 'volume' in row else 0, # 'amount': float(row['amount']) if 'amount' in row else 0 # }) # # # Save prediction results to file # try: # save_prediction_results( # file_path=file_path, # prediction_type=prediction_type, # prediction_results=prediction_results, # actual_data=actual_data, # input_data=x_df, # prediction_params={ # 'lookback': lookback, # 'pred_len': pred_len, # 'temperature': temperature, # 'top_p': top_p, # 'sample_count': sample_count, # 'start_date': start_date if start_date else 'latest' # } # ) # except Exception as e: # print(f"Failed to save prediction results: {e}") # # return jsonify({ # 'success': True, # 'prediction_type': prediction_type, # 'chart': chart_json, # 'prediction_results': prediction_results, # 'actual_data': actual_data, # 'has_comparison': len(actual_data) > 0, # 'message': f'Prediction completed, generated {pred_len} prediction points' + (f', including {len(actual_data)} actual data points for comparison' if len(actual_data) > 0 else '') # }) # # except Exception as e: # return jsonify({'error': f'Prediction failed: {str(e)}'}), 500 @app.route('/api/predict', methods=['POST']) def predict(): """Perform prediction""" try: data = request.get_json() file_path = data.get('file_path') lookback = int(data.get('lookback', 400)) pred_len = int(data.get('pred_len', 120)) # Get prediction quality parameters temperature = float(data.get('temperature', 1.0)) top_p = float(data.get('top_p', 0.9)) sample_count = int(data.get('sample_count', 1)) if not file_path: return jsonify({'error': 'File path cannot be empty'}), 400 # Load data df, error = load_data_file(file_path) if error: return jsonify({'error': error}), 400 if len(df) < lookback: return jsonify({'error': f'Insufficient data length, need at least {lookback} rows'}), 400 # Perform prediction if MODEL_AVAILABLE and predictor is not None: try: # Use real Kronos model # Only use necessary columns: OHLCV + amount required_cols = ['open', 'high', 'low', 'close', 'volume', 'amount'] # Process time period selection start_date = data.get('start_date') if start_date: # Custom time period - fix logic: use data within selected window start_dt = pd.to_datetime(start_date) # Find data after start time mask = df['timestamps'] >= start_dt time_range_df = df[mask] # Ensure sufficient data: lookback + pred_len if len(time_range_df) < lookback + pred_len: return jsonify({ 'error': f'Insufficient data from start time {start_dt.strftime("%Y-%m-%d %H:%M")}, need at least {lookback + pred_len} data points, currently only {len(time_range_df)} available'}), 400 # Use first lookback data points within selected window for prediction x_df = time_range_df.iloc[:lookback][required_cols] x_timestamp = time_range_df.iloc[:lookback]['timestamps'] # Use last pred_len data points within selected window as actual values y_timestamp = time_range_df.iloc[lookback:lookback + pred_len]['timestamps'] # Calculate actual time period length start_timestamp = time_range_df['timestamps'].iloc[0] end_timestamp = time_range_df['timestamps'].iloc[lookback + pred_len - 1] time_span = end_timestamp - start_timestamp prediction_type = f"Kronos model prediction (within selected window: first {lookback} data points for prediction, last {pred_len} data points for comparison, time span: {time_span})" else: # Use latest data x_df = df.iloc[:lookback][required_cols] x_timestamp = df.iloc[:lookback]['timestamps'] y_timestamp = df.iloc[lookback:lookback + pred_len]['timestamps'] prediction_type = "Kronos model prediction (latest data)" # Debug information print(f"🔍 传递给predictor的数据列: {x_df.columns.tolist()}") print(f"🔍 数据形状: {x_df.shape}") print(f"🔍 数据样例:") print(x_df.head(2)) # Ensure timestamps are Series format, not DatetimeIndex, to avoid .dt attribute error in Kronos model if isinstance(x_timestamp, pd.DatetimeIndex): x_timestamp = pd.Series(x_timestamp, name='timestamps') if isinstance(y_timestamp, pd.DatetimeIndex): y_timestamp = pd.Series(y_timestamp, name='timestamps') pred_df = predictor.predict( df=x_df, x_timestamp=x_timestamp, y_timestamp=y_timestamp, pred_len=pred_len, T=temperature, top_p=top_p, sample_count=sample_count ) except Exception as e: return jsonify({'error': f'Kronos model prediction failed: {str(e)}'}), 500 else: return jsonify({'error': 'Kronos model not loaded, please load model first'}), 400 # Prepare actual data for comparison (if exists) actual_data = [] actual_df = None if start_date: # Custom time period # Fix logic: use data within selected window # Prediction uses first 400 data points within selected window # Actual data should be last 120 data points within selected window start_dt = pd.to_datetime(start_date) # Find data starting from start_date mask = df['timestamps'] >= start_dt time_range_df = df[mask] if len(time_range_df) >= lookback + pred_len: # Get last 120 data points within selected window as actual values actual_df = time_range_df.iloc[lookback:lookback + pred_len] for i, (_, row) in enumerate(actual_df.iterrows()): actual_data.append({ 'timestamp': row['timestamps'].isoformat(), 'open': float(row['open']), 'high': float(row['high']), 'low': float(row['low']), 'close': float(row['close']), 'volume': float(row['volume']) if 'volume' in row else 0, 'amount': float(row['amount']) if 'amount' in row else 0 }) else: # Latest data # Prediction uses first 400 data points # Actual data should be 120 data points after first 400 data points if len(df) >= lookback + pred_len: actual_df = df.iloc[lookback:lookback + pred_len] for i, (_, row) in enumerate(actual_df.iterrows()): actual_data.append({ 'timestamp': row['timestamps'].isoformat(), 'open': float(row['open']), 'high': float(row['high']), 'low': float(row['low']), 'close': float(row['close']), 'volume': float(row['volume']) if 'volume' in row else 0, 'amount': float(row['amount']) if 'amount' in row else 0 }) # Create chart - pass historical data start position if start_date: # Custom time period: find starting position of historical data in original df start_dt = pd.to_datetime(start_date) mask = df['timestamps'] >= start_dt historical_start_idx = df[mask].index[0] if len(df[mask]) > 0 else 0 else: # Latest data: start from beginning historical_start_idx = 0 chart_json = create_prediction_chart(df, pred_df, lookback, pred_len, actual_df, historical_start_idx) # Prepare prediction result data - fix timestamp calculation logic if 'timestamps' in df.columns: if start_date: # Custom time period: use selected window data to calculate timestamps start_dt = pd.to_datetime(start_date) mask = df['timestamps'] >= start_dt time_range_df = df[mask] if len(time_range_df) >= lookback: # Calculate prediction timestamps starting from last time point of selected window last_timestamp = time_range_df['timestamps'].iloc[lookback - 1] time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] future_timestamps = pd.date_range( start=last_timestamp + time_diff, periods=pred_len, freq=time_diff ) else: future_timestamps = [] else: # Latest data: calculate from last time point of entire data file last_timestamp = df['timestamps'].iloc[-1] time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] future_timestamps = pd.date_range( start=last_timestamp + time_diff, periods=pred_len, freq=time_diff ) else: future_timestamps = range(len(df), len(df) + pred_len) prediction_results = [] for i, (_, row) in enumerate(pred_df.iterrows()): prediction_results.append({ 'timestamp': future_timestamps[i].isoformat() if i < len(future_timestamps) else f"T{i}", 'open': float(row['open']), 'high': float(row['high']), 'low': float(row['low']), 'close': float(row['close']), 'volume': float(row['volume']) if 'volume' in row else 0, 'amount': float(row['amount']) if 'amount' in row else 0 }) # Save prediction results to file try: save_prediction_results( file_path=file_path, prediction_type=prediction_type, prediction_results=prediction_results, actual_data=actual_data, input_data=x_df, prediction_params={ 'lookback': lookback, 'pred_len': pred_len, 'temperature': temperature, 'top_p': top_p, 'sample_count': sample_count, 'start_date': start_date if start_date else 'latest' } ) except Exception as e: print(f"Failed to save prediction results: {e}") # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # 在返回前添加 print(f"✅ 预测完成，返回数据:") print(f" 成功: {True}") print(f" 预测类型: {prediction_type}") print(f" 图表数据长度: {len(chart_json)}") print(f" 预测结果数量: {len(prediction_results)}") print(f" 实际数据数量: {len(actual_data)}") print(f" 有比较数据: {len(actual_data) > 0}") return jsonify({ 'success': True, 'prediction_type': prediction_type, 'chart': chart_json, 'prediction_results': prediction_results, 'actual_data': actual_data, 'has_comparison': len(actual_data) > 0, 'message': f'Prediction completed, generated {pred_len} prediction points' + ( f', including {len(actual_data)} actual data points for comparison' if len(actual_data) > 0 else '') }) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # return jsonify({ # 'success': True, # 'prediction_type': prediction_type, # 'chart': chart_json, # 'prediction_results': prediction_results, # 'actual_data': actual_data, # 'has_comparison': len(actual_data) > 0, # 'message': f'Prediction completed, generated {pred_len} prediction points' + ( # f', including {len(actual_data)} actual data points for comparison' if len(actual_data) > 0 else '') # }) except Exception as e: return jsonify({'error': f'Prediction failed: {str(e)}'}), 500 # @app.route('/api/load-model', methods=['POST']) # def load_model(): # """Load Kronos model""" # global tokenizer, model, predictor # # try: # if not MODEL_AVAILABLE: # return jsonify({'error': 'Kronos model library not available'}), 400 # # data = request.get_json() # model_key = data.get('model_key', 'kronos-small') # device = data.get('device', 'cpu') # # if model_key not in AVAILABLE_MODELS: # return jsonify({'error': f'Unsupported model: {model_key}'}), 400 # # model_config = AVAILABLE_MODELS[model_key] # # # Load tokenizer and model # tokenizer = KronosTokenizer.from_pretrained(model_config['tokenizer_id']) # model = Kronos.from_pretrained(model_config['model_id']) # # # Create predictor # predictor = KronosPredictor(model, tokenizer, device=device, max_context=model_config['context_length']) # # return jsonify({ # 'success': True, # 'message': f'Model loaded successfully: {model_config["name"]} ({model_config["params"]}) on {device}', # 'model_info': { # 'name': model_config['name'], # 'params': model_config['params'], # 'context_length': model_config['context_length'], # 'description': model_config['description'] # } # }) # # except Exception as e: # return jsonify({'error': f'Model loading failed: {str(e)}'}), 500 @app.route('/api/load-model', methods=['POST']) def load_model(): global tokenizer, model, predictor try: if not MODEL_AVAILABLE: return jsonify({'error': 'Kronos model library not available'}), 400 data = request.get_json() model_key = data.get('model_key', 'kronos-small') device = data.get('device', 'cpu') if model_key not in AVAILABLE_MODELS: return jsonify({'error': f'Unsupported model: {model_key}'}), 400 model_config = AVAILABLE_MODELS[model_key] print(f"Loading model from: {model_config['model_id']}") # 检查模型路径是否存在 if not os.path.exists(model_config['model_id']): return jsonify({'error': f'Model path does not exist: {model_config["model_id"]}'}), 400 try: # 直接从本地加载模型 model = Kronos.from_pretrained( model_config['model_id'], local_files_only=True ) # 读取模型配置文件获取正确参数 config_path = os.path.join(model_config['model_id'], 'config.json') if os.path.exists(config_path): with open(config_path, 'r') as f: config = json.load(f) print("使用模型配置参数:", config) # 使用配置中的参数创建tokenizer tokenizer = KronosTokenizer( d_in=6, # OHLC + volume d_model=config['d_model'], # 832 n_heads=config['n_heads'], # 16 ff_dim=config['ff_dim'], # 2048 n_enc_layers=config['n_layers'], # 12 n_dec_layers=config['n_layers'], # 12 ffn_dropout_p=config['ffn_dropout_p'], # 0.2 attn_dropout_p=config['attn_dropout_p'], # 0.0 resid_dropout_p=config['resid_dropout_p'], # 0.2 s1_bits=config['s1_bits'], # 10 s2_bits=config['s2_bits'], # 10 beta=1.0, gamma0=1.0, gamma=1.0, zeta=1.0, group_size=1 ) else: return jsonify({'error': f'Config file not found: {config_path}'}), 400 except Exception as e: return jsonify({'error': f'Failed to load model: {str(e)}'}), 500 # 创建predictor predictor = KronosPredictor( model, tokenizer, device=device, max_context=model_config['context_length'] ) return jsonify({ 'success': True, 'message': f'Model loaded successfully: {model_config["name"]} ({model_config["params"]}) on {device}', 'model_info': model_config }) except Exception as e: return jsonify({'error': f'Model loading failed: {str(e)}'}), 500 @app.route('/api/available-models') def get_available_models(): """Get available model list""" return jsonify({ 'models': AVAILABLE_MODELS, 'model_available': MODEL_AVAILABLE }) @app.route('/api/model-status') def get_model_status(): """Get model status""" if MODEL_AVAILABLE: if predictor is not None: return jsonify({ 'available': True, 'loaded': True, 'message': 'Kronos model loaded and available', 'current_model': { 'name': predictor.model.__class__.__name__, 'device': str(next(predictor.model.parameters()).device) } }) else: return jsonify({ 'available': True, 'loaded': False, 'message': 'Kronos model available but not loaded' }) else: return jsonify({ 'available': False, 'loaded': False, 'message': 'Kronos model library not available, please install related dependencies' }) @app.route('/api/stock-data', methods=['POST']) def Stock_Data(): try: data = request.get_json() stock_code = data.get('stock_code', '').strip() # 股票代码不能为空 if not stock_code: return jsonify({ 'success': False, 'error': f'Stock code cannot be empty' }), 400 # 股票代码格式验证 if not re.match(r'^[a-z]+\.\d+$', stock_code): return jsonify({ 'success': False, 'error': f'The stock code you entered is invalid' }), 400 # 登录 baostock lg = bs.login() if lg.error_code != '0': return jsonify({ 'success': False, 'error': f'Login failed: {lg.error_msg}' }), 400 rs = bs.query_history_k_data_plus( stock_code, "time,open,high,low,close,volume,amount", start_date = '2024-06-01', end_date = '2024-10-31', frequency = "5", adjustflag = "3" ) # 检查获取结果 if rs.error_code != '0': bs.logout() return jsonify({ 'success': False, 'error': f'Failed to retrieve data, please enter a valid stock code' }), 400 # 提取数据 data_list = [] while rs.next(): data_list.append(rs.get_row_data()) # 登出系统 bs.logout() columns = rs.fields df = pd.DataFrame(data_list, columns=columns) # 数值列转换 df = df.rename(columns={'time': 'timestamps'}) numeric_columns = ['timestamps','open', 'high', 'low', 'close', 'volume', 'amount'] for col in numeric_columns: df[col] = pd.to_numeric(df[col], errors='coerce') df['timestamps'] = pd.to_datetime(df['timestamps'].astype(str), format='%Y%m%d%H%M%S%f') # 去除无效数据 df = df.dropna() # 保存 data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data') os.makedirs(data_dir, exist_ok=True) filename = f"Stock_5min_A股.csv" file_path = os.path.join(data_dir, filename) df.to_csv( file_path, index = False, encoding = 'utf-8', mode = 'w' ) data_files = load_data_files() return jsonify({ 'success': True, 'message': f'Stock data saved successfully: {filename}', 'file_name': filename }) except Exception as e: return jsonify({ 'success': False, 'error': f'Error processing stock data: {str(e)}' }), 500 @app.route('/api/generate-chart', methods=['POST']) def generate_chart(): try: data = request.get_json() # 验证参数 required_fields = ['file_path', 'lookback', 'diagram_type', 'historical_start_idx'] for field in required_fields: if field not in data: return jsonify({'success': False, 'error': f'Missing required field: {field}'}), 400 # 解析参数 file_path = data['file_path'] lookback = int(data['lookback']) diagram_type = data['diagram_type'] historical_start_idx = int(data['historical_start_idx']) # 加载数据 df, error = load_data_file(file_path) if error: return jsonify({'success': False, 'error': error}), 400 if len(df) < lookback + historical_start_idx: return jsonify({ 'success': False, 'error': f'Insufficient data length, need at least {lookback + historical_start_idx} rows' }), 400 pred_df = None actual_df = None # 生成图表 chart_json = create_technical_chart( df=df, pred_df=pred_df, lookback=lookback, pred_len=0, diagram_type=diagram_type, actual_df=actual_df, historical_start_idx=historical_start_idx ) # 表格数据 table_data_start = historical_start_idx table_data_end = historical_start_idx + lookback table_df = df.iloc[table_data_start:table_data_end] table_data = table_df.to_dict('records') return jsonify({ 'success': True, 'chart': json.loads(chart_json), 'table_data': table_data, 'message': 'Technical chart generated successfully' }) except Exception as e: return jsonify({ 'success': False, 'error': f'Failed to generate technical chart: {str(e)}' }), 500 if __name__ == '__main__': print("Starting Kronos Web UI...") print(f"Model availability: {MODEL_AVAILABLE}") if MODEL_AVAILABLE: print("Tip: You can load Kronos model through /api/load-model endpoint") else: print("Tip: Will use simulated data for demonstration") app.run(debug=True, host='0.0.0.0', port=7070)