14863929a0
- Translate all Chinese comments and strings in webui/app.py - Translate all Chinese comments and strings in webui/run.py - Translate all Chinese comments and strings in webui/start.sh - Translate all Chinese content in webui/README.md - Translate all Chinese content in webui/templates/index.html - Add prediction results directory for analysis - Complete internationalization of webui module
709 lines
30 KiB
Python
709 lines
30 KiB
Python
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
|
|
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
|
|
|
|
# Available model configurations
|
|
AVAILABLE_MODELS = {
|
|
'kronos-mini': {
|
|
'name': 'Kronos-mini',
|
|
'model_id': 'NeoQuasar/Kronos-mini',
|
|
'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-2k',
|
|
'context_length': 2048,
|
|
'params': '4.1M',
|
|
'description': 'Lightweight model, suitable for fast prediction'
|
|
},
|
|
'kronos-small': {
|
|
'name': 'Kronos-small',
|
|
'model_id': 'NeoQuasar/Kronos-small',
|
|
'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
|
|
'context_length': 512,
|
|
'params': '24.7M',
|
|
'description': 'Small model, balanced performance and speed'
|
|
},
|
|
'kronos-base': {
|
|
'name': 'Kronos-base',
|
|
'model_id': 'NeoQuasar/Kronos-base',
|
|
'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
|
|
'context_length': 512,
|
|
'params': '102.3M',
|
|
'description': 'Base model, provides better prediction quality'
|
|
}
|
|
}
|
|
|
|
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"""
|
|
# 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'
|
|
)
|
|
|
|
return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
|
|
|
|
@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')
|
|
|
|
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/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/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'
|
|
})
|
|
|
|
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)
|