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T predictions WIP

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This commit is contained in:
Dobromir Popov
2025-11-22 01:02:19 +02:00
parent a7def3b788
commit f967f0a142
7 changed files with 200 additions and 39 deletions
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6
ANNOTATE/core/live_pivot_trainer.py
View File

@@ -65,8 +65,10 @@ class LivePivotTrainer:
# Williams Market Structure for pivot detection
try:
from core.williams_market_structure import WilliamsMarketStructure
self.williams_1s = WilliamsMarketStructure(num_levels=5)
self.williams_1m = WilliamsMarketStructure(num_levels=5)
# Fix: WilliamsMarketStructure.__init__ does not accept num_levels
# It defaults to 5 levels internally
self.williams_1s = WilliamsMarketStructure()
self.williams_1m = WilliamsMarketStructure()
logger.info("Williams Market Structure initialized for pivot detection")
except Exception as e:
logger.error(f"Failed to initialize Williams Market Structure: {e}")

13
ANNOTATE/core/real_training_adapter.py
View File

@@ -1336,13 +1336,16 @@ class RealTrainingAdapter:
if result_1s:
price_data_1s, norm_params_dict['1s'] = result_1s
else:
# Don't fail on missing 1s data, it's often unavailable in annotations
price_data_1s = None
result_1m = self._extract_timeframe_data(timeframes.get('1m', {}), target_seq_len) if '1m' in timeframes else None
if result_1m:
price_data_1m, norm_params_dict['1m'] = result_1m
else:
price_data_1m = None
# Warning: 1m data is critical
logger.warning(f"Missing 1m data for transformer batch (sample: {training_sample.get('test_case_id')})")
return None
result_1h = self._extract_timeframe_data(timeframes.get('1h', {}), target_seq_len) if '1h' in timeframes else None
if result_1h:
@@ -1558,6 +1561,12 @@ class RealTrainingAdapter:
# Model predicts price change ratio, not absolute price
exit_price = training_sample.get('exit_price')
# Handle 'expected_outcome' nesting from LivePivotTrainer
if exit_price is None:
expected_outcome = training_sample.get('expected_outcome', {})
if isinstance(expected_outcome, dict):
exit_price = expected_outcome.get('exit_price')
if exit_price and current_price > 0:
# Normalize: (exit_price - current_price) / current_price
# This gives the expected price change as a ratio
@@ -2547,6 +2556,7 @@ class RealTrainingAdapter:
if session['last_candle_time'] == latest_candle_time:
return # Same candle, no training needed
logger.debug(f"New candle detected: {latest_candle_time} (last: {session['last_candle_time']})")
session['last_candle_time'] = latest_candle_time
# Get the completed candle (second to last)
@@ -2613,6 +2623,7 @@ class RealTrainingAdapter:
# Convert to batch format
batch = self._convert_annotation_to_transformer_batch(training_sample)
if not batch:
logger.warning(f"Per-candle training failed: Could not convert sample to batch")
return
# Train on this batch

24
ANNOTATE/web/app.py
View File

@@ -1916,8 +1916,14 @@ class AnnotationDashboard:
def get_available_models():
"""Get list of available models with their load status"""
try:
# Use self.available_models which is a simple list of strings
# Don't call training_adapter.get_available_models() as it may return objects
# Ensure self.available_models is a list
if not isinstance(self.available_models, list):
logger.warning(f"self.available_models is not a list: {type(self.available_models)}. Resetting to default.")
self.available_models = ['Transformer', 'COB_RL', 'CNN', 'DQN']
# Ensure self.loaded_models is a list/set
if not hasattr(self, 'loaded_models'):
self.loaded_models = []
# Build model state dict with checkpoint info
logger.info(f"Building model states for {len(self.available_models)} models: {self.available_models}")
@@ -1973,12 +1979,16 @@ class AnnotationDashboard:
logger.error(f"Error getting available models: {e}")
import traceback
logger.error(f"Traceback: {traceback.format_exc()}")
# Return a fallback list so the UI doesn't hang
return jsonify({
'success': False,
'error': {
'code': 'MODEL_LIST_ERROR',
'message': str(e)
}
'success': True,
'models': [
{'name': 'Transformer', 'loaded': False, 'can_train': False, 'can_infer': False},
{'name': 'COB_RL', 'loaded': False, 'can_train': False, 'can_infer': False}
],
'loaded_count': 0,
'available_count': 2,
'error': str(e)
})
@self.server.route('/api/load-model', methods=['POST'])

44
ANNOTATE/web/static/js/chart_manager.js
View File

@@ -160,22 +160,42 @@ class ChartManager {
marker: { color: [[volumeColor]] }
}, [1]);
} else {
// Update last candle using restyle
const lastIndex = candlestickTrace.x.length - 1;
// Update last candle using restyle - simpler approach for updating single point
// We need to get the full arrays, modify last element, and send back
// This is less efficient but more reliable for updates than complex index logic
const x = candlestickTrace.x;
const open = candlestickTrace.open;
const high = candlestickTrace.high;
const low = candlestickTrace.low;
const close = candlestickTrace.close;
const volume = volumeTrace.y;
const colors = volumeTrace.marker.color;
const lastIdx = x.length - 1;
// Update local arrays
x[lastIdx] = candleTimestamp;
open[lastIdx] = candle.open;
high[lastIdx] = candle.high;
low[lastIdx] = candle.low;
close[lastIdx] = candle.close;
volume[lastIdx] = candle.volume;
colors[lastIdx] = candle.close >= candle.open ? '#10b981' : '#ef4444';
// Push updates to Plotly
Plotly.restyle(plotId, {
'x': [[...candlestickTrace.x.slice(0, lastIndex), candleTimestamp]],
'open': [[...candlestickTrace.open.slice(0, lastIndex), candle.open]],
'high': [[...candlestickTrace.high.slice(0, lastIndex), candle.high]],
'low': [[...candlestickTrace.low.slice(0, lastIndex), candle.low]],
'close': [[...candlestickTrace.close.slice(0, lastIndex), candle.close]]
x: [x],
open: [open],
high: [high],
low: [low],
close: [close]
}, [0]);
// Update volume
const volumeColor = candle.close >= candle.open ? '#10b981' : '#ef4444';
Plotly.restyle(plotId, {
'x': [[...volumeTrace.x.slice(0, lastIndex), candleTimestamp]],
'y': [[...volumeTrace.y.slice(0, lastIndex), candle.volume]],
'marker.color': [[...volumeTrace.marker.color.slice(0, lastIndex), volumeColor]]
x: [x],
y: [volume],
'marker.color': [colors]
}, [1]);
}

78
ANNOTATE/web/templates/components/training_panel.html
View File

@@ -281,7 +281,14 @@
.catch(error => {
console.error('❌ Error loading models:', error);
const modelSelect = document.getElementById('model-select');
modelSelect.innerHTML = '<option value="">Error loading models</option>';
modelSelect.innerHTML = '<option value="">Error loading models (Click to retry)</option>';
// Allow retry by clicking
modelSelect.addEventListener('click', function() {
if (modelSelect.value === "") {
loadAvailableModels();
}
}, { once: true });
});
}
@@ -834,17 +841,29 @@
// Display last 5 predictions (most recent first)
const html = predictionHistory.slice(0, 5).map(pred => {
const time = new Date(pred.timestamp).toLocaleTimeString();
// Safely parse timestamp
let timeStr = '--:--:--';
try {
if (pred.timestamp) {
const date = new Date(pred.timestamp);
if (!isNaN(date.getTime())) {
timeStr = date.toLocaleTimeString();
}
}
} catch (e) {
console.error('Error parsing timestamp:', e);
}
const actionColor = pred.action === 'BUY' ? 'text-success' :
pred.action === 'SELL' ? 'text-danger' : 'text-secondary';
const confidence = (pred.confidence * 100).toFixed(1);
const price = pred.predicted_price ? pred.predicted_price.toFixed(2) : '--';
const price = (pred.predicted_price && !isNaN(pred.predicted_price)) ? pred.predicted_price.toFixed(2) : '--';
return `
<div class="d-flex justify-content-between align-items-center mb-1 pb-1 border-bottom">
<div>
<span class="${actionColor} fw-bold">${pred.action}</span>
<span class="text-muted ms-1">${time}</span>
<span class="text-muted ms-1">${timeStr}</span>
</div>
<div class="text-end">
<div>${confidence}%</div>
@@ -864,14 +883,46 @@
.then(response => response.json())
.then(data => {
if (data.success && data.signals.length > 0) {
// Update Accuracy Metrics in Header
if (data.metrics) {
document.getElementById('metric-accuracy').textContent = (data.metrics.accuracy * 100).toFixed(1) + '%';
// If you want to show these in the live banner too:
const banner = document.getElementById('inference-status');
if (banner) {
// Check if we already have a metrics div, if not create one
let metricsDiv = document.getElementById('live-banner-metrics');
if (!metricsDiv) {
metricsDiv = document.createElement('div');
metricsDiv.id = 'live-banner-metrics';
metricsDiv.className = 'mt-1 pt-1 border-top small text-muted d-flex justify-content-between';
banner.querySelector('.small').appendChild(metricsDiv);
}
metricsDiv.innerHTML = `
<span>Acc: ${(data.metrics.accuracy * 100).toFixed(1)}%</span>
<span>Loss: ${data.metrics.loss ? data.metrics.loss.toFixed(4) : '--'}</span>
`;
}
}
const latest = data.signals[0];
document.getElementById('latest-signal').textContent = latest.action;
document.getElementById('latest-confidence').textContent =
(latest.confidence * 100).toFixed(1) + '%';
// Handle prediction price and timestamp safely
const predPrice = (latest.predicted_price && !isNaN(latest.predicted_price))
? latest.predicted_price.toFixed(2)
: '--';
// Format timestamp carefully
let timestamp = new Date().toISOString();
if (latest.timestamp) {
timestamp = latest.timestamp;
}
// Add to prediction history (keep last 5)
predictionHistory.unshift({
timestamp: latest.timestamp || new Date().toISOString(),
timestamp: timestamp,
action: latest.action,
confidence: latest.confidence,
predicted_price: latest.predicted_price
@@ -882,7 +933,7 @@
updatePredictionHistory();
// Update chart with signal markers
if (appState.chartManager) {
if (window.appState && window.appState.chartManager) {
displaySignalOnChart(latest);
}
}
@@ -890,13 +941,12 @@
.catch(error => {
console.error('Error polling signals:', error);
});
// Update charts with latest data
updateChartsWithLiveData();
}, 1000); // Poll every second
}
}
function updateChartsWithLiveData() {
// REMOVED: updateChartsWithLiveData() - Full chart refresh is deprecated in favor of incremental updates
/* DEPRECATED: Old update logic
// Fetch latest chart data
fetch('/api/chart-data', {
method: 'POST',
@@ -924,8 +974,7 @@
console.error('Error updating charts:', error);
});
}
let liveUpdateCount = 0;
*/
function updateSingleChart(timeframe, newData) {
const chart = appState.chartManager.charts[timeframe];
@@ -955,7 +1004,8 @@
// Update counter
liveUpdateCount++;
const counterEl = document.getElementById('live-update-count');
// Note: The element ID is 'live-updates-count' in the header, not 'live-update-count'
const counterEl = document.getElementById('live-updates-count') || document.getElementById('live-update-count');
if (counterEl) {
counterEl.textContent = liveUpdateCount + ' updates';
}

3
NN/models/advanced_transformer_trading.py
View File

@@ -20,7 +20,8 @@ import json
from datetime import datetime
# Configure logging
logger = logging.getLogger(__name__)
from utils.safe_logger import get_logger
logger = get_logger(__name__)
@dataclass
class TradingTransformerConfig:

67
utils/safe_logger.py Normal file
View File

@@ -0,0 +1,67 @@
"""
Safe Logging Wrapper
This module provides a logging wrapper that handles Unicode encoding errors
gracefully, preventing crashes when logging special characters on Windows consoles.
"""
import logging
import sys
import traceback
class SafeLogger:
"""Wrapper for logging.Logger to safely handle unicode characters"""
def __init__(self, name):
self.logger = logging.getLogger(name)
def info(self, msg, *args, **kwargs):
try:
self.logger.info(msg, *args, **kwargs)
except UnicodeEncodeError:
# Fallback: sanitize message or print safely
safe_msg = msg.encode('ascii', 'replace').decode('ascii')
self.logger.info(safe_msg, *args, **kwargs)
except Exception:
# Last resort fallback
pass
def warning(self, msg, *args, **kwargs):
try:
self.logger.warning(msg, *args, **kwargs)
except UnicodeEncodeError:
safe_msg = msg.encode('ascii', 'replace').decode('ascii')
self.logger.warning(safe_msg, *args, **kwargs)
except Exception:
pass
def error(self, msg, *args, **kwargs):
try:
self.logger.error(msg, *args, **kwargs)
except UnicodeEncodeError:
safe_msg = msg.encode('ascii', 'replace').decode('ascii')
self.logger.error(safe_msg, *args, **kwargs)
except Exception:
pass
def debug(self, msg, *args, **kwargs):
try:
self.logger.debug(msg, *args, **kwargs)
except UnicodeEncodeError:
safe_msg = msg.encode('ascii', 'replace').decode('ascii')
self.logger.debug(safe_msg, *args, **kwargs)
except Exception:
pass
def exception(self, msg, *args, **kwargs):
try:
self.logger.exception(msg, *args, **kwargs)
except UnicodeEncodeError:
safe_msg = msg.encode('ascii', 'replace').decode('ascii')
self.logger.exception(safe_msg, *args, **kwargs)
except Exception:
pass
def get_logger(name):
return SafeLogger(name)