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This commit is contained in:
Dobromir Popov
2025-07-20 14:27:11 +03:00
parent 0bb4409c30
commit a2c07a1f3e
7 changed files with 561 additions and 316 deletions
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177
web/clean_dashboard.py
View File

@ -2311,49 +2311,56 @@ class CleanTradingDashboard:
cob_data = self.data_provider.get_latest_cob_data(symbol)
logger.debug(f"COB data type for {symbol}: {type(cob_data)}, data: {cob_data}")
if cob_data and isinstance(cob_data, dict) and 'stats' in cob_data:
logger.debug(f"COB snapshot available for {symbol} from centralized data provider")
# Create a snapshot object from the data provider's data
class COBSnapshot:
def __init__(self, data):
# Convert list format [[price, qty], ...] to dictionary format
raw_bids = data.get('bids', [])
raw_asks = data.get('asks', [])
# Convert to dictionary format expected by component manager
self.consolidated_bids = []
for bid in raw_bids:
if isinstance(bid, list) and len(bid) >= 2:
self.consolidated_bids.append({
'price': bid[0],
'size': bid[1],
'total_size': bid[1],
'total_volume_usd': bid[0] * bid[1]
})
self.consolidated_asks = []
for ask in raw_asks:
if isinstance(ask, list) and len(ask) >= 2:
self.consolidated_asks.append({
'price': ask[0],
'size': ask[1],
'total_size': ask[1],
'total_volume_usd': ask[0] * ask[1]
})
self.stats = data.get('stats', {})
# Add direct attributes for new format compatibility
self.volume_weighted_mid = self.stats.get('mid_price', 0)
self.spread_bps = self.stats.get('spread_bps', 0)
self.liquidity_imbalance = self.stats.get('imbalance', 0)
self.total_bid_liquidity = self.stats.get('bid_liquidity', 0)
self.total_ask_liquidity = self.stats.get('ask_liquidity', 0)
self.exchanges_active = ['Binance'] # Default for now
return COBSnapshot(cob_data)
if cob_data and isinstance(cob_data, dict):
# Validate COB data structure
if 'stats' in cob_data and cob_data['stats']:
logger.debug(f"COB snapshot available for {symbol} from centralized data provider")
# Create a snapshot object from the data provider's data
class COBSnapshot:
def __init__(self, data):
# Convert list format [[price, qty], ...] to dictionary format
raw_bids = data.get('bids', [])
raw_asks = data.get('asks', [])
# Convert to dictionary format expected by component manager
self.consolidated_bids = []
for bid in raw_bids:
if isinstance(bid, list) and len(bid) >= 2:
self.consolidated_bids.append({
'price': bid[0],
'size': bid[1],
'total_size': bid[1],
'total_volume_usd': bid[0] * bid[1]
})
self.consolidated_asks = []
for ask in raw_asks:
if isinstance(ask, list) and len(ask) >= 2:
self.consolidated_asks.append({
'price': ask[0],
'size': ask[1],
'total_size': ask[1],
'total_volume_usd': ask[0] * ask[1]
})
self.stats = data.get('stats', {})
# Add direct attributes for new format compatibility
self.volume_weighted_mid = self.stats.get('mid_price', 0)
self.spread_bps = self.stats.get('spread_bps', 0)
self.liquidity_imbalance = self.stats.get('imbalance', 0)
self.total_bid_liquidity = self.stats.get('bid_liquidity', 0)
self.total_ask_liquidity = self.stats.get('ask_liquidity', 0)
self.exchanges_active = ['Binance'] # Default for now
return COBSnapshot(cob_data)
else:
# Data exists but no stats - this is the "Invalid COB data" case
logger.debug(f"COB data for {symbol} missing stats structure: {type(cob_data)}, keys: {list(cob_data.keys()) if isinstance(cob_data, dict) else 'not dict'}")
return None
else:
logger.warning(f"Invalid COB data for {symbol}: type={type(cob_data)}, has_stats={'stats' in cob_data if isinstance(cob_data, dict) else False}")
logger.debug(f"No COB data available for {symbol} from data provider")
return None
except Exception as e:
logger.error(f"Error getting COB data from data provider: {e}")
@ -5358,6 +5365,18 @@ class CleanTradingDashboard:
self.latest_cob_data[symbol] = cob_snapshot
# Store in history for moving average calculations
if not hasattr(self, 'cob_data_history'):
self.cob_data_history = {'ETH/USDT': deque(maxlen=61), 'BTC/USDT': deque(maxlen=61)}
if symbol in self.cob_data_history:
self.cob_data_history[symbol].append(cob_snapshot)
# Update last update timestamp
if not hasattr(self, 'cob_last_update'):
self.cob_last_update = {}
self.cob_last_update[symbol] = time.time()
# Update current price from COB data
if 'stats' in cob_snapshot and 'mid_price' in cob_snapshot['stats']:
self.current_prices[symbol] = cob_snapshot['stats']['mid_price']
@ -6021,33 +6040,71 @@ class CleanTradingDashboard:
raise
def _calculate_cumulative_imbalance(self, symbol: str) -> Dict[str, float]:
"""Calculate average imbalance over multiple time windows."""
"""Calculate Moving Averages (MA) of imbalance over different periods."""
stats = {}
now = time.time()
history = self.cob_data_history.get(symbol)
if not history:
return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
for name, duration in periods.items():
recent_imbalances = []
for snap in history:
# Check if snap is a valid dict with timestamp and stats
if isinstance(snap, dict) and 'timestamp' in snap and (now - snap['timestamp'] <= duration) and 'stats' in snap and snap['stats']:
imbalance = snap['stats'].get('imbalance')
if imbalance is not None:
recent_imbalances.append(imbalance)
# Convert history to list and get recent snapshots
history_list = list(history)
if not history_list:
return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
if recent_imbalances:
stats[name] = sum(recent_imbalances) / len(recent_imbalances)
else:
stats[name] = 0.0
# Extract imbalance values from recent snapshots
imbalances = []
for snap in history_list:
if isinstance(snap, dict) and 'stats' in snap and snap['stats']:
imbalance = snap['stats'].get('imbalance')
if imbalance is not None:
imbalances.append(imbalance)
if not imbalances:
return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
# Calculate Moving Averages over different periods
# MA periods: 1s=1 period, 5s=5 periods, 15s=15 periods, 60s=60 periods
ma_periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
# Debug logging to verify cumulative imbalance calculation
for name, period in ma_periods.items():
if len(imbalances) >= period:
# Calculate SMA over the last 'period' values
recent_imbalances = imbalances[-period:]
sma_value = sum(recent_imbalances) / len(recent_imbalances)
# Also calculate EMA for better responsiveness
if period > 1:
# EMA calculation with alpha = 2/(period+1)
alpha = 2.0 / (period + 1)
ema_value = recent_imbalances[0] # Start with first value
for value in recent_imbalances[1:]:
ema_value = alpha * value + (1 - alpha) * ema_value
# Use EMA for better responsiveness
stats[name] = ema_value
else:
# For 1s, use SMA (no EMA needed)
stats[name] = sma_value
else:
# If not enough data, use available data
available_imbalances = imbalances[-min(period, len(imbalances)):]
if available_imbalances:
if len(available_imbalances) > 1:
# Calculate EMA for available data
alpha = 2.0 / (len(available_imbalances) + 1)
ema_value = available_imbalances[0]
for value in available_imbalances[1:]:
ema_value = alpha * value + (1 - alpha) * ema_value
stats[name] = ema_value
else:
# Single value, use as is
stats[name] = available_imbalances[0]
else:
stats[name] = 0.0
# Debug logging to verify MA calculation
if any(value != 0.0 for value in stats.values()):
logger.debug(f"[CUMULATIVE-IMBALANCE] {symbol}: {stats}")
logger.debug(f"[MOVING-AVERAGE-IMBALANCE] {symbol}: {stats} (from {len(imbalances)} snapshots)")
return stats

8
web/component_manager.py
View File

@ -412,10 +412,10 @@ class DashboardComponentManager:
]),
html.Div([
self._create_timeframe_imbalance("1s", stats.get('imbalance_1s', imbalance)),
self._create_timeframe_imbalance("5s", stats.get('imbalance_5s', imbalance)),
self._create_timeframe_imbalance("15s", stats.get('imbalance_15s', imbalance)),
self._create_timeframe_imbalance("60s", stats.get('imbalance_60s', imbalance)),
self._create_timeframe_imbalance("1s", cumulative_imbalance_stats.get('1s', imbalance)),
self._create_timeframe_imbalance("5s", cumulative_imbalance_stats.get('5s', imbalance)),
self._create_timeframe_imbalance("15s", cumulative_imbalance_stats.get('15s', imbalance)),
self._create_timeframe_imbalance("60s", cumulative_imbalance_stats.get('60s', imbalance)),
], className="d-flex justify-content-between mb-2"),
html.Div(imbalance_stats_display),

74
web/templated_dashboard.py
View File

@ -986,33 +986,71 @@ class TemplatedTradingDashboard:
logger.debug(f"TEMPLATED DASHBOARD: Error generating bucketed COB data: {e}")
def _calculate_cumulative_imbalance(self, symbol: str) -> Dict[str, float]:
"""Calculate average imbalance over multiple time windows."""
"""Calculate Moving Averages (MA) of imbalance over different periods."""
stats = {}
now = time.time()
history = self.cob_data_history.get(symbol)
if not history:
return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
for name, duration in periods.items():
recent_imbalances = []
for snap in history:
# Check if snap is a valid dict with timestamp and stats
if isinstance(snap, dict) and 'timestamp' in snap and (now - snap['timestamp'] <= duration) and 'stats' in snap and snap['stats']:
imbalance = snap['stats'].get('imbalance')
if imbalance is not None:
recent_imbalances.append(imbalance)
# Convert history to list and get recent snapshots
history_list = list(history)
if not history_list:
return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
if recent_imbalances:
stats[name] = sum(recent_imbalances) / len(recent_imbalances)
else:
stats[name] = 0.0
# Extract imbalance values from recent snapshots
imbalances = []
for snap in history_list:
if isinstance(snap, dict) and 'stats' in snap and snap['stats']:
imbalance = snap['stats'].get('imbalance')
if imbalance is not None:
imbalances.append(imbalance)
if not imbalances:
return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
# Calculate Moving Averages over different periods
# MA periods: 1s=1 period, 5s=5 periods, 15s=15 periods, 60s=60 periods
ma_periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
# Debug logging to verify cumulative imbalance calculation
for name, period in ma_periods.items():
if len(imbalances) >= period:
# Calculate SMA over the last 'period' values
recent_imbalances = imbalances[-period:]
sma_value = sum(recent_imbalances) / len(recent_imbalances)
# Also calculate EMA for better responsiveness
if period > 1:
# EMA calculation with alpha = 2/(period+1)
alpha = 2.0 / (period + 1)
ema_value = recent_imbalances[0] # Start with first value
for value in recent_imbalances[1:]:
ema_value = alpha * value + (1 - alpha) * ema_value
# Use EMA for better responsiveness
stats[name] = ema_value
else:
# For 1s, use SMA (no EMA needed)
stats[name] = sma_value
else:
# If not enough data, use available data
available_imbalances = imbalances[-min(period, len(imbalances)):]
if available_imbalances:
if len(available_imbalances) > 1:
# Calculate EMA for available data
alpha = 2.0 / (len(available_imbalances) + 1)
ema_value = available_imbalances[0]
for value in available_imbalances[1:]:
ema_value = alpha * value + (1 - alpha) * ema_value
stats[name] = ema_value
else:
# Single value, use as is
stats[name] = available_imbalances[0]
else:
stats[name] = 0.0
# Debug logging to verify MA calculation
if any(value != 0.0 for value in stats.values()):
logger.debug(f"TEMPLATED DASHBOARD: [CUMULATIVE-IMBALANCE] {symbol}: {stats}")
logger.debug(f"TEMPLATED DASHBOARD: [MOVING-AVERAGE-IMBALANCE] {symbol}: {stats} (from {len(imbalances)} snapshots)")
return stats