cob wip
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Dobromir Popov
@ -47,6 +47,7 @@ class COBStabilityTester:
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self.start_time = None
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self.subscriber_id = None
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self.last_log_time = None
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def _tick_callback(self, tick: MarketTick):
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"""Callback function to receive ticks from the DataProvider."""
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@ -76,6 +77,14 @@ class COBStabilityTester:
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# Store the complete COB snapshot for heatmap generation
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if 'bids' in cob_data and 'asks' in cob_data:
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# Debug: Log structure of first few COB snapshots
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if len(self.cob_snapshots) < 3:
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logger.info(f"DEBUG: COB data structure - bids: {len(cob_data['bids'])} items, asks: {len(cob_data['asks'])} items")
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if cob_data['bids']:
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logger.info(f"DEBUG: First bid: {cob_data['bids'][0]}")
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if cob_data['asks']:
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logger.info(f"DEBUG: First ask: {cob_data['asks'][0]}")
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snapshot = {
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'timestamp': cob_data.get('timestamp', datetime.now()),
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'bids': cob_data['bids'],
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@ -84,6 +93,12 @@ class COBStabilityTester:
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}
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self.cob_snapshots.append(snapshot)
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# Log bucketed COB data every second
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now = datetime.now()
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if self.last_log_time is None or (now - self.last_log_time).total_seconds() >= 1.0:
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self.last_log_time = now
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self._log_bucketed_cob_data(cob_data)
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# Convert COB data to tick-like format for analysis
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if 'stats' in cob_data and 'mid_price' in cob_data['stats']:
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mid_price = cob_data['stats']['mid_price']
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@ -111,6 +126,68 @@ class COBStabilityTester:
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if self.cob_data_received % 10 == 0: # Log every 10th update
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logger.info(f"COB update #{self.cob_data_received}: {symbol} @ ${mid_price:.2f}")
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def _log_bucketed_cob_data(self, cob_data: dict):
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"""Log bucketed COB data every second"""
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try:
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if 'bids' not in cob_data or 'asks' not in cob_data:
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logger.info("COB-1s: No order book data available")
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return
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if 'stats' not in cob_data or 'mid_price' not in cob_data['stats']:
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logger.info("COB-1s: No mid price available")
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return
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mid_price = cob_data['stats']['mid_price']
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if mid_price <= 0:
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return
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# Bucket the order book data
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bid_buckets = {}
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ask_buckets = {}
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# Process bids (top 10)
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for bid in cob_data['bids'][:10]:
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try:
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if isinstance(bid, dict):
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price = float(bid['price'])
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size = float(bid['size'])
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elif isinstance(bid, (list, tuple)) and len(bid) >= 2:
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price = float(bid[0])
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size = float(bid[1])
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else:
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continue
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bucketed_price = round(price / self.price_granularity) * self.price_granularity
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bid_buckets[bucketed_price] = bid_buckets.get(bucketed_price, 0) + size
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except (ValueError, TypeError, IndexError):
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continue
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# Process asks (top 10)
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for ask in cob_data['asks'][:10]:
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try:
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if isinstance(ask, dict):
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price = float(ask['price'])
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size = float(ask['size'])
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elif isinstance(ask, (list, tuple)) and len(ask) >= 2:
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price = float(ask[0])
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size = float(ask[1])
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else:
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continue
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bucketed_price = round(price / self.price_granularity) * self.price_granularity
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ask_buckets[bucketed_price] = ask_buckets.get(bucketed_price, 0) + size
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except (ValueError, TypeError, IndexError):
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continue
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# Format for log output
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bid_str = ", ".join([f"${p:.0f}:{s:.3f}" for p, s in sorted(bid_buckets.items(), reverse=True)])
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ask_str = ", ".join([f"${p:.0f}:{s:.3f}" for p, s in sorted(ask_buckets.items())])
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logger.info(f"COB-1s @ ${mid_price:.2f} | BIDS: {bid_str} | ASKS: {ask_str}")
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except Exception as e:
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logger.warning(f"Error logging bucketed COB data: {e}")
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async def run_test(self):
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"""Run the data collection and plotting test."""
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logger.info(f"Starting COB stability test for {self.symbol} for {self.duration.total_seconds()} seconds...")
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@ -169,66 +246,125 @@ class COBStabilityTester:
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return
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logger.info(f"Creating price and order book heatmap chart...")
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logger.info(f"Data summary: {len(self.price_data)} price points, {len(self.cob_snapshots)} COB snapshots")
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# Prepare data
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# Prepare price data with consistent timestamp handling
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price_df = pd.DataFrame(self.price_data)
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price_df['timestamp'] = pd.to_datetime(price_df['timestamp'])
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# Extract order book data for heatmap
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logger.info(f"Price data time range: {price_df['timestamp'].min()} to {price_df['timestamp'].max()}")
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logger.info(f"Price range: ${price_df['price'].min():.2f} to ${price_df['price'].max():.2f}")
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# Extract order book data for heatmap with consistent timestamp handling
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heatmap_data = []
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for snapshot in self.cob_snapshots:
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timestamp = snapshot['timestamp']
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timestamp = pd.to_datetime(snapshot['timestamp']) # Ensure datetime
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for side in ['bids', 'asks']:
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for order in snapshot[side]:
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if side not in snapshot or not snapshot[side]:
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continue
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# Take top 50 levels for better visualization
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orders = snapshot[side][:50]
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for order in orders:
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try:
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# Handle both dict and list formats
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if isinstance(order, dict):
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price = order['price']
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size = order['size']
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price = float(order['price'])
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size = float(order['size'])
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elif isinstance(order, (list, tuple)) and len(order) >= 2:
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price = float(order[0])
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size = float(order[1])
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else:
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logger.warning(f"Unknown order format: {order}")
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continue
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# Apply granularity bucketing
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bucketed_price = round(price / self.price_granularity) * self.price_granularity
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heatmap_data.append({
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'time': timestamp,
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'price': bucketed_price,
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'size': size,
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'side': side
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})
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except (ValueError, TypeError, IndexError) as e:
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continue
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if not heatmap_data:
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logger.warning("No valid heatmap data found, creating price chart only")
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self._create_simple_price_chart()
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return
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heatmap_df = pd.DataFrame(heatmap_data)
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logger.info(f"Heatmap data: {len(heatmap_df)} order book entries")
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logger.info(f"Heatmap time range: {heatmap_df['time'].min()} to {heatmap_df['time'].max()}")
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# Create plot
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fig, ax1 = plt.subplots(figsize=(16, 8))
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# Create plot with better time handling
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fig, ax = plt.subplots(figsize=(16, 10))
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# Plot price line
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ax1.plot(price_df['timestamp'], price_df['price'], 'cyan', linewidth=1, label='Price')
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# Determine overall time range
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all_times = pd.concat([price_df['timestamp'], heatmap_df['time']])
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time_min = all_times.min()
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time_max = all_times.max()
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# Prepare heatmap
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for side, cmap in zip(['bids', 'asks'], ['Greens', 'Reds']):
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# Create price range for heatmap
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price_min = min(price_df['price'].min(), heatmap_df['price'].min()) - self.price_granularity * 2
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price_max = max(price_df['price'].max(), heatmap_df['price'].max()) + self.price_granularity * 2
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logger.info(f"Chart time range: {time_min} to {time_max}")
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logger.info(f"Chart price range: ${price_min:.2f} to ${price_max:.2f}")
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# Create heatmap first (background)
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for side, cmap, alpha in zip(['bids', 'asks'], ['Greens', 'Reds'], [0.6, 0.6]):
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side_df = heatmap_df[heatmap_df['side'] == side]
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if not side_df.empty:
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# Create more granular bins
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time_bins = pd.date_range(time_min, time_max, periods=min(100, len(side_df) // 10 + 10))
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price_bins = np.arange(price_min, price_max + self.price_granularity, self.price_granularity)
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try:
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# Convert to seconds for histogram
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time_seconds = (side_df['time'] - time_min).dt.total_seconds()
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time_range_seconds = (time_max - time_min).total_seconds()
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if time_range_seconds > 0:
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hist, xedges, yedges = np.histogram2d(
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side_df['time'].astype(np.int64) // 10**9,
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time_seconds,
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side_df['price'],
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bins=[np.unique(side_df['time'].astype(np.int64) // 10**9), np.arange(price_df['price'].min(), price_df['price'].max(), self.price_granularity)],
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bins=[np.linspace(0, time_range_seconds, len(time_bins)), price_bins],
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weights=side_df['size']
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)
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ax1.pcolormesh(pd.to_datetime(xedges, unit='s'), yedges, hist.T, cmap=cmap, alpha=0.5)
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# Enhance plot
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ax1.set_title(f'Price Chart with Order Book Heatmap - {self.symbol}')
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ax1.set_xlabel('Time')
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ax1.set_ylabel('Price (USDT)')
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ax1.legend(loc='upper left')
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ax1.grid(True, alpha=0.3)
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# Convert back to datetime for plotting
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time_edges = pd.to_datetime(xedges, unit='s', origin=time_min)
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if hist.max() > 0: # Only plot if we have data
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pcm = ax.pcolormesh(time_edges, yedges, hist.T,
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cmap=cmap, alpha=alpha, shading='auto')
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logger.info(f"Plotted {side} heatmap: max value = {hist.max():.2f}")
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except Exception as e:
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logger.warning(f"Error creating {side} heatmap: {e}")
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# Plot price line on top
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ax.plot(price_df['timestamp'], price_df['price'], 'yellow', linewidth=2,
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label='Mid Price', alpha=0.9, zorder=10)
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# Enhance plot appearance
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ax.set_title(f'Price Chart with Order Book Heatmap - {self.symbol}\n'
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f'Granularity: ${self.price_granularity} | Duration: {self.duration.total_seconds()}s\n'
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f'Green=Bids, Red=Asks (darker = more volume)', fontsize=14)
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ax.set_xlabel('Time')
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ax.set_ylabel('Price (USDT)')
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ax.legend(loc='upper left')
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ax.grid(True, alpha=0.3)
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# Format time axis
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ax.set_xlim(time_min, time_max)
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fig.autofmt_xdate()
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plt.tight_layout()
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plot_filename = f"price_heatmap_chart_{self.symbol.replace('/', '_')}_{datetime.now():%Y%m%d_%H%M%S}.png"
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plt.savefig(plot_filename, dpi=150)
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plt.savefig(plot_filename, dpi=150, bbox_inches='tight')
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logger.info(f"Price and heatmap chart saved to {plot_filename}")
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plt.show()
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