COB tests and data analysis
This commit is contained in:
Dobromir Popov
@ -10,6 +10,7 @@ import pandas as pd
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from matplotlib.colors import LogNorm
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from core.data_provider import DataProvider, MarketTick
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from core.config import get_config
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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@ -17,11 +18,33 @@ logger = logging.getLogger(__name__)
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class COBStabilityTester:
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def __init__(self, symbol='ETH/USDT', duration_seconds=15):
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def __init__(self, symbol='ETHUSDT', duration_seconds=15):
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self.symbol = symbol
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self.duration = timedelta(seconds=duration_seconds)
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self.ticks = deque()
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self.data_provider = DataProvider(symbols=[self.symbol], timeframes=['1s'])
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# Set granularity (buckets) based on symbol
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if 'ETH' in symbol.upper():
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self.price_granularity = 1.0 # 1 USD for ETH
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elif 'BTC' in symbol.upper():
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self.price_granularity = 10.0 # 10 USD for BTC
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else:
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self.price_granularity = 1.0 # Default 1 USD
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logger.info(f"Using price granularity: ${self.price_granularity} for {symbol}")
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# Initialize DataProvider the same way as clean_dashboard
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logger.info("Initializing DataProvider like in clean_dashboard...")
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self.data_provider = DataProvider() # Use default constructor like clean_dashboard
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# Initialize COB data collection like clean_dashboard does
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self.cob_data_received = 0
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self.latest_cob_data = {}
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# Store all COB snapshots for heatmap generation
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self.cob_snapshots = deque()
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self.price_data = [] # For price line chart
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self.start_time = None
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self.subscriber_id = None
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@ -33,16 +56,79 @@ class COBStabilityTester:
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# Store all ticks
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self.ticks.append(tick)
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def _cob_data_callback(self, symbol: str, cob_data: dict):
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"""Callback function to receive COB data from the DataProvider."""
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self.cob_data_received += 1
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self.latest_cob_data[symbol] = cob_data
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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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snapshot = {
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'timestamp': cob_data.get('timestamp', datetime.now()),
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'bids': cob_data['bids'],
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'asks': cob_data['asks'],
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'stats': cob_data.get('stats', {})
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}
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self.cob_snapshots.append(snapshot)
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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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if mid_price > 0:
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# Store price data for line chart
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self.price_data.append({
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'timestamp': cob_data.get('timestamp', datetime.now()),
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'price': mid_price
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})
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# Create a synthetic tick from COB data
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synthetic_tick = MarketTick(
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symbol=symbol,
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timestamp=cob_data.get('timestamp', datetime.now()),
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price=mid_price,
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volume=cob_data.get('stats', {}).get('total_volume', 0),
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quantity=0, # Not available in COB data
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side='unknown', # COB data doesn't have side info
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trade_id=f"cob_{self.cob_data_received}",
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is_buyer_maker=False,
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raw_data=cob_data
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)
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self.ticks.append(synthetic_tick)
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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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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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# Subscribe to ticks
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self.subscriber_id = self.data_provider.subscribe_to_ticks(self._tick_callback, symbols=[self.symbol])
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# Initialize COB collection like clean_dashboard does
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try:
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logger.info("Starting COB collection in data provider...")
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self.data_provider.start_cob_collection()
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logger.info("Started COB collection in data provider")
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# Subscribe to COB updates
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logger.info("Subscribing to COB data updates...")
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self.data_provider.subscribe_to_cob(self._cob_data_callback)
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logger.info("Subscribed to COB data updates from data provider")
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except Exception as e:
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logger.error(f"Failed to start COB collection or subscribe: {e}")
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# Subscribe to ticks as fallback
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try:
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self.subscriber_id = self.data_provider.subscribe_to_ticks(self._tick_callback, symbols=[self.symbol])
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logger.info("Subscribed to tick data as fallback")
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except Exception as e:
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logger.warning(f"Failed to subscribe to ticks: {e}")
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# Start the data provider's real-time streaming
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await self.data_provider.start_real_time_streaming()
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try:
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await self.data_provider.start_real_time_streaming()
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logger.info("Started real-time streaming")
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except Exception as e:
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logger.error(f"Failed to start real-time streaming: {e}")
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# Collect data for the specified duration
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self.start_time = datetime.now()
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@ -57,57 +143,203 @@ class COBStabilityTester:
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logger.info(f"Finished collecting data. Total ticks: {len(self.ticks)}")
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# Plot the results
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if self.ticks:
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self.plot_spectrogram()
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if self.price_data and self.cob_snapshots:
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self.create_price_heatmap_chart()
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elif self.ticks:
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self._create_simple_price_chart()
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else:
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logger.warning("No ticks were collected. Cannot generate plot.")
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logger.warning("No data was collected. Cannot generate plot.")
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def plot_spectrogram(self):
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"""Create a spectrogram-like plot of trade intensity."""
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"""Create a bookmap-style visualization showing order book depth over time."""
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if not self.ticks:
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logger.warning("No ticks to plot.")
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return
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df = pd.DataFrame([{
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'timestamp': tick.timestamp,
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'price': tick.price,
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'volume': tick.volume,
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'side': 1 if tick.side == 'buy' else -1
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} for tick in self.ticks])
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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df = df.set_index('timestamp')
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# Create the plot
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fig, ax = plt.subplots(figsize=(15, 8))
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# Define bins for the 2D histogram
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time_bins = pd.date_range(df.index.min(), df.index.max(), periods=100)
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price_bins = np.linspace(df['price'].min(), df['price'].max(), 100)
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# Create the 2D histogram
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# x-axis: time, y-axis: price, weights: volume
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h, xedges, yedges = np.histogram2d(
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df.index.astype(np.int64) // 10**9,
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df['price'],
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bins=[time_bins.astype(np.int64) // 10**9, price_bins],
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weights=df['volume']
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)
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# Use a logarithmic color scale for better visibility of smaller trades
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pcm = ax.pcolormesh(time_bins, price_bins, h.T, norm=LogNorm(vmin=1e-3, vmax=h.max()), cmap='inferno')
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fig.colorbar(pcm, ax=ax, label='Trade Volume (USDT)')
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ax.set_title(f'Trade Intensity Spectrogram for {self.symbol}')
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ax.set_xlabel('Time')
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ax.set_ylabel('Price (USDT)')
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logger.info(f"Creating bookmap-style visualization with {len(self.ticks)} data points...")
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# Extract order book data from ticks
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time_points = []
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bid_data = []
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ask_data = []
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price_levels = set()
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for tick in self.ticks:
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if hasattr(tick, 'raw_data') and tick.raw_data:
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cob_data = tick.raw_data
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if 'bids' in cob_data and 'asks' in cob_data:
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timestamp = tick.timestamp
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# Extract bid levels (green - buy orders)
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bids = cob_data['bids'][:20] # Top 20 levels
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for bid in bids:
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if isinstance(bid, dict) and 'price' in bid and 'size' in bid:
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bid_data.append({
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'time': timestamp,
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'price': bid['price'],
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'size': bid['size'],
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'side': 'bid'
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})
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price_levels.add(bid['price'])
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# Extract ask levels (red - sell orders)
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asks = cob_data['asks'][:20] # Top 20 levels
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for ask in asks:
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if isinstance(ask, dict) and 'price' in ask and 'size' in ask:
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ask_data.append({
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'time': timestamp,
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'price': ask['price'],
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'size': ask['size'],
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'side': 'ask'
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})
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price_levels.add(ask['price'])
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if not bid_data and not ask_data:
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logger.warning("No order book data found in ticks. Cannot create bookmap visualization.")
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# Fallback to simple price chart
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self._create_simple_price_chart()
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return
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logger.info(f"Extracted {len(bid_data)} bid levels and {len(ask_data)} ask levels")
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# Create the bookmap visualization
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fig, ax = plt.subplots(figsize=(16, 10))
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# Combine all data
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all_data = bid_data + ask_data
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if not all_data:
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logger.warning("No order book data to plot")
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return
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# Create DataFrames
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df = pd.DataFrame(all_data)
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df['time'] = pd.to_datetime(df['time'])
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# Create price bins (like in bookmap)
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price_min = df['price'].min()
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price_max = df['price'].max()
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price_range = price_max - price_min
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if price_range == 0:
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logger.warning("No price variation in data")
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return
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# Create time bins
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time_min = df['time'].min()
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time_max = df['time'].max()
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# Create 2D heatmaps for bids and asks separately
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time_bins = pd.date_range(time_min, time_max, periods=100)
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price_bins = np.linspace(price_min, price_max, 200) # Higher resolution for price
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# Separate bid and ask data
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bid_df = df[df['side'] == 'bid']
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ask_df = df[df['side'] == 'ask']
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# Create bid heatmap (green)
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if not bid_df.empty:
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bid_hist, _, _ = np.histogram2d(
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bid_df['time'].astype(np.int64) // 10**9,
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bid_df['price'],
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bins=[time_bins.astype(np.int64) // 10**9, price_bins],
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weights=bid_df['size']
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)
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# Plot bids in green (buying pressure)
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bid_mask = bid_hist > 0
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pcm_bid = ax.pcolormesh(
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time_bins, price_bins, bid_hist.T,
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cmap='Greens', alpha=0.7, vmin=0, vmax=bid_hist.max()
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)
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# Create ask heatmap (red)
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if not ask_df.empty:
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ask_hist, _, _ = np.histogram2d(
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ask_df['time'].astype(np.int64) // 10**9,
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ask_df['price'],
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bins=[time_bins.astype(np.int64) // 10**9, price_bins],
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weights=ask_df['size']
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)
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# Plot asks in red (selling pressure)
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ask_mask = ask_hist > 0
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pcm_ask = ax.pcolormesh(
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time_bins, price_bins, ask_hist.T,
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cmap='Reds', alpha=0.7, vmin=0, vmax=ask_hist.max()
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)
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# Add mid price line
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mid_prices = []
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mid_times = []
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for tick in self.ticks:
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if hasattr(tick, 'raw_data') and tick.raw_data and 'stats' in tick.raw_data:
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stats = tick.raw_data['stats']
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if 'mid_price' in stats and stats['mid_price'] > 0:
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mid_prices.append(stats['mid_price'])
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mid_times.append(tick.timestamp)
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if mid_prices:
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ax.plot(pd.to_datetime(mid_times), mid_prices, 'yellow', linewidth=2, alpha=0.8, label='Mid Price')
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# Styling like bookmap
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ax.set_facecolor('black')
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fig.patch.set_facecolor('black')
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ax.set_title(f'Order Book Depth Map - {self.symbol}\n(Green=Bids/Buy Orders, Red=Asks/Sell Orders)',
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color='white', fontsize=14)
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ax.set_xlabel('Time', color='white')
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ax.set_ylabel('Price (USDT)', color='white')
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# White ticks and labels
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ax.tick_params(colors='white')
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ax.spines['bottom'].set_color('white')
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ax.spines['top'].set_color('white')
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ax.spines['right'].set_color('white')
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ax.spines['left'].set_color('white')
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# Add colorbar for bid data
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if not bid_df.empty:
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cbar_bid = fig.colorbar(pcm_bid, ax=ax, location='right', pad=0.02, shrink=0.5)
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cbar_bid.set_label('Bid Size (Order Volume)', color='white', labelpad=15)
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cbar_bid.ax.yaxis.set_tick_params(color='white')
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cbar_bid.ax.yaxis.set_tick_params(labelcolor='white')
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# Format the x-axis to show time properly
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fig.autofmt_xdate()
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plot_filename = f"cob_stability_spectrogram_{self.symbol.replace('/', '_')}_{datetime.now():%Y%m%d_%H%M%S}.png"
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if mid_prices:
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ax.legend(loc='upper left')
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plt.tight_layout()
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plot_filename = f"cob_bookmap_{self.symbol.replace('/', '_')}_{datetime.now():%Y%m%d_%H%M%S}.png"
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plt.savefig(plot_filename, facecolor='black', dpi=150)
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logger.info(f"Bookmap-style plot saved to {plot_filename}")
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plt.show()
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def _create_simple_price_chart(self):
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"""Create a simple price chart as fallback"""
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logger.info("Creating simple price chart as fallback...")
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prices = []
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times = []
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for tick in self.ticks:
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if tick.price > 0:
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prices.append(tick.price)
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times.append(tick.timestamp)
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if not prices:
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logger.warning("No price data to plot")
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return
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fig, ax = plt.subplots(figsize=(15, 8))
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ax.plot(pd.to_datetime(times), prices, 'cyan', linewidth=1)
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ax.set_title(f'Price Chart - {self.symbol}')
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ax.set_xlabel('Time')
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ax.set_ylabel('Price (USDT)')
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fig.autofmt_xdate()
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plot_filename = f"cob_price_chart_{self.symbol.replace('/', '_')}_{datetime.now():%Y%m%d_%H%M%S}.png"
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plt.savefig(plot_filename)
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logger.info(f"Plot saved to {plot_filename}")
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logger.info(f"Price chart saved to {plot_filename}")
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plt.show()
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