fixed showing all moves
This commit is contained in:
Dobromir Popov
parent
44b02b4e7d
commit
b0a57c5330
83
realtime.py
83
realtime.py
@ -30,6 +30,9 @@ class BinanceHistoricalData:
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"""
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def __init__(self):
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self.base_url = "https://api.binance.com/api/v3"
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self.cache_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'cache')
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if not os.path.exists(self.cache_dir):
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os.makedirs(self.cache_dir)
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def get_historical_candles(self, symbol, interval_seconds=3600, limit=1000):
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"""
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@ -60,6 +63,14 @@ class BinanceHistoricalData:
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# Format symbol for Binance API (remove slash)
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formatted_symbol = symbol.replace("/", "")
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# Check if we have cached data first
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cache_file = self._get_cache_filename(formatted_symbol, interval)
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cached_data = self._load_from_cache(formatted_symbol, interval)
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if cached_data is not None and len(cached_data) >= limit:
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logger.info(f"Using cached historical data for {symbol} ({interval})")
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return cached_data
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try:
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# Build URL for klines endpoint
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url = f"{self.base_url}/klines"
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@ -93,14 +104,48 @@ class BinanceHistoricalData:
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# Sort by timestamp
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df = df.sort_values("timestamp")
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# Save to cache for future use
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self._save_to_cache(df, formatted_symbol, interval)
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logger.info(f"Fetched {len(df)} candles for {symbol} ({interval})")
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return df
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except Exception as e:
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logger.error(f"Error fetching historical data from Binance: {str(e)}")
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# Return cached data if we have it, even if it's not enough
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if cached_data is not None:
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logger.warning(f"Using cached data instead (may be incomplete)")
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return cached_data
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# Return empty dataframe on error
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return pd.DataFrame()
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def _get_cache_filename(self, symbol, interval):
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"""Get filename for cache file"""
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return os.path.join(self.cache_dir, f"{symbol}_{interval}_candles.csv")
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def _load_from_cache(self, symbol, interval):
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"""Load candles from cache file"""
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try:
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cache_file = self._get_cache_filename(symbol, interval)
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if os.path.exists(cache_file):
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df = pd.read_csv(cache_file)
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df["timestamp"] = pd.to_datetime(df["timestamp"])
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return df
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except Exception as e:
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logger.error(f"Error loading cached data: {str(e)}")
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return None
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def _save_to_cache(self, df, symbol, interval):
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"""Save candles to cache file"""
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try:
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cache_file = self._get_cache_filename(symbol, interval)
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df.to_csv(cache_file, index=False)
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logger.info(f"Saved {len(df)} candles to cache: {cache_file}")
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return True
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except Exception as e:
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logger.error(f"Error saving to cache: {str(e)}")
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return False
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def get_recent_trades(self, symbol, limit=1000):
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"""Get recent trades for a symbol"""
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formatted_symbol = symbol.replace("/", "")
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@ -553,6 +598,7 @@ class TickStorage:
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try:
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# Load data for different timeframes
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timeframes = [
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(1, '1s'), # 1 second
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(60, '1m'), # 1 minute
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(300, '5m'), # 5 minutes
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(900, '15m'), # 15 minutes
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@ -562,7 +608,22 @@ class TickStorage:
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]
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for interval_seconds, interval_key in timeframes:
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df = historical_data.get_historical_candles(symbol, interval_seconds)
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# Set appropriate limits based on timeframe
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limit = 1000 # Default
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if interval_seconds == 1:
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limit = 500 # 1s is too much data, limit to 500
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elif interval_seconds < 60:
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limit = 750 # For seconds-level data
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elif interval_seconds < 300:
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limit = 1000 # 1m
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elif interval_seconds < 900:
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limit = 500 # 5m
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elif interval_seconds < 3600:
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limit = 300 # 15m
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else:
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limit = 200 # hourly/daily data
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df = historical_data.get_historical_candles(symbol, interval_seconds, limit)
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if df is not None and not df.empty:
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logger.info(f"Loaded {len(df)} historical candles for {symbol} ({interval_key})")
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@ -578,7 +639,9 @@ class TickStorage:
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}
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self.candles[interval_key].append(candle)
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# Also use the close price to simulate ticks
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# For 1m and above, also use the close price to simulate ticks
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# but don't do this for seconds-level data as it creates too many ticks
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if interval_seconds >= 60 and interval_key == '1m':
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self.add_tick(
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price=row['close'],
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volume=row['volume'],
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@ -904,8 +967,13 @@ class RealTimeChart:
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sell_times = []
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sell_prices = []
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# Use only last 20 positions for clarity
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for position in self.positions[-20:]:
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# Use all positions for chart display
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# Filter recent ones based on visible time range
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now = datetime.now()
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time_limit = now - timedelta(hours=24) # Show at most 24h of trades
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for position in self.positions:
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if position.entry_timestamp > time_limit:
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if position.action == "BUY":
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buy_times.append(position.entry_timestamp)
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buy_prices.append(position.entry_price)
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@ -1112,14 +1180,17 @@ class RealTimeChart:
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winning_trades = sum(1 for p in self.positions if p.pnl and p.pnl > 0)
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win_rate = winning_trades / total_trades * 100 if total_trades > 0 else 0
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# Format display colors for PnL
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pnl_color = "green" if self.accumulative_pnl >= 0 else "red"
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summary_row = html.Tr([
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html.Td("SUMMARY", colSpan=2, style={"fontWeight": "bold"}),
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html.Td(f"Trades: {total_trades}"),
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html.Td(f"Win Rate: {win_rate:.1f}%"),
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html.Td("Total PnL:", style={"fontWeight": "bold"}),
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html.Td(f"${self.accumulative_pnl:.2f}",
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style={"color": "green" if self.accumulative_pnl >= 0 else "red", "fontWeight": "bold"}),
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html.Td("")
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style={"color": pnl_color, "fontWeight": "bold"}),
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html.Td(f"Balance: ${self.current_balance:.2f}")
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], style={"backgroundColor": "rgba(80, 80, 80, 0.3)"})
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# Create the table with improved styling
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@ -516,17 +516,27 @@ class RLTrainingIntegrator:
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if len(self.price_history) > self.price_history_max_len:
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self.price_history = self.price_history[-self.price_history_max_len:]
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# Normalize rewards to be realistic for crypto trading (smaller values)
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normalized_reward = reward * 0.1 # Scale down rewards
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if abs(normalized_reward) > 5.0: # Cap maximum reward value
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normalized_reward = 5.0 if normalized_reward > 0 else -5.0
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# Update session PnL and balance
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self.session_step += 1
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self.session_pnl += reward
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self.session_pnl += normalized_reward
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# Increase balance based on reward
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self.session_balance += reward
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# Increase balance based on reward - cap to reasonable values
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self.session_balance += normalized_reward
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self.session_balance = min(self.session_balance, 1000.0) # Cap maximum balance
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self.session_balance = max(self.session_balance, 0.0) # Prevent negative balance
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# Update chart's accumulativePnL and balance if available
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if self.chart:
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if hasattr(self.chart, 'accumulative_pnl'):
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self.chart.accumulative_pnl = self.session_pnl
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# Cap accumulated PnL to reasonable values
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self.chart.accumulative_pnl = min(self.chart.accumulative_pnl, 500.0)
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self.chart.accumulative_pnl = max(self.chart.accumulative_pnl, -100.0)
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if hasattr(self.chart, 'current_balance'):
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self.chart.current_balance = self.session_balance
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@ -751,7 +761,7 @@ def _add_trade_compat(chart, price, timestamp, amount, pnl=0.0, action="BUY"):
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position.close(price, timestamp)
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# Use realistic PnL values rather than the enormous ones from the model
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# Cap PnL to reasonable values based on position size and price
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max_reasonable_pnl = price * amount * 0.10 # Max 10% profit
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max_reasonable_pnl = price * amount * 0.05 # Max 5% profit per trade
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if abs(pnl) > max_reasonable_pnl:
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if pnl > 0:
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pnl = max_reasonable_pnl * 0.8 # Positive but reasonable
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@ -762,11 +772,14 @@ def _add_trade_compat(chart, price, timestamp, amount, pnl=0.0, action="BUY"):
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# Update chart's accumulated PnL if available
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if hasattr(chart, 'accumulative_pnl'):
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chart.accumulative_pnl += pnl
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# Cap accumulated PnL to reasonable values
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chart.accumulative_pnl = min(chart.accumulative_pnl, 500.0)
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chart.accumulative_pnl = max(chart.accumulative_pnl, -100.0)
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# Add to positions list, keeping only the last 10 if we have more
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# Add to positions list, keeping only the last 200 for chart display
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chart.positions.append(position)
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if len(chart.positions) > 10:
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chart.positions = chart.positions[-10:]
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if len(chart.positions) > 200:
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chart.positions = chart.positions[-200:]
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logger.info(f"Added {action} trade: price={price:.2f}, amount={amount}, pnl={pnl:.2f}")
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return True
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