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cleanup and removed dummy data

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This commit is contained in:
Dobromir Popov
2025-07-26 23:35:14 +03:00
parent 3eb6335169
commit 87942d3807
14 changed files with 220 additions and 2465 deletions
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40
core/data_models.py
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@ -114,42 +114,32 @@ class BaseDataInput:
FIXED_FEATURE_SIZE = 7850
features = []
# OHLCV features for ETH (300 frames x 4 timeframes x 5 features = 6000 features)
# OHLCV features for ETH (up to 300 frames x 4 timeframes x 5 features)
for ohlcv_list in [self.ohlcv_1s, self.ohlcv_1m, self.ohlcv_1h, self.ohlcv_1d]:
# Ensure exactly 300 frames by padding or truncating
# Use actual data only, up to 300 frames
ohlcv_frames = ohlcv_list[-300:] if len(ohlcv_list) >= 300 else ohlcv_list
# Pad with zeros if not enough data
while len(ohlcv_frames) < 300:
# Create a dummy OHLCV bar with zeros
dummy_bar = OHLCVBar(
symbol="ETH/USDT",
timestamp=datetime.now(),
open=0.0, high=0.0, low=0.0, close=0.0, volume=0.0,
timeframe="1s"
)
ohlcv_frames.insert(0, dummy_bar)
# Extract features from exactly 300 frames
# Extract features from actual frames
for bar in ohlcv_frames:
features.extend([bar.open, bar.high, bar.low, bar.close, bar.volume])
# Pad with zeros only if we have some data but less than 300 frames
frames_needed = 300 - len(ohlcv_frames)
if frames_needed > 0:
features.extend([0.0] * (frames_needed * 5)) # 5 features per frame
# BTC OHLCV features (300 frames x 5 features = 1500 features)
# BTC OHLCV features (up to 300 frames x 5 features = 1500 features)
btc_frames = self.btc_ohlcv_1s[-300:] if len(self.btc_ohlcv_1s) >= 300 else self.btc_ohlcv_1s
# Pad BTC data if needed
while len(btc_frames) < 300:
dummy_bar = OHLCVBar(
symbol="BTC/USDT",
timestamp=datetime.now(),
open=0.0, high=0.0, low=0.0, close=0.0, volume=0.0,
timeframe="1s"
)
btc_frames.insert(0, dummy_bar)
# Extract features from actual BTC frames
for bar in btc_frames:
features.extend([bar.open, bar.high, bar.low, bar.close, bar.volume])
# Pad with zeros only if we have some data but less than 300 frames
btc_frames_needed = 300 - len(btc_frames)
if btc_frames_needed > 0:
features.extend([0.0] * (btc_frames_needed * 5)) # 5 features per frame
# COB features (FIXED SIZE: 200 features)
cob_features = []
if self.cob_data: