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extrema trainer WIP

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This commit is contained in:
Dobromir Popov
2025-08-10 03:20:13 +03:00
parent 1cc8509e87
commit b2faa9b6ca
5 changed files with 315 additions and 35 deletions
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112
web/clean_dashboard.py
View File

@ -2625,10 +2625,114 @@ class CleanTradingDashboard:
self._add_cnn_predictions_to_chart(fig, symbol, df_main, row)
self._add_cob_rl_predictions_to_chart(fig, symbol, df_main, row)
self._add_prediction_accuracy_feedback(fig, symbol, df_main, row)
self._add_williams_pivots_to_chart(fig, symbol, df_main, row)
# 2b. Overlay extrema next-pivot prediction vector (within 5 minutes)
try:
latest_predictions = self._get_latest_model_predictions()
extrema_pred = latest_predictions.get('extrema_trainer', {})
pred_obj = extrema_pred.get('prediction') if isinstance(extrema_pred, dict) else None
if pred_obj and 'predicted_time' in pred_obj and 'predicted_price' in pred_obj:
# Build a short vector from now/current price to predicted pivot
now_ts = df_main.index[-1] if not df_main.empty else datetime.now()
current_price = float(df_main['close'].iloc[-1]) if not df_main.empty else self._get_current_price(symbol)
pt = pred_obj['predicted_time']
# Ensure datetime
if not isinstance(pt, datetime):
try:
pt = pd.to_datetime(pt)
except Exception:
pt = now_ts
pp = float(pred_obj['predicted_price'])
fig.add_trace(
go.Scatter(
x=[now_ts, pt],
y=[current_price, pp],
mode='lines+markers',
line=dict(color='#cddc39', width=2, dash='dot'),
marker=dict(size=6, color='#cddc39'),
name='Next Pivot (<=5m)'
),
row=row, col=1
)
except Exception as e:
logger.debug(f"Error overlaying extrema pivot vector: {e}")
except Exception as e:
logger.warning(f"Error adding model predictions to chart: {e}")
def _add_williams_pivots_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
"""Overlay first two levels of Williams pivot points on the 1m chart."""
try:
if not hasattr(self.data_provider, 'get_recent_pivot_points'):
return
pivots_lvl1 = self.data_provider.get_recent_pivot_points(symbol, level=1, count=50) or []
pivots_lvl2 = self.data_provider.get_recent_pivot_points(symbol, level=2, count=50) or []
# Normalize timestamps to match chart timezone (avoid 3h offset)
try:
from datetime import timezone as _dt_tz
local_tz = datetime.now().astimezone().tzinfo
def normalize_ts(ts: datetime) -> datetime:
try:
if ts is None:
return ts
if getattr(df_main.index, 'tz', None) is not None:
# Match DataFrame index timezone
if ts.tzinfo is None:
ts = ts.replace(tzinfo=_dt_tz.utc)
return ts.astimezone(df_main.index.tz)
# Chart is tz-naive: convert to local and drop tz
if ts.tzinfo is None:
# Treat naive timestamp as UTC coming from server calculations
ts = ts.replace(tzinfo=_dt_tz.utc)
ts_local = ts.astimezone(local_tz)
return ts_local.replace(tzinfo=None)
except Exception:
return ts
except Exception:
def normalize_ts(ts: datetime) -> datetime:
return ts
def to_xy(pivots):
xs_h, ys_h, xs_l, ys_l = [], [], [], []
for p in pivots:
ts = getattr(p, 'timestamp', None)
price = getattr(p, 'price', None)
ptype = getattr(p, 'type', getattr(p, 'pivot_type', 'low'))
if ts and price:
ts = normalize_ts(ts)
if str(ptype).lower() == 'high':
xs_h.append(ts); ys_h.append(price)
else:
xs_l.append(ts); ys_l.append(price)
return xs_h, ys_h, xs_l, ys_l
l1xh, l1yh, l1xl, l1yl = to_xy(pivots_lvl1)
l2xh, l2yh, l2xl, l2yl = to_xy(pivots_lvl2)
if l1xh or l1xl:
if l1xh:
fig.add_trace(go.Scatter(x=l1xh, y=l1yh, mode='markers', name='L1 High',
marker=dict(color='#ff7043', size=7, symbol='triangle-up'), hoverinfo='skip'),
row=row, col=1)
if l1xl:
fig.add_trace(go.Scatter(x=l1xl, y=l1yl, mode='markers', name='L1 Low',
marker=dict(color='#42a5f5', size=7, symbol='triangle-down'), hoverinfo='skip'),
row=row, col=1)
if l2xh or l2xl:
if l2xh:
fig.add_trace(go.Scatter(x=l2xh, y=l2yh, mode='markers', name='L2 High',
marker=dict(color='#ef6c00', size=6, symbol='triangle-up-open'), hoverinfo='skip'),
row=row, col=1)
if l2xl:
fig.add_trace(go.Scatter(x=l2xl, y=l2yl, mode='markers', name='L2 Low',
marker=dict(color='#1e88e5', size=6, symbol='triangle-down-open'), hoverinfo='skip'),
row=row, col=1)
except Exception as e:
logger.debug(f"Error overlaying Williams pivots: {e}")
def _add_dqn_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
"""Add DQN action predictions as directional arrows"""
try:
@ -5900,7 +6004,7 @@ class CleanTradingDashboard:
state_features = self._get_dqn_state_features(symbol, current_price)
if hasattr(self.orchestrator.rl_agent, 'predict'):
return self.orchestrator.rl_agent.predict(state_features)
return 0.5 # Default neutral prediction
return 0.0
except:
return 0.5
@ -5914,7 +6018,7 @@ class CleanTradingDashboard:
if features:
import numpy as np
return self.orchestrator.cnn_model.predict(np.array([features]))
return 0.5 # Default neutral prediction
return 0.0
except:
return 0.5
@ -5928,7 +6032,7 @@ class CleanTradingDashboard:
if features:
import numpy as np
return self.orchestrator.primary_transformer.predict(np.array([features]))
return 0.5 # Default neutral prediction
return 0.0
except:
return 0.5
@ -5940,7 +6044,7 @@ class CleanTradingDashboard:
if cob_features and hasattr(self.orchestrator.cob_rl_agent, 'predict'):
import numpy as np
return self.orchestrator.cob_rl_agent.predict(np.array([cob_features]))
return 0.5 # Default neutral prediction
return 0.0
except:
return 0.5

12
web/component_manager.py
View File

@ -442,20 +442,12 @@ class DashboardComponentManager:
extras.append(html.Small(f"Recent ticks: {len(recent)}", className="text-muted ms-2"))
extras_div = html.Div(extras, className="mb-1") if extras else None
# Insert mini heatmap inside the COB panel (right side)
# Heatmap is rendered in dedicated tiles (avoid duplicate component IDs)
heatmap_graph = None
try:
# The dashboard's data provider is accessible through a global reference on the dashboard instance.
# We embed a placeholder Graph here; actual figure is provided by the dashboard callback tied to this id.
graph_id = 'cob-heatmap-eth' if 'ETH' in symbol else 'cob-heatmap-btc'
heatmap_graph = dcc.Graph(id=graph_id, config={'displayModeBar': False}, style={"height": "220px"})
except Exception:
heatmap_graph = None
children = [dbc.Col(overview_panel, width=5, className="pe-1")]
right_children = []
if heatmap_graph:
right_children.append(heatmap_graph)
# Do not append inline heatmap here to prevent duplicate IDs
right_children.append(ladder_panel)
if extras_div:
right_children.insert(0, extras_div)