Add Jared read-only token OHLCV ingestion and manual contract lookup

app.py

@@ -75,13 +75,27 @@ def run_dashboard(data_src, ticker, manual_token, file_upload, timeframe, start_
         sentiment_text, sentiment_score = sentiment_analysis(ticker, start_date, end_date, news_api_key)
 
         features = valid_indicators # Use valid_indicators for feature
+        # Newly launched Jared-observed tokens may have only a few real candles.
+        # Adapt sequence parameters to the available history rather than creating
+        # synthetic rows or allowing an impossible preprocessing request.
+        requested_window = int(window_size)
+        requested_horizon = int(horizon)
+        available_rows = len(df.dropna(subset=["value"]))
+        effective_window = min(requested_window, max(5, available_rows // 3))
+        max_horizon_for_samples = available_rows - effective_window - 9
+        effective_horizon = min(requested_horizon, max(1, max_horizon_for_samples))
+        if available_rows - effective_window - effective_horizon + 1 < 10:
+            effective_window = max(5, available_rows - effective_horizon - 9)
+        sizing_note = ""
+        if effective_window != requested_window or effective_horizon != requested_horizon:
+            sizing_note = f" Sparse history: LSTM window {requested_window}->{effective_window}, horizon {requested_horizon}->{effective_horizon}."
         target = 'value'
         result = get_model(
             df=df,
             features=features,
             target=target,
             model_name=model,
-            horizon=horizon,
+            horizon=effective_horizon,
             hidden_units=hidden_units,
             n_layers=n_layers,
             epochs=epochs,
@@ -90,7 +104,7 @@ def run_dashboard(data_src, ticker, manual_token, file_upload, timeframe, start_
             beta2=beta2,
             weight_decay=weight_decay,
             dropout=dropout,
-            window_size=window_size,
+            window_size=effective_window,
             test_split=test_split,
             selector_method=feat_selector,
             importance_threshold=feat_threshold
@@ -144,7 +158,7 @@ def run_dashboard(data_src, ticker, manual_token, file_upload, timeframe, start_
         return [
             chart_plot, sentiment_text, signals_table, backtest_plot, future_plot, future_table,
             r2_plot, error_plot, precision_recall_plot, risk_plot, loss_plot, architecture_plot,
-            f"Dashboard generated successfully | source={df.attrs.get('source', data_src)} | token={df.attrs.get('token_address', display_ticker)}",
+            f"Dashboard generated successfully | source={df.attrs.get('source', data_src)} | token={df.attrs.get('token_address', display_ticker)}.{sizing_note}",
             chart_png, signals_csv, predictions_csv, signals_plot, log_output
         ]
     except Exception as e: