Add CUDA device compatibility checks to TimesFM backend

- Implemented a function to check if the current CUDA device is supported by the installed PyTorch version.
- Added warnings for unsupported CUDA devices, informing users that the CPU will be used instead.
- Updated the TimesFMBackend class to utilize the new compatibility checks and handle GPU names appropriately.
- Introduced unit tests to validate the behavior of the CUDA compatibility function with various simulated CUDA environments.

.env.example

@@ -86,7 +86,7 @@ DSE_PASSWORD=
 #TRADINGAGENTS_SOCIAL_MEDIA_ENABLED=false
 #TRADINGAGENTS_MACRO_DATA_ENABLED=false
 #TRADINGAGENTS_PREDICTION_MARKETS_ENABLED=false
-#TRADINGAGENTS_DSE_GATEWAY_URL=https://gateway.dohasecurities.com.bd
+# TRADINGAGENTS_DSE_GATEWAY_URL=https://gateway.dohasecurities.com.bd
 #TRADINGAGENTS_DSE_REQUEST_TIMEOUT=30
 #TRADINGAGENTS_DSE_VERIFY_SSL=true
 #TRADINGAGENTS_DSE_BENCHMARK_TICKER=DSEX

analysis-ui/src/app/timesfm-prediction.component.html

@@ -79,24 +79,88 @@
     <section class="chart-card">
       <header>
         <div>
-          <p class="eyebrow">HISTORICAL BACKTEST + FORWARD PATH</p>
-          <h2>Actual candles against {{ report.model.name }}</h2>
+          <p class="eyebrow">{{ chartMode() === 'future' ? 'FORWARD FORECAST' : 'HISTORICAL BACKTEST' }}</p>
+          <h2>
+            {{ chartMode() === 'future'
+              ? 'Recent actual candles + ' + report.model.name + ' future'
+              : 'Hidden actuals against ' + report.model.name + ' backtest' }}
+          </h2>
+        </div>
+        <div class="chart-controls">
+          <div class="chart-mode-switch" role="group" aria-label="Forecast chart view">
+            <button
+              type="button"
+              [attr.aria-pressed]="chartMode() === 'future'"
+              [class.active]="chartMode() === 'future'"
+              (click)="selectChartMode('future')"
+            >Future forecast</button>
+            <button
+              type="button"
+              [attr.aria-pressed]="chartMode() === 'backtest'"
+              [class.active]="chartMode() === 'backtest'"
+              (click)="selectChartMode('backtest')"
+            >Backtest</button>
           </div>
           <div class="legend" aria-label="Chart legend">
+            @if (chartMode() === 'future') {
               <span><i class="candle"></i>Actual OHLC</span>
-          <span><i class="backtest"></i>Held-out prediction</span>
               <span><i class="future"></i>Future forecast</span>
+              @if (showFutureInterval()) {
+                <span><i class="interval future-interval"></i>Q10–Q90</span>
+              }
               <span><i class="live"></i>Live actual</span>
+            } @else {
+              <span><i class="actual-line"></i>Actual close</span>
+              <span><i class="backtest"></i>Backtest prediction</span>
+              @if (showBacktestInterval()) {
+                <span><i class="interval"></i>Q10–Q90</span>
+              }
+            }
+          </div>
         </div>
       </header>
-      <div #predictionChart class="prediction-chart" role="img" [attr.aria-label]="'DSE actual and ' + report.model.name + ' forecast chart'"></div>
+      <div class="chart-context-note">
+        <p>
+          @if (chartMode() === 'future') {
+            Uses the latest real prices. The separate historical backtest is excluded from this scale.
+          } @else {
+            The model uses the first {{ report.data.context_points | number }} bars as context, then predicts all
+            {{ report.data.holdout_points | number }} hidden bars. Compare predicted and actual closing prices directly.
+          }
+        </p>
+        <button
+          type="button"
+          class="interval-toggle"
+          [class.active]="intervalVisible()"
+          [attr.aria-pressed]="intervalVisible()"
+          (click)="toggleInterval()"
+        >{{ intervalVisible() ? 'Hide' : 'Show' }} 80% uncertainty</button>
+      </div>
+      <div
+        #predictionChart
+        id="prediction-chart"
+        class="prediction-chart"
+        role="img"
+        [attr.aria-label]="chartMode() === 'future'
+          ? 'Recent DSE actual prices and ' + report.model.name + ' future forecast chart'
+          : 'DSE hidden actual prices and ' + report.model.name + ' historical backtest chart'"
+      ></div>
       <footer>
-        <span>Context</span>
+        @if (chartMode() === 'future') {
+          <span>Recent actual</span>
+          <strong>{{ futureHistoryStart(report) | date: 'mediumDate' }} → {{ report.data.last_timestamp | date: 'mediumDate' }}</strong>
+          <span>Forecast window</span>
+          <strong>{{ report.future[0].time | date: 'mediumDate' }} → {{ report.future[report.future.length - 1].time | date: 'mediumDate' }}</strong>
+          <span>Horizon</span>
+          <strong>{{ report.future.length }} bars</strong>
+        } @else {
+          <span>Model context</span>
           <strong>{{ report.data.first_timestamp | date: 'mediumDate' }} → {{ report.history[report.data.context_points - 1].time | date: 'mediumDate' }}</strong>
-        <span>Hidden test</span>
+          <span>Line comparison</span>
           <strong>{{ report.backtest[0].time | date: 'mediumDate' }} → {{ report.data.last_timestamp | date: 'mediumDate' }}</strong>
-        <span>Next forecast</span>
-        <strong>{{ report.future.length }} bars</strong>
+          <span>Scored bars</span>
+          <strong>{{ report.backtest.length }}</strong>
+        }
       </footer>
     </section>
 

analysis-ui/src/app/timesfm-prediction.component.scss

@@ -179,8 +179,73 @@
 .legend span { display: inline-flex; align-items: center; gap: 0.35rem; }
 .legend i { width: 1.25rem; height: 0.16rem; border-radius: 1rem; background: #34d399; }
 .legend .backtest { background: var(--amber); }
+.legend .actual-line { background: #34d399; }
 .legend .future { background: var(--violet); }
 .legend .live { background: var(--cyan); }
+.legend .interval {
+  height: 0;
+  border-top: 2px dashed rgb(245 158 11 / 65%);
+  border-radius: 0;
+  background: transparent;
+}
+.legend .future-interval { border-color: rgb(167 139 250 / 70%); }
+.chart-controls { display: grid; justify-items: end; gap: 0.7rem; }
+.chart-mode-switch {
+  display: inline-grid;
+  grid-template-columns: repeat(2, 1fr);
+  padding: 0.2rem;
+  border: 1px solid #334155;
+  border-radius: 0.7rem;
+  background: #0b1220;
+}
+.chart-mode-switch button,
+.interval-toggle {
+  border: 0;
+  color: var(--muted);
+  background: transparent;
+  font: inherit;
+  font-size: 0.76rem;
+  font-weight: 700;
+  cursor: pointer;
+}
+.chart-mode-switch button {
+  padding: 0.5rem 0.75rem;
+  border-radius: 0.5rem;
+}
+.chart-mode-switch button.active {
+  color: #f8fafc;
+  background: #253146;
+  box-shadow: 0 0 0 1px rgb(148 163 184 / 12%);
+}
+.chart-mode-switch button:focus-visible,
+.interval-toggle:focus-visible {
+  outline: 2px solid var(--cyan);
+  outline-offset: 2px;
+}
+.chart-context-note {
+  display: flex;
+  min-height: 3rem;
+  align-items: center;
+  justify-content: space-between;
+  gap: 1rem;
+  padding: 0.65rem 1.25rem;
+  border-bottom: 1px solid var(--line);
+  color: var(--muted);
+  background: #0d1420;
+  font-size: 0.76rem;
+}
+.chart-context-note p { margin: 0; line-height: 1.5; }
+.interval-toggle {
+  flex: 0 0 auto;
+  padding: 0.45rem 0.7rem;
+  border: 1px solid #475569;
+  border-radius: 0.55rem;
+}
+.interval-toggle.active {
+  border-color: #7c3aed;
+  color: #ddd6fe;
+  background: rgb(124 58 237 / 14%);
+}
 .prediction-chart { width: 100%; min-height: 520px; }
 .chart-card > footer {
   display: grid;
@@ -239,7 +304,9 @@ td { color: #dbe5f3; font-size: 0.82rem; }
   .accuracy-card { flex-basis: auto; }
   .metric-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
   .chart-card > header, .table-card > header, .prediction-footer { align-items: flex-start; flex-direction: column; }
+  .chart-controls { justify-items: start; }
   .legend { justify-content: flex-start; }
+  .chart-context-note { align-items: flex-start; flex-direction: column; }
   .prediction-chart { min-height: 420px; }
   .live-comparison { grid-template-columns: 1fr; }
   .table-card > header > span { text-align: left; }

analysis-ui/src/app/timesfm-prediction.component.ts

@@ -32,6 +32,10 @@ import {
 } from './timesfm-prediction.model';
 import { TimesFmPredictionService } from './timesfm-prediction.service';
 
+type PredictionChartMode = 'future' | 'backtest';
+
+const FUTURE_HISTORY_BARS = 30;
+
 @Component({
   selector: 'app-timesfm-prediction',
   standalone: true,
@@ -51,13 +55,15 @@ export class TimesFmPredictionComponent
   );
   readonly livePrice = signal<number | null>(null);
   readonly liveMatches = signal<LiveForecastMatch[]>([]);
+  readonly chartMode = signal<PredictionChartMode>('future');
+  readonly showFutureInterval = signal(false);
+  readonly showBacktestInterval = signal(false);
   readonly latestLiveMatch = computed(() => this.liveMatches().at(-1) ?? null);
   readonly recentBacktest = computed(() => this.result()?.backtest.slice(-12) ?? []);
   readonly subscriptions: Subscription[] = [];
 
   private chart: IChartApi | null = null;
   private liveSeries: ISeriesApi<'Line'> | null = null;
-  private resizeObserver: ResizeObserver | null = null;
   private stompClient: Client | null = null;
   private chartReady = false;
 
@@ -107,7 +113,6 @@ export class TimesFmPredictionComponent
   ngOnDestroy(): void {
     this.subscriptions.forEach((subscription) => subscription.unsubscribe());
     void this.stompClient?.deactivate();
-    this.resizeObserver?.disconnect();
     this.chart?.remove();
   }
 
@@ -133,13 +138,38 @@ export class TimesFmPredictionComponent
     return point.time;
   }
 
+  selectChartMode(mode: PredictionChartMode): void {
+    if (this.chartMode() === mode) return;
+    this.chartMode.set(mode);
+    this.renderChart();
+  }
+
+  toggleInterval(): void {
+    if (this.chartMode() === 'future') {
+      this.showFutureInterval.update((visible) => !visible);
+    } else {
+      this.showBacktestInterval.update((visible) => !visible);
+    }
+    this.renderChart();
+  }
+
+  intervalVisible(): boolean {
+    return this.chartMode() === 'future'
+      ? this.showFutureInterval()
+      : this.showBacktestInterval();
+  }
+
+  futureHistoryStart(result: TimesFmPredictionResult): string {
+    return result.history.at(-FUTURE_HISTORY_BARS)?.time ?? result.data.first_timestamp;
+  }
+
   private renderChart(): void {
     const result = this.result();
     const container = this.chartContainer?.nativeElement;
     if (!result || !container || !this.chartReady) return;
 
-    this.resizeObserver?.disconnect();
     this.chart?.remove();
+    this.liveSeries = null;
     this.chart = createChart(container, {
       autoSize: true,
       height: 520,
@@ -164,15 +194,24 @@ export class TimesFmPredictionComponent
       },
     });
 
-    const candles = this.chart.addCandlestickSeries({
+    if (this.chartMode() === 'future') {
+      this.renderFutureSeries(result);
+    } else {
+      this.renderBacktestSeries(result);
+    }
+    this.chart.timeScale().fitContent();
+  }
+
+  private renderFutureSeries(result: TimesFmPredictionResult): void {
+    const candles = this.chart?.addCandlestickSeries({
       upColor: '#34d399',
       downColor: '#f87171',
       borderVisible: false,
       wickUpColor: '#34d399',
       wickDownColor: '#f87171',
     });
-    candles.setData(
-      result.history.map((point) => ({
+    candles?.setData(
+      result.history.slice(-FUTURE_HISTORY_BARS).map((point) => ({
         time: this.chartTime(point.time),
         open: point.open,
         high: point.high,
@@ -181,49 +220,7 @@ export class TimesFmPredictionComponent
       })),
     );
 
-    const predicted = this.chart.addLineSeries({
-      color: '#f59e0b',
-      lineWidth: 2,
-      title: 'Held-out prediction',
-      priceLineVisible: false,
-    });
-    predicted.setData(
-      result.backtest.map((point) => ({
-        time: this.chartTime(point.time),
-        value: point.predicted,
-      })),
-    );
-
-    const lower = this.chart.addLineSeries({
-      color: 'rgba(245, 158, 11, 0.48)',
-      lineWidth: 1,
-      lineStyle: LineStyle.Dashed,
-      title: 'Q10',
-      priceLineVisible: false,
-      lastValueVisible: false,
-    });
-    const upper = this.chart.addLineSeries({
-      color: 'rgba(245, 158, 11, 0.48)',
-      lineWidth: 1,
-      lineStyle: LineStyle.Dashed,
-      title: 'Q90',
-      priceLineVisible: false,
-      lastValueVisible: false,
-    });
-    lower.setData(
-      result.backtest.map((point) => ({
-        time: this.chartTime(point.time),
-        value: point.q10,
-      })),
-    );
-    upper.setData(
-      result.backtest.map((point) => ({
-        time: this.chartTime(point.time),
-        value: point.q90,
-      })),
-    );
-
-    const future = this.chart.addLineSeries({
+    const future = this.chart?.addLineSeries({
       color: '#a78bfa',
       lineWidth: 3,
       lineStyle: LineStyle.Dashed,
@@ -231,7 +228,7 @@ export class TimesFmPredictionComponent
       priceLineVisible: false,
     });
     const futureStart = result.history.at(-1);
-    future.setData([
+    future?.setData([
       ...(futureStart
         ? [
             {
@@ -246,16 +243,91 @@ export class TimesFmPredictionComponent
       })),
     ]);
 
-    this.liveSeries = this.chart.addLineSeries({
+    if (this.showFutureInterval()) {
+      this.addIntervalSeries(result.future, '#a78bfa');
+    }
+
+    this.liveSeries =
+      this.chart?.addLineSeries({
         color: '#22d3ee',
         lineWidth: 2,
         title: 'Live actual',
         priceLineVisible: true,
         lastValueVisible: true,
+      }) ?? null;
+    const latestMatch = this.latestLiveMatch();
+    if (latestMatch) {
+      this.liveSeries?.update({
+        time: this.chartTime(latestMatch.targetTime),
+        value: latestMatch.actual,
       });
-    this.chart.timeScale().fitContent();
-    this.resizeObserver = new ResizeObserver(() => this.chart?.timeScale().fitContent());
-    this.resizeObserver.observe(container);
+    }
+  }
+
+  private renderBacktestSeries(result: TimesFmPredictionResult): void {
+    const contextEnd = result.history[result.data.context_points - 1];
+    const anchor = contextEnd
+      ? [{ time: this.chartTime(contextEnd.time), value: contextEnd.close }]
+      : [];
+
+    const actual = this.chart?.addLineSeries({
+      color: '#34d399',
+      lineWidth: 3,
+      title: 'Actual close',
+      priceLineVisible: false,
+    });
+    actual?.setData([
+      ...anchor,
+      ...result.backtest.map((point) => ({
+        time: this.chartTime(point.time),
+        value: point.actual,
+      })),
+    ]);
+
+    const predicted = this.chart?.addLineSeries({
+      color: '#f59e0b',
+      lineWidth: 3,
+      lineStyle: LineStyle.Dashed,
+      title: 'Backtest prediction',
+      priceLineVisible: false,
+    });
+    predicted?.setData([
+      ...anchor,
+      ...result.backtest.map((point) => ({
+        time: this.chartTime(point.time),
+        value: point.predicted,
+      })),
+    ]);
+    if (this.showBacktestInterval()) {
+      this.addIntervalSeries(result.backtest, '#f59e0b');
+    }
+  }
+
+  private addIntervalSeries(
+    points: Array<{ time: string; q10: number; q90: number }>,
+    color: string,
+  ): void {
+    const intervalOptions = {
+      color,
+      lineWidth: 1 as const,
+      lineStyle: LineStyle.Dashed,
+      priceLineVisible: false,
+      lastValueVisible: false,
+    };
+    const lower = this.chart?.addLineSeries({ ...intervalOptions, title: 'Q10' });
+    const upper = this.chart?.addLineSeries({ ...intervalOptions, title: 'Q90' });
+    lower?.setData(
+      points.map((point) => ({
+        time: this.chartTime(point.time),
+        value: point.q10,
+      })),
+    );
+    upper?.setData(
+      points.map((point) => ({
+        time: this.chartTime(point.time),
+        value: point.q90,
+      })),
+    );
   }
 
   private connectLiveFeed(result: TimesFmPredictionResult): void {

dohasecuritiesstockai/timesfm_forecasting/forecaster.py

@@ -8,6 +8,7 @@ import io
 import math
 import os
 import sys
+import warnings
 from pathlib import Path
 from types import ModuleType
 from typing import Any
@@ -82,6 +83,44 @@ def _load_archived_timesfm() -> ModuleType:
     return module
 
 
+def _cuda_device_is_supported(torch: ModuleType, device_index: int = 0) -> bool:
+    """Return whether this PyTorch build contains code usable by the CUDA device."""
+
+    if not torch.cuda.is_available():
+        return False
+
+    compiled_arches = torch.cuda.get_arch_list()
+    if not compiled_arches:
+        # ROCm builds and some custom CUDA builds do not expose an architecture
+        # list. In that case availability is the best signal PyTorch provides.
+        return True
+
+    device_major, device_minor = torch.cuda.get_device_capability(device_index)
+    device_arch = device_major * 10 + device_minor
+    sm_arches: list[int] = []
+    ptx_arches: list[int] = []
+    for arch in compiled_arches:
+        kind, separator, version = arch.partition("_")
+        if not separator:
+            continue
+        numeric_version = "".join(character for character in version if character.isdigit())
+        if not numeric_version:
+            continue
+        if kind == "sm":
+            sm_arches.append(int(numeric_version))
+        elif kind == "compute":
+            ptx_arches.append(int(numeric_version))
+
+    # CUDA cubins are compatible with later minor revisions in the same major
+    # architecture. PTX can be JIT-compiled for newer CUDA architectures.
+    if any(
+        compiled // 10 == device_arch // 10 and compiled <= device_arch
+        for compiled in sm_arches
+    ):
+        return True
+    return any(compiled <= device_arch for compiled in ptx_arches)
+
+
 class TimesFMBackend:
     """One TimesFM 2.0 500M model instance configured for one prediction run."""
 
@@ -111,7 +150,24 @@ class TimesFMBackend:
             ) from exc
 
         timesfm = _load_archived_timesfm()
-        use_cuda = torch.cuda.is_available()
+        gpu_name = None
+        with warnings.catch_warnings():
+            # PyTorch emits several long compatibility warnings while querying
+            # an unsupported device. Replace them with one actionable warning.
+            warnings.simplefilter("ignore", UserWarning)
+            cuda_available = torch.cuda.is_available()
+            if cuda_available:
+                gpu_name = torch.cuda.get_device_name(0)
+            use_cuda = _cuda_device_is_supported(torch)
+            capability = torch.cuda.get_device_capability(0) if cuda_available else None
+        if cuda_available and not use_cuda:
+            device_arch = f"sm_{capability[0]}{capability[1]}" if capability else "unknown"
+            warnings.warn(
+                f"{gpu_name or 'CUDA device'} ({device_arch}) is not supported by this "
+                "PyTorch build; TimesFM is using the CPU instead.",
+                RuntimeWarning,
+                stacklevel=2,
+            )
         torch.set_float32_matmul_precision("high")
 
         desired_context = max(INPUT_PATCH, min(context_points, MODEL_CONTEXT_LIMIT))
@@ -138,7 +194,7 @@ class TimesFMBackend:
         )
         if use_cuda:
             self.device = "cuda:0"
-            self.gpu_name = torch.cuda.get_device_name(0)
+            self.gpu_name = gpu_name
         else:
             self.device = "cpu"
             self.gpu_name = None

tests/test_timesfm_forecasting.py

@@ -6,6 +6,7 @@ import numpy as np
 import pytest
 
 from dohasecuritiesstockai.timesfm_forecasting.cli import _lookback_start
+from dohasecuritiesstockai.timesfm_forecasting.forecaster import _cuda_device_is_supported
 from dohasecuritiesstockai.timesfm_forecasting.market_data import (
     MarketCandle,
     fetch_dse_candles,
@@ -48,6 +49,27 @@ class LinearForecastBackend:
         return point, quantiles, 1
 
 
+class FakeCuda:
+    def __init__(self, *, available: bool, capability=(0, 0), arches=()):
+        self.available = available
+        self.capability = capability
+        self.arches = list(arches)
+
+    def is_available(self):
+        return self.available
+
+    def get_device_capability(self, _device_index):
+        return self.capability
+
+    def get_arch_list(self):
+        return self.arches
+
+
+class FakeTorch:
+    def __init__(self, cuda):
+        self.cuda = cuda
+
+
 def _linear_candles(count: int) -> list[MarketCandle]:
     start = datetime(2024, 1, 1, tzinfo=timezone.utc)
     return [
@@ -91,6 +113,36 @@ def test_lookback_is_separate_from_candle_resolution():
     assert _lookback_start(datetime(2026, 8, 10).date(), "max") is None
 
 
+def test_timesfm_rejects_cuda_device_older_than_compiled_pytorch_arches():
+    torch = FakeTorch(
+        FakeCuda(
+            available=True,
+            capability=(6, 0),
+            arches=("sm_70", "sm_75", "sm_80", "sm_86", "sm_90"),
+        )
+    )
+
+    assert not _cuda_device_is_supported(torch)
+
+
+def test_timesfm_accepts_compatible_cuda_cubin_or_forward_compatible_ptx():
+    same_major = FakeTorch(
+        FakeCuda(available=True, capability=(8, 9), arches=("sm_80", "sm_86"))
+    )
+    forward_ptx = FakeTorch(
+        FakeCuda(available=True, capability=(9, 0), arches=("sm_86", "compute_86"))
+    )
+
+    assert _cuda_device_is_supported(same_major)
+    assert _cuda_device_is_supported(forward_ptx)
+
+
+def test_timesfm_treats_cuda_as_unavailable_when_pytorch_does():
+    torch = FakeTorch(FakeCuda(available=False))
+
+    assert not _cuda_device_is_supported(torch)
+
+
 def test_fetch_dse_candles_normalizes_envelope_and_ohlcv_arrays():
     first = int(datetime(2025, 1, 1, tzinfo=timezone.utc).timestamp())
     client = FakeDSEClient(