diff --git a/.github/workflows/daily-discovery.yml b/.github/workflows/daily-discovery.yml
new file mode 100644
index 00000000..96df31ca
--- /dev/null
+++ b/.github/workflows/daily-discovery.yml
@@ -0,0 +1,120 @@
+name: Daily Discovery
+
+on:
+ schedule:
+ # 8:30 AM ET (13:30 UTC) on weekdays
+ - cron: "30 13 * * 1-5"
+ workflow_dispatch:
+ # Manual trigger with optional overrides
+ inputs:
+ date:
+ description: "Analysis date (YYYY-MM-DD, blank = today)"
+ required: false
+ default: ""
+ provider:
+ description: "LLM provider"
+ required: false
+ default: "google"
+ type: choice
+ options:
+ - google
+ - openai
+ - anthropic
+
+env:
+ PYTHON_VERSION: "3.10"
+
+jobs:
+ discovery:
+ runs-on: ubuntu-latest
+ environment: TradingAgent
+ timeout-minutes: 30
+
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+
+ - name: Set up Python
+ uses: actions/setup-python@v5
+ with:
+ python-version: ${{ env.PYTHON_VERSION }}
+ cache: pip
+
+ - name: Install dependencies
+ run: |
+ pip install --upgrade pip
+ pip install -e .
+
+ - name: Determine analysis date
+ id: date
+ run: |
+ if [ -n "${{ github.event.inputs.date }}" ]; then
+ echo "analysis_date=${{ github.event.inputs.date }}" >> "$GITHUB_OUTPUT"
+ else
+ echo "analysis_date=$(date -u +%Y-%m-%d)" >> "$GITHUB_OUTPUT"
+ fi
+
+ - name: Run discovery pipeline
+ env:
+ # LLM keys (set whichever provider you use)
+ GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
+ OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+ ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+ # Data source keys
+ FINNHUB_API_KEY: ${{ secrets.FINNHUB_API_KEY }}
+ ALPHA_VANTAGE_API_KEY: ${{ secrets.ALPHA_VANTAGE_API_KEY }}
+ FMP_API_KEY: ${{ secrets.FMP_API_KEY }}
+ REDDIT_CLIENT_ID: ${{ secrets.REDDIT_CLIENT_ID }}
+ REDDIT_CLIENT_SECRET: ${{ secrets.REDDIT_CLIENT_SECRET }}
+ TRADIER_API_KEY: ${{ secrets.TRADIER_API_KEY }}
+ run: |
+ python scripts/run_daily_discovery.py \
+ --date "${{ steps.date.outputs.analysis_date }}" \
+ --no-update-positions
+
+ - name: Commit recommendations to repo
+ run: |
+ git config user.name "github-actions[bot]"
+ git config user.email "github-actions[bot]@users.noreply.github.com"
+
+ # Stage new/updated recommendation files
+ git add data/recommendations/ || true
+ git add results/ || true
+
+ # Only commit if there are changes
+ if git diff --cached --quiet; then
+ echo "No new recommendations to commit"
+ else
+ git commit -m "chore: daily discovery ${{ steps.date.outputs.analysis_date }}"
+ git push
+ fi
+
+ - name: Update positions
+ if: success()
+ env:
+ FINNHUB_API_KEY: ${{ secrets.FINNHUB_API_KEY }}
+ run: |
+ python scripts/update_positions.py
+
+ - name: Commit position updates
+ if: success()
+ run: |
+ git config user.name "github-actions[bot]"
+ git config user.email "github-actions[bot]@users.noreply.github.com"
+ git add data/recommendations/ || true
+ if git diff --cached --quiet; then
+ echo "No position updates"
+ else
+ git commit -m "chore: update positions ${{ steps.date.outputs.analysis_date }}"
+ git push
+ fi
+
+ - name: Upload results as artifact
+ if: always()
+ uses: actions/upload-artifact@v4
+ with:
+ name: discovery-${{ steps.date.outputs.analysis_date }}
+ path: |
+ data/recommendations/${{ steps.date.outputs.analysis_date }}*.json
+ results/discovery/${{ steps.date.outputs.analysis_date }}/
+ retention-days: 30
diff --git a/data/recommendations/performance_database.json b/data/recommendations/performance_database.json
index fe63e7b4..9556b915 100644
--- a/data/recommendations/performance_database.json
+++ b/data/recommendations/performance_database.json
@@ -1,6 +1,6 @@
{
- "last_updated": "2026-02-10 22:26:47",
- "total_recommendations": 185,
+ "last_updated": "2026-02-11 12:47:48",
+ "total_recommendations": 200,
"recommendations_by_date": {
"2026-02-10": [
{
@@ -13,10 +13,12 @@
"entry_price": 24.889999389648438,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 24.81999969482422,
- "return_pct": -0.28,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 24.15999984741211,
+ "return_pct": -2.93,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -2.79,
+ "win_1d": false
},
{
"ticker": "AVR",
@@ -28,10 +30,12 @@
"entry_price": 5.829999923706055,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 5.789999961853027,
- "return_pct": -0.69,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 5.670000076293945,
+ "return_pct": -2.74,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -2.14,
+ "win_1d": false
},
{
"ticker": "PEGA",
@@ -43,10 +47,12 @@
"entry_price": 42.525001525878906,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 43.029998779296875,
- "return_pct": 1.19,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 38.189998626708984,
+ "return_pct": -10.19,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -13.7,
+ "win_1d": false
},
{
"ticker": "BLKB",
@@ -58,10 +64,12 @@
"entry_price": 49.790000915527344,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 47.68000030517578,
- "return_pct": -4.24,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 50.150001525878906,
+ "return_pct": 0.72,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -2.73,
+ "win_1d": false
},
{
"ticker": "INMD",
@@ -73,10 +81,12 @@
"entry_price": 15.300000190734863,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 14.619999885559082,
- "return_pct": -4.44,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 14.225000381469727,
+ "return_pct": -7.03,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -6.73,
+ "win_1d": false
},
{
"ticker": "TMC",
@@ -88,10 +98,12 @@
"entry_price": 6.460000038146973,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 6.400000095367432,
- "return_pct": -0.93,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 6.545000076293945,
+ "return_pct": 1.32,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -2.24,
+ "win_1d": false
},
{
"ticker": "DDOG",
@@ -103,10 +115,12 @@
"entry_price": 131.63499450683594,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 129.6699981689453,
- "return_pct": -1.49,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 126.83999633789062,
+ "return_pct": -3.64,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -4.87,
+ "win_1d": false
},
{
"ticker": "PATH",
@@ -118,10 +132,12 @@
"entry_price": 13.074999809265137,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 12.949999809265137,
- "return_pct": -0.96,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 11.645000457763672,
+ "return_pct": -10.94,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -11.01,
+ "win_1d": false
},
{
"ticker": "PMN",
@@ -133,10 +149,12 @@
"entry_price": 13.550000190734863,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 14.289999961853027,
- "return_pct": 5.46,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 14.510000228881836,
+ "return_pct": 7.08,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": 8.12,
+ "win_1d": true
},
{
"ticker": "IGV",
@@ -148,10 +166,12 @@
"entry_price": 86.29499816894531,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 85.41000366210938,
- "return_pct": -1.03,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 82.99659729003906,
+ "return_pct": -3.82,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -4.19,
+ "win_1d": false
},
{
"ticker": "POET",
@@ -163,10 +183,12 @@
"entry_price": 6.114999771118164,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 5.840000152587891,
- "return_pct": -4.5,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 5.815000057220459,
+ "return_pct": -4.91,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -5.81,
+ "win_1d": false
},
{
"ticker": "ASTS",
@@ -178,10 +200,12 @@
"entry_price": 99.80999755859375,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 96.2699966430664,
- "return_pct": -3.55,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 97.70500183105469,
+ "return_pct": -2.11,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -4.8,
+ "win_1d": false
},
{
"ticker": "APH",
@@ -193,10 +217,12 @@
"entry_price": 145.18910217285156,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 144.13999938964844,
- "return_pct": -0.72,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 144.24000549316406,
+ "return_pct": -0.65,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -1.45,
+ "win_1d": false
},
{
"ticker": "META",
@@ -208,10 +234,12 @@
"entry_price": 671.3660278320312,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 670.719970703125,
- "return_pct": -0.1,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 671.5800170898438,
+ "return_pct": 0.03,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": -1.03,
+ "win_1d": false
},
{
"ticker": "WRB",
@@ -223,10 +251,12 @@
"entry_price": 69.02999877929688,
"discovery_date": "2026-02-10",
"status": "open",
- "current_price": 69.91999816894531,
- "return_pct": 1.29,
- "days_held": 0,
- "last_updated": "2026-02-10"
+ "current_price": 71.52999877929688,
+ "return_pct": 3.62,
+ "days_held": 1,
+ "last_updated": "2026-02-11",
+ "return_1d": 2.1,
+ "win_1d": true
}
],
"2026-02-06": [
@@ -240,10 +270,10 @@
"entry_price": 24.93000030517578,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 24.81999969482422,
- "return_pct": -0.44,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 24.15999984741211,
+ "return_pct": -3.09,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": -0.44,
"win_1d": false
},
@@ -257,10 +287,10 @@
"entry_price": 12.289999961853027,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 10.5,
- "return_pct": -14.56,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 10.395000457763672,
+ "return_pct": -15.42,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": -14.56,
"win_1d": false
},
@@ -274,10 +304,10 @@
"entry_price": 394.7300109863281,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 413.2699890136719,
- "return_pct": 4.7,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 403.8500061035156,
+ "return_pct": 2.31,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 4.7,
"win_1d": true
},
@@ -291,10 +321,10 @@
"entry_price": 410.4100036621094,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 425.2099914550781,
- "return_pct": 3.61,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 426.9999084472656,
+ "return_pct": 4.04,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 3.61,
"win_1d": true
},
@@ -308,10 +338,10 @@
"entry_price": 279.0199890136719,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 273.67999267578125,
- "return_pct": -1.91,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 276.17999267578125,
+ "return_pct": -1.02,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": -1.91,
"win_1d": false
},
@@ -325,10 +355,10 @@
"entry_price": 5.569900035858154,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 5.840000152587891,
- "return_pct": 4.85,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 5.815000057220459,
+ "return_pct": 4.4,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 4.85,
"win_1d": true
},
@@ -342,10 +372,10 @@
"entry_price": 35.709999084472656,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 40.45000076293945,
- "return_pct": 13.27,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 44.02000045776367,
+ "return_pct": 23.27,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 13.27,
"win_1d": true
},
@@ -359,10 +389,10 @@
"entry_price": 109.41999816894531,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 112.27999877929688,
- "return_pct": 2.61,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 113.41000366210938,
+ "return_pct": 3.65,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 2.61,
"win_1d": true
},
@@ -376,10 +406,10 @@
"entry_price": 220.76499938964844,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 206.5800018310547,
- "return_pct": -6.43,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 207.5,
+ "return_pct": -6.01,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": -6.43,
"win_1d": false
},
@@ -393,10 +423,10 @@
"entry_price": 271.44000244140625,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 274.07000732421875,
- "return_pct": 0.97,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 270.80999755859375,
+ "return_pct": -0.23,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 0.97,
"win_1d": true
},
@@ -410,10 +440,10 @@
"entry_price": 185.42999267578125,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 182.69000244140625,
- "return_pct": -1.48,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 186.33070373535156,
+ "return_pct": 0.49,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": -1.48,
"win_1d": false
},
@@ -427,10 +457,10 @@
"entry_price": 182.40069580078125,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 188.5399932861328,
- "return_pct": 3.37,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 191.2100067138672,
+ "return_pct": 4.83,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 3.37,
"win_1d": true
},
@@ -444,10 +474,10 @@
"entry_price": 150.97999572753906,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 148.94000244140625,
- "return_pct": -1.35,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 146.69000244140625,
+ "return_pct": -2.84,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": -1.35,
"win_1d": false
},
@@ -461,10 +491,10 @@
"entry_price": 12.345000267028809,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 12.949999809265137,
- "return_pct": 4.9,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 11.645000457763672,
+ "return_pct": -5.67,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": 4.9,
"win_1d": true
},
@@ -478,10 +508,10 @@
"entry_price": 322.0899963378906,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 318.5799865722656,
- "return_pct": -1.09,
- "days_held": 4,
- "last_updated": "2026-02-10",
+ "current_price": 310.6000061035156,
+ "return_pct": -3.57,
+ "days_held": 5,
+ "last_updated": "2026-02-11",
"return_1d": -1.09,
"win_1d": false
}
@@ -497,10 +527,10 @@
"entry_price": 718.7100219726562,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 670.719970703125,
- "return_pct": -6.68,
- "days_held": 11,
- "last_updated": "2026-02-10",
+ "current_price": 671.5800170898438,
+ "return_pct": -6.56,
+ "days_held": 12,
+ "last_updated": "2026-02-11",
"return_1d": -6.68,
"win_1d": false,
"return_7d": -6.68,
@@ -516,10 +546,10 @@
"entry_price": 56.5,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 47.54999923706055,
- "return_pct": -15.84,
- "days_held": 11,
- "last_updated": "2026-02-10",
+ "current_price": 45.494998931884766,
+ "return_pct": -19.48,
+ "days_held": 12,
+ "last_updated": "2026-02-11",
"return_1d": -15.84,
"win_1d": false,
"return_7d": -15.84,
@@ -535,10 +565,10 @@
"entry_price": 22.950000762939453,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 21.8799991607666,
- "return_pct": -4.66,
- "days_held": 11,
- "last_updated": "2026-02-10",
+ "current_price": 21.84000015258789,
+ "return_pct": -4.84,
+ "days_held": 12,
+ "last_updated": "2026-02-11",
"return_1d": -4.66,
"win_1d": false,
"return_7d": -4.66,
@@ -554,10 +584,10 @@
"entry_price": 78.58000183105469,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 86.29000091552734,
- "return_pct": 9.81,
- "days_held": 11,
- "last_updated": "2026-02-10",
+ "current_price": 85.375,
+ "return_pct": 8.65,
+ "days_held": 12,
+ "last_updated": "2026-02-11",
"return_1d": 9.81,
"win_1d": true,
"return_7d": 9.81,
@@ -573,10 +603,10 @@
"entry_price": 37.064998626708984,
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},
{
"ticker": "AAP",
@@ -2624,12 +2656,14 @@
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},
{
"ticker": "RYN",
@@ -2641,12 +2675,14 @@
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{
"ticker": "LLY",
@@ -2658,12 +2694,14 @@
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{
"ticker": "HON",
@@ -2675,12 +2713,14 @@
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},
{
"ticker": "DELL",
@@ -2692,12 +2732,14 @@
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{
"ticker": "ADBE",
@@ -2709,12 +2751,14 @@
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{
"ticker": "HPE",
@@ -2726,12 +2770,14 @@
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},
{
"ticker": "KO",
@@ -2743,12 +2789,14 @@
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{
"ticker": "ADP",
@@ -2760,12 +2808,14 @@
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{
"ticker": "BWA",
@@ -2777,12 +2827,14 @@
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{
"ticker": "ACHC",
@@ -2794,12 +2846,14 @@
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},
{
"ticker": "MCD",
@@ -2811,12 +2865,14 @@
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},
{
"ticker": "SMCI",
@@ -2828,12 +2884,14 @@
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},
{
"ticker": "EA",
@@ -2845,12 +2903,14 @@
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@@ -3359,13 +3419,240 @@
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+ ],
+ "2026-02-11": [
+ {
+ "ticker": "AVR",
+ "rank": 1,
+ "strategy_match": "insider_buying",
+ "final_score": 92,
+ "confidence": 9,
+ "reason": "AVR presents a compelling high-conviction setup with a massive $28.75M insider purchase representing roughly 5% of the market cap, signaling extreme confidence from L1 Capital. The stock is technically strong, trading above its 50 SMA with a bullish RSI of 54, and the Machine Learning model assigns it a high 45.8% win probability despite the general difficulty of predicting wins. The confluence of a breakout technical structure and significant insider backing creates a powerful catalyst for immediate upside.",
+ "entry_price": 5.659999847412109,
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+ },
+ {
+ "ticker": "INTC",
+ "rank": 2,
+ "strategy_match": "options_flow",
+ "final_score": 90,
+ "confidence": 9,
+ "reason": "Intel stands out with the highest Machine Learning win probability in the group at 47.6% (Predicted: WIN), suggesting a strong statistical edge for a bounce. Despite a recent daily dip, the stock remains in a broader uptrend above its 50 SMA, and options flow is bullish with a Put/Call ratio of 0.39. This divergence between price action and bullish flow often precedes a sharp recovery.",
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+ {
+ "ticker": "MANE",
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+ "strategy_match": "insider_buying",
+ "final_score": 88,
+ "confidence": 8,
+ "reason": "MANE shows exceptional insider conviction with over $66M in recent purchases, a massive amount relative to its $1.4B market cap. Technically, the stock is showing strong momentum, trading near its upper Bollinger Band with a bullish MACD crossover. While the RSI is slightly overbought, the sheer magnitude of insider accumulation suggests a fundamental repricing is underway.",
+ "entry_price": 40.04999923706055,
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+ {
+ "ticker": "APH",
+ "rank": 4,
+ "strategy_match": "insider_buying",
+ "final_score": 85,
+ "confidence": 8,
+ "reason": "Amphenol combines strong technical momentum with a solid 44.5% ML win probability. The stock is in a confirmed strong uptrend, trading above its 50 SMA and 20 EMA, reinforced by recent insider buying. The stochastic oscillator indicates a bullish crossover, suggesting momentum is accelerating in the short term.",
+ "entry_price": 142.9550018310547,
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+ {
+ "ticker": "WRB",
+ "rank": 5,
+ "strategy_match": "insider_buying",
+ "final_score": 84,
+ "confidence": 8,
+ "reason": "WRB offers a defensive momentum play backed by a staggering $308M in insider purchases over the last three months. The stock maintains a steady uptrend above its 50 SMA and VWAP, indicating institutional support. With a bullish positioning in options open interest, the risk/reward ratio is highly favorable for continued upside.",
+ "entry_price": 70.44999694824219,
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+ },
+ {
+ "ticker": "NVDA",
+ "rank": 6,
+ "strategy_match": "options_flow",
+ "final_score": 82,
+ "confidence": 8,
+ "reason": "NVIDIA remains the market leader in momentum, trading in a strong uptrend above its 50 and 200 SMAs. Unusual bullish options activity (P/C 0.52) and a bullish MACD crossover confirm continued investor appetite ahead of earnings. The technical setup supports a continuation move as long as it holds above the $185 support zone.",
+ "entry_price": 192.13999938964844,
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+ {
+ "ticker": "AUR",
+ "rank": 7,
+ "strategy_match": "earnings_calendar",
+ "final_score": 80,
+ "confidence": 7,
+ "reason": "Aurora faces an immediate earnings catalyst today, supported by a respectable 38.8% ML win probability. The stock is technically positioned for a breakout, trading above its 20 and 50 SMAs with rising OBV volume. The low price and high volatility (ATR 5.9%) imply a significant percentage move is likely upon the news release.",
+ "entry_price": 4.320000171661377,
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+ {
+ "ticker": "RPRX",
+ "rank": 8,
+ "strategy_match": "earnings_calendar",
+ "final_score": 78,
+ "confidence": 7,
+ "reason": "RPRX is reacting positively to its earnings (BMO), up 5% intraday with strong volume. Technicals are extremely bullish with the price above all key moving averages and the ADX signaling a very strong trend. Unusual call volume (P/C 0.083) suggests traders are positioning for the rally to extend further in the coming days.",
+ "entry_price": 44.8849983215332,
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+ },
+ {
+ "ticker": "APP",
+ "rank": 9,
+ "strategy_match": "earnings_calendar",
+ "final_score": 76,
+ "confidence": 7,
+ "reason": "AppLovin reports earnings after the close today, presenting a high-volatility event risk with massive upside potential given its 22% 5-day run-up. The ML model gives it a solid 35.5% probability, and despite being in a broader downtrend, recent short-term momentum is undeniable. This is a pure volatility play on the earnings reaction.",
+ "entry_price": 449.45001220703125,
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+ },
+ {
+ "ticker": "META",
+ "rank": 10,
+ "strategy_match": "options_flow",
+ "final_score": 75,
+ "confidence": 7,
+ "reason": "Meta displays resilience with a bullish options flow (P/C 0.49) and a 37.0% ML win probability. The stock is holding its uptrend above the 50 SMA and 20 EMA, indicating sustained institutional accumulation. It offers a balanced risk/reward profile as a mega-cap leader with continued momentum.",
+ "entry_price": 663.7750244140625,
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+ "days_held": 0,
+ "last_updated": "2026-02-11"
+ },
+ {
+ "ticker": "AAPL",
+ "rank": 11,
+ "strategy_match": "options_flow",
+ "final_score": 74,
+ "confidence": 7,
+ "reason": "Apple shows constructive bullish options flow with a P/C ratio of 0.50. Technicals are robust, with the price comfortably above the 50 SMA and RSI at 61, indicating strength without being severely overbought. It serves as a lower-volatility anchor in a momentum portfolio.",
+ "entry_price": 279.6499938964844,
+ "discovery_date": "2026-02-11",
+ "status": "open",
+ "current_price": 276.1549987792969,
+ "return_pct": -1.25,
+ "days_held": 0,
+ "last_updated": "2026-02-11"
+ },
+ {
+ "ticker": "AVGO",
+ "rank": 12,
+ "strategy_match": "options_flow",
+ "final_score": 72,
+ "confidence": 6,
+ "reason": "Broadcom is showing signs of a reversal with a bullish MACD crossover and unusual call activity. While broadly in a downtrend, the short-term indicators like the 20 EMA and VWAP are turning positive. A bounce here is supported by the 31.9% ML probability and bullish volume divergence.",
+ "entry_price": 341.8599853515625,
+ "discovery_date": "2026-02-11",
+ "status": "open",
+ "current_price": 342.5199890136719,
+ "return_pct": 0.19,
+ "days_held": 0,
+ "last_updated": "2026-02-11"
+ },
+ {
+ "ticker": "POET",
+ "rank": 13,
+ "strategy_match": "reddit_dd",
+ "final_score": 70,
+ "confidence": 6,
+ "reason": "POET is a speculative play driven by Reddit rumors of a large funding drop and a strong 41.2% ML win probability. The stock is highly volatile (ATR >10%), making it suitable for aggressive short-term trading. Bullish option volume supports the thesis of a potential speculative spike.",
+ "entry_price": 5.755000114440918,
+ "discovery_date": "2026-02-11",
+ "status": "open",
+ "current_price": 5.815000057220459,
+ "return_pct": 1.04,
+ "days_held": 0,
+ "last_updated": "2026-02-11"
+ },
+ {
+ "ticker": "PATH",
+ "rank": 14,
+ "strategy_match": "reddit_dd",
+ "final_score": 68,
+ "confidence": 5,
+ "reason": "UiPath is a contrarian squeeze candidate with high short interest (20.7%) and a decent ML win probability of 41.8%. The stock has taken a significant intraday hit (-10%), which could trigger an oversold bounce or short covering rally if retail interest persists. Risk is high, but the asymmetry is present.",
+ "entry_price": 11.651000022888184,
+ "discovery_date": "2026-02-11",
+ "status": "open",
+ "current_price": 11.645000457763672,
+ "return_pct": -0.05,
+ "days_held": 0,
+ "last_updated": "2026-02-11"
+ },
+ {
+ "ticker": "GOOGL",
+ "rank": 15,
+ "strategy_match": "options_flow",
+ "final_score": 67,
+ "confidence": 6,
+ "reason": "Alphabet is trading near its lower Bollinger Band, setting up a potential mean reversion bounce. Bullish options flow (P/C 0.55) suggests traders expect a recovery. Despite the downtrend, the technical extension to the downside often results in a snap-back rally.",
+ "entry_price": 311.7699890136719,
+ "discovery_date": "2026-02-11",
+ "status": "open",
+ "current_price": 310.42999267578125,
+ "return_pct": -0.43,
+ "days_held": 0,
+ "last_updated": "2026-02-11"
+ }
]
}
}
\ No newline at end of file
diff --git a/data/recommendations/statistics.json b/data/recommendations/statistics.json
index c758d7d1..f9c1d2f9 100644
--- a/data/recommendations/statistics.json
+++ b/data/recommendations/statistics.json
@@ -1,36 +1,36 @@
{
- "total_recommendations": 185,
+ "total_recommendations": 200,
"by_strategy": {
"momentum": {
"count": 92,
- "wins_1d": 45,
- "losses_1d": 33,
+ "wins_1d": 47,
+ "losses_1d": 45,
"wins_7d": 25,
"losses_7d": 23,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 1.0,
+ "avg_return_1d": 0.32,
"avg_return_7d": 0.71,
"avg_return_30d": 0,
- "win_rate_1d": 57.7,
+ "win_rate_1d": 51.1,
"win_rate_7d": 52.1
},
"volume_accumulation": {
"count": 2,
"wins_1d": 1,
- "losses_1d": 0,
+ "losses_1d": 1,
"wins_7d": 1,
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 19.7,
+ "avg_return_1d": 7.41,
"avg_return_7d": 19.7,
"avg_return_30d": 0,
- "win_rate_1d": 100.0,
+ "win_rate_1d": 50.0,
"win_rate_7d": 100.0
},
"insider_buying": {
- "count": 21,
+ "count": 25,
"wins_1d": 15,
"losses_1d": 6,
"wins_7d": 10,
@@ -44,7 +44,7 @@
"win_rate_7d": 66.7
},
"options_flow": {
- "count": 5,
+ "count": 11,
"wins_1d": 4,
"losses_1d": 1,
"wins_7d": 0,
@@ -57,18 +57,18 @@
"win_rate_1d": 80.0
},
"earnings_calendar": {
- "count": 17,
+ "count": 20,
"wins_1d": 6,
"losses_1d": 11,
- "wins_7d": 4,
+ "wins_7d": 5,
"losses_7d": 8,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": -0.23,
- "avg_return_7d": 0.36,
+ "avg_return_7d": 2.79,
"avg_return_30d": 0,
"win_rate_1d": 35.3,
- "win_rate_7d": 33.3
+ "win_rate_7d": 38.5
},
"contrarian_value": {
"count": 6,
@@ -102,15 +102,15 @@
"count": 10,
"wins_1d": 5,
"losses_1d": 5,
- "wins_7d": 4,
- "losses_7d": 3,
+ "wins_7d": 6,
+ "losses_7d": 4,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": 0.56,
- "avg_return_7d": 0.85,
+ "avg_return_7d": 2.15,
"avg_return_30d": 0,
"win_rate_1d": 50.0,
- "win_rate_7d": 57.1
+ "win_rate_7d": 60.0
},
"early_accumulation": {
"count": 1,
@@ -131,28 +131,28 @@
"wins_1d": 2,
"losses_1d": 5,
"wins_7d": 2,
- "losses_7d": 4,
+ "losses_7d": 5,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": -2.0,
- "avg_return_7d": -2.06,
+ "avg_return_7d": -1.94,
"avg_return_30d": 0,
"win_rate_1d": 28.6,
- "win_rate_7d": 33.3
+ "win_rate_7d": 28.6
},
"analyst_upgrade": {
"count": 8,
"wins_1d": 6,
"losses_1d": 2,
- "wins_7d": 4,
+ "wins_7d": 6,
"losses_7d": 2,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": 1.32,
- "avg_return_7d": -0.1,
+ "avg_return_7d": 0.99,
"avg_return_30d": 0,
"win_rate_1d": 75.0,
- "win_rate_7d": 66.7
+ "win_rate_7d": 75.0
},
"ipo_opportunity": {
"count": 1,
@@ -201,78 +201,95 @@
"wins_1d": 1,
"losses_1d": 1,
"wins_7d": 0,
- "losses_7d": 0,
+ "losses_7d": 2,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": -3.38,
- "avg_return_7d": 0,
+ "avg_return_7d": -3.85,
"avg_return_30d": 0,
- "win_rate_1d": 50.0
+ "win_rate_1d": 50.0,
+ "win_rate_7d": 0.0
},
"momentum_options": {
"count": 2,
"wins_1d": 1,
"losses_1d": 1,
- "wins_7d": 0,
+ "wins_7d": 2,
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": 0.93,
- "avg_return_7d": 0,
+ "avg_return_7d": 2.27,
"avg_return_30d": 0,
- "win_rate_1d": 50.0
+ "win_rate_1d": 50.0,
+ "win_rate_7d": 100.0
},
"oversold_reversal": {
"count": 1,
"wins_1d": 0,
"losses_1d": 1,
"wins_7d": 0,
- "losses_7d": 0,
+ "losses_7d": 1,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": -5.11,
- "avg_return_7d": 0,
+ "avg_return_7d": -7.41,
"avg_return_30d": 0,
- "win_rate_1d": 0.0
+ "win_rate_1d": 0.0,
+ "win_rate_7d": 0.0
},
"earnings_reversal": {
"count": 2,
"wins_1d": 1,
"losses_1d": 1,
- "wins_7d": 0,
- "losses_7d": 0,
+ "wins_7d": 1,
+ "losses_7d": 1,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": -1.47,
- "avg_return_7d": 0,
+ "avg_return_7d": -2.82,
"avg_return_30d": 0,
- "win_rate_1d": 50.0
+ "win_rate_1d": 50.0,
+ "win_rate_7d": 50.0
},
"earnings_growth": {
"count": 1,
"wins_1d": 1,
"losses_1d": 0,
"wins_7d": 0,
- "losses_7d": 0,
+ "losses_7d": 1,
"wins_30d": 0,
"losses_30d": 0,
"avg_return_1d": 1.36,
- "avg_return_7d": 0,
+ "avg_return_7d": -1.94,
"avg_return_30d": 0,
- "win_rate_1d": 100.0
+ "win_rate_1d": 100.0,
+ "win_rate_7d": 0.0
+ },
+ "reddit_dd": {
+ "count": 2,
+ "wins_1d": 0,
+ "losses_1d": 0,
+ "wins_7d": 0,
+ "losses_7d": 0,
+ "wins_30d": 0,
+ "losses_30d": 0,
+ "avg_return_1d": 0,
+ "avg_return_7d": 0,
+ "avg_return_30d": 0
}
},
"overall_1d": {
- "count": 170,
- "wins": 94,
- "avg_return": 0.26,
- "win_rate": 55.3
+ "count": 185,
+ "wins": 96,
+ "avg_return": -0.05,
+ "win_rate": 51.9
},
"overall_7d": {
- "count": 110,
- "wins": 56,
- "avg_return": -0.18,
- "win_rate": 50.9
+ "count": 125,
+ "wins": 64,
+ "avg_return": 0.13,
+ "win_rate": 51.2
},
"overall_30d": {
"count": 0,
diff --git a/scripts/run_daily_discovery.py b/scripts/run_daily_discovery.py
new file mode 100755
index 00000000..7936d3ed
--- /dev/null
+++ b/scripts/run_daily_discovery.py
@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+"""
+Daily Discovery Runner — non-interactive script for cron/launchd scheduling.
+
+Runs the full discovery pipeline (scan → filter → rank), saves recommendations,
+and updates position tracking. Designed to run before market open (~8:30 AM ET).
+
+Usage:
+ python scripts/run_daily_discovery.py # Uses defaults
+ python scripts/run_daily_discovery.py --date 2026-02-12 # Specific date
+ python scripts/run_daily_discovery.py --provider google # Override LLM provider
+
+Scheduling (macOS launchd):
+ See the companion plist at scripts/com.tradingagents.discovery.plist
+
+Scheduling (cron):
+ 30 13 * * 1-5 cd /path/to/TradingAgents && .venv/bin/python scripts/run_daily_discovery.py >> logs/discovery_cron.log 2>&1
+ (13:30 UTC = 8:30 AM ET, weekdays only)
+"""
+
+import argparse
+import json
+import os
+import sys
+from datetime import datetime
+from pathlib import Path
+
+# Ensure project root is on sys.path
+ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(ROOT))
+os.chdir(ROOT)
+
+from tradingagents.dataflows.config import set_config
+from tradingagents.default_config import DEFAULT_CONFIG
+from tradingagents.graph.discovery_graph import DiscoveryGraph
+from tradingagents.utils.logger import get_logger
+
+logger = get_logger("daily_discovery")
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description="Run daily discovery pipeline")
+ parser.add_argument(
+ "--date",
+ default=datetime.now().strftime("%Y-%m-%d"),
+ help="Analysis date (YYYY-MM-DD), defaults to today",
+ )
+ parser.add_argument(
+ "--provider",
+ default=None,
+ help="LLM provider override (openai, google, anthropic)",
+ )
+ parser.add_argument(
+ "--shallow-model",
+ default=None,
+ help="Override quick_think_llm model name",
+ )
+ parser.add_argument(
+ "--deep-model",
+ default=None,
+ help="Override deep_think_llm model name",
+ )
+ parser.add_argument(
+ "--update-positions",
+ action="store_true",
+ default=True,
+ help="Update position tracking after discovery (default: True)",
+ )
+ parser.add_argument(
+ "--no-update-positions",
+ action="store_false",
+ dest="update_positions",
+ )
+ return parser.parse_args()
+
+
+def run_discovery(args):
+ """Run the discovery pipeline with the given arguments."""
+ config = DEFAULT_CONFIG.copy()
+
+ # Apply overrides
+ if args.provider:
+ config["llm_provider"] = args.provider.lower()
+ if args.shallow_model:
+ config["quick_think_llm"] = args.shallow_model
+ if args.deep_model:
+ config["deep_think_llm"] = args.deep_model
+
+ set_config(config)
+
+ # Create results directory
+ run_timestamp = datetime.now().strftime("%H_%M_%S")
+ results_dir = Path(config["results_dir"]) / "discovery" / args.date / f"run_{run_timestamp}"
+ results_dir.mkdir(parents=True, exist_ok=True)
+ config["discovery_run_dir"] = str(results_dir)
+
+ logger.info(f"Starting daily discovery for {args.date}")
+ logger.info(
+ f"Provider: {config['llm_provider']} | "
+ f"Shallow: {config['quick_think_llm']} | "
+ f"Deep: {config['deep_think_llm']}"
+ )
+
+ # Run discovery
+ graph = DiscoveryGraph(config=config)
+ result = graph.run(trade_date=args.date)
+
+ final_ranking = result.get("final_ranking", "No ranking available")
+ logger.info(f"Discovery complete. Results saved to {results_dir}")
+
+ return result
+
+
+def update_positions():
+ """Run position updates after discovery."""
+ try:
+ from scripts.update_positions import main as update_main
+
+ logger.info("Updating position tracking...")
+ update_main()
+ except Exception as e:
+ logger.error(f"Position update failed: {e}")
+
+
+def main():
+ args = parse_args()
+
+ logger.info("=" * 60)
+ logger.info(f"DAILY DISCOVERY RUN — {datetime.now().isoformat()}")
+ logger.info("=" * 60)
+
+ try:
+ result = run_discovery(args)
+
+ if args.update_positions:
+ update_positions()
+
+ logger.info("Daily discovery completed successfully")
+
+ except Exception as e:
+ logger.error(f"Discovery failed: {e}", exc_info=True)
+ sys.exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tradingagents/dataflows/discovery/scanners/options_flow.py b/tradingagents/dataflows/discovery/scanners/options_flow.py
index 5ff3312d..67c267c4 100644
--- a/tradingagents/dataflows/discovery/scanners/options_flow.py
+++ b/tradingagents/dataflows/discovery/scanners/options_flow.py
@@ -1,6 +1,12 @@
-"""Unusual options activity scanner."""
+"""Unusual options activity scanner.
-from typing import Any, Dict, List
+Scans a ticker universe (loaded from data/tickers.txt by default) for
+unusual options volume relative to open interest. Uses ThreadPoolExecutor
+for parallel chain fetching so large universes remain practical.
+"""
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any, Dict, List, Optional
from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner
from tradingagents.dataflows.y_finance import get_option_chain, get_ticker_options
@@ -8,9 +14,30 @@ from tradingagents.utils.logger import get_logger
logger = get_logger(__name__)
+DEFAULT_TICKER_FILE = "data/tickers.txt"
+
+
+def _load_tickers_from_file(path: str) -> List[str]:
+ """Load ticker symbols from a text file (one per line, # comments allowed)."""
+ try:
+ with open(path) as f:
+ tickers = [
+ line.strip().upper()
+ for line in f
+ if line.strip() and not line.strip().startswith("#")
+ ]
+ if tickers:
+ logger.info(f"Options scanner: loaded {len(tickers)} tickers from {path}")
+ return tickers
+ except FileNotFoundError:
+ logger.warning(f"Ticker file not found: {path}")
+ except Exception as e:
+ logger.warning(f"Failed to load ticker file {path}: {e}")
+ return []
+
class OptionsFlowScanner(BaseScanner):
- """Scan for unusual options activity."""
+ """Scan for unusual options activity across a ticker universe."""
name = "options_flow"
pipeline = "edge"
@@ -20,32 +47,55 @@ class OptionsFlowScanner(BaseScanner):
self.min_volume_oi_ratio = self.scanner_config.get("unusual_volume_multiple", 2.0)
self.min_volume = self.scanner_config.get("min_volume", 1000)
self.min_premium = self.scanner_config.get("min_premium", 25000)
- self.ticker_universe = self.scanner_config.get(
- "ticker_universe", ["AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "AMD", "TSLA"]
- )
+ self.max_tickers = self.scanner_config.get("max_tickers", 150)
+ self.max_workers = self.scanner_config.get("max_workers", 8)
+
+ # Load universe: explicit list > ticker_file > default file
+ if "ticker_universe" in self.scanner_config:
+ self.ticker_universe = self.scanner_config["ticker_universe"]
+ else:
+ ticker_file = self.scanner_config.get(
+ "ticker_file",
+ config.get("tickers_file", DEFAULT_TICKER_FILE),
+ )
+ self.ticker_universe = _load_tickers_from_file(ticker_file)
+ if not self.ticker_universe:
+ logger.warning("No tickers loaded — options scanner will be empty")
def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]:
if not self.is_enabled():
return []
- logger.info("Scanning unusual options activity...")
+ universe = self.ticker_universe[: self.max_tickers]
+ logger.info(
+ f"Scanning {len(universe)} tickers for unusual options activity "
+ f"({self.max_workers} workers)..."
+ )
- candidates = []
+ candidates: List[Dict[str, Any]] = []
- for ticker in self.ticker_universe[:20]: # Limit for speed
- try:
- unusual = self._analyze_ticker_options(ticker)
- if unusual:
- candidates.append(unusual)
- if len(candidates) >= self.limit:
- break
- except Exception:
- continue
+ with ThreadPoolExecutor(max_workers=self.max_workers) as pool:
+ futures = {
+ pool.submit(self._analyze_ticker_options, ticker): ticker
+ for ticker in universe
+ }
+ for future in as_completed(futures):
+ try:
+ result = future.result()
+ if result:
+ candidates.append(result)
+ if len(candidates) >= self.limit:
+ # Cancel remaining futures
+ for f in futures:
+ f.cancel()
+ break
+ except Exception:
+ continue
logger.info(f"Found {len(candidates)} unusual options flows")
return candidates
- def _analyze_ticker_options(self, ticker: str) -> Dict[str, Any]:
+ def _analyze_ticker_options(self, ticker: str) -> Optional[Dict[str, Any]]:
try:
expirations = get_ticker_options(ticker)
if not expirations:
@@ -58,8 +108,8 @@ class OptionsFlowScanner(BaseScanner):
# Find unusual strikes
unusual_strikes = []
for _, opt in calls.iterrows():
- vol = opt.get("volume", 0)
- oi = opt.get("openInterest", 0)
+ vol = opt.get("volume", 0) or 0
+ oi = opt.get("openInterest", 0) or 0
if oi > 0 and vol > self.min_volume and (vol / oi) >= self.min_volume_oi_ratio:
unusual_strikes.append(
{"type": "call", "strike": opt["strike"], "volume": vol, "oi": oi}
@@ -78,7 +128,10 @@ class OptionsFlowScanner(BaseScanner):
return {
"ticker": ticker,
"source": self.name,
- "context": f"Unusual options: {len(unusual_strikes)} strikes, P/C={pc_ratio:.2f} ({sentiment})",
+ "context": (
+ f"Unusual options: {len(unusual_strikes)} strikes, "
+ f"P/C={pc_ratio:.2f} ({sentiment})"
+ ),
"priority": "high" if sentiment == "bullish" else "medium",
"strategy": "options_flow",
"put_call_ratio": round(pc_ratio, 2),
diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py
index 40138055..6b394412 100644
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -137,28 +137,10 @@ DEFAULT_CONFIG = {
"unusual_volume_multiple": 2.0, # Min volume/OI ratio for unusual activity
"min_premium": 25000, # Minimum premium ($) to filter noise
"min_volume": 1000, # Minimum option volume to consider
- "ticker_universe": [
- "AAPL",
- "MSFT",
- "GOOGL",
- "AMZN",
- "META",
- "NVDA",
- "AMD",
- "TSLA",
- "TSMC",
- "ASML",
- "AVGO",
- "ORCL",
- "CRM",
- "ADBE",
- "INTC",
- "QCOM",
- "TXN",
- "AMAT",
- "LRCX",
- "KLAC",
- ], # Top 20 liquid options
+ # ticker_file: path to ticker list (defaults to tickers_file from root config)
+ # ticker_universe: explicit list overrides ticker_file if set
+ "max_tickers": 150, # Max tickers to scan (from start of file)
+ "max_workers": 8, # Parallel option chain fetch threads
},
"congress_trades": {
"enabled": False,
@@ -178,7 +160,7 @@ DEFAULT_CONFIG = {
"compression_min_volume_ratio": 1.3, # Min volume ratio for compression
},
"market_movers": {
- "enabled": True,
+ "enabled": False,
"pipeline": "momentum",
"limit": 10,
},
@@ -195,7 +177,7 @@ DEFAULT_CONFIG = {
"news_lookback_days": 0.5, # Days of news history to analyze
},
"analyst_upgrade": {
- "enabled": False,
+ "enabled": True,
"pipeline": "news",
"limit": 5,
"lookback_days": 1, # Days to look back for rating changes
@@ -221,7 +203,7 @@ DEFAULT_CONFIG = {
"min_market_cap": 0, # Minimum market cap in billions (0 = no filter)
},
"short_squeeze": {
- "enabled": False,
+ "enabled": True,
"pipeline": "events",
"limit": 5,
"min_short_interest_pct": 15.0, # Minimum short interest %
diff --git a/tradingagents/ui/dashboard.py b/tradingagents/ui/dashboard.py
index ffedf114..bf6d88ea 100644
--- a/tradingagents/ui/dashboard.py
+++ b/tradingagents/ui/dashboard.py
@@ -119,10 +119,16 @@ def route_page(page):
"Config": pages.settings,
}
module = page_map.get(page)
- if module:
- module.render()
- else:
+ if module is None:
st.error(f"Unknown page: {page}")
+ return
+ try:
+ module.render()
+ except Exception as exc:
+ st.error(f"Error rendering {page}: {exc}")
+ import traceback
+
+ st.code(traceback.format_exc(), language="python")
def main():
diff --git a/tradingagents/ui/pages/__init__.py b/tradingagents/ui/pages/__init__.py
index 4ab695db..22a16b20 100644
--- a/tradingagents/ui/pages/__init__.py
+++ b/tradingagents/ui/pages/__init__.py
@@ -5,29 +5,38 @@ This package contains all page modules that can be rendered in the dashboard.
Each module should have a render() function that displays the page content.
"""
+import logging
+
+_logger = logging.getLogger(__name__)
+
try:
from tradingagents.ui.pages import home
-except ImportError:
+except Exception as _e:
+ _logger.error("Failed to import home page: %s", _e, exc_info=True)
home = None
try:
from tradingagents.ui.pages import todays_picks
-except ImportError:
+except Exception as _e:
+ _logger.error("Failed to import todays_picks page: %s", _e, exc_info=True)
todays_picks = None
try:
from tradingagents.ui.pages import portfolio
-except ImportError:
+except Exception as _e:
+ _logger.error("Failed to import portfolio page: %s", _e, exc_info=True)
portfolio = None
try:
from tradingagents.ui.pages import performance
-except ImportError:
+except Exception as _e:
+ _logger.error("Failed to import performance page: %s", _e, exc_info=True)
performance = None
try:
from tradingagents.ui.pages import settings
-except ImportError:
+except Exception as _e:
+ _logger.error("Failed to import settings page: %s", _e, exc_info=True)
settings = None
diff --git a/tradingagents/ui/pages/performance.py b/tradingagents/ui/pages/performance.py
index 130d5bc0..9467cdbd 100644
--- a/tradingagents/ui/pages/performance.py
+++ b/tradingagents/ui/pages/performance.py
@@ -2,16 +2,21 @@
Performance analytics page — strategy comparison and win/loss analysis.
Shows strategy scatter plot with themed Plotly charts, per-strategy
-breakdown table, and win rate distribution.
+breakdown table, win rate distribution, and full recommendation history.
"""
+import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
-from tradingagents.ui.theme import COLORS, get_plotly_template, page_header
-from tradingagents.ui.utils import load_statistics, load_strategy_metrics
+from tradingagents.ui.theme import COLORS, get_plotly_template, page_header, pnl_color
+from tradingagents.ui.utils import (
+ load_performance_database,
+ load_statistics,
+ load_strategy_metrics,
+)
def render() -> None:
@@ -35,8 +40,19 @@ def render() -> None:
# ---- Summary KPIs ----
total_trades = df["Count"].sum()
- avg_wr = (df["Win Rate"] * df["Count"]).sum() / total_trades if total_trades > 0 else 0
- avg_ret = (df["Avg Return"] * df["Count"]).sum() / total_trades if total_trades > 0 else 0
+ # Weighted averages only over strategies that have evaluated data (non-NaN)
+ eval_df = df.dropna(subset=["Win Rate", "Avg Return"])
+ eval_trades = eval_df["Count"].sum()
+ avg_wr = (
+ (eval_df["Win Rate"] * eval_df["Count"]).sum() / eval_trades
+ if eval_trades > 0
+ else 0
+ )
+ avg_ret = (
+ (eval_df["Avg Return"] * eval_df["Count"]).sum() / eval_trades
+ if eval_trades > 0
+ else 0
+ )
n_strategies = len(df)
cols = st.columns(4)
@@ -107,16 +123,16 @@ def render() -> None:
unsafe_allow_html=True,
)
- df_sorted = df.sort_values("Win Rate", ascending=True)
- colors = [COLORS["green"] if wr >= 50 else COLORS["red"] for wr in df_sorted["Win Rate"]]
+ df_bar = df.dropna(subset=["Win Rate"]).sort_values("Win Rate", ascending=True)
+ colors = [COLORS["green"] if wr >= 50 else COLORS["red"] for wr in df_bar["Win Rate"]]
fig_bar = go.Figure(
go.Bar(
- x=df_sorted["Win Rate"],
- y=df_sorted["Strategy"],
+ x=df_bar["Win Rate"],
+ y=df_bar["Strategy"],
orientation="h",
marker_color=colors,
- text=[f"{wr:.0f}%" for wr in df_sorted["Win Rate"]],
+ text=[f"{wr:.0f}%" for wr in df_bar["Win Rate"]],
textposition="auto",
textfont=dict(family="JetBrains Mono", size=11, color=COLORS["text_primary"]),
)
@@ -169,18 +185,334 @@ def render() -> None:
{
"Strategy": strat_name,
"Count": data.get("count", 0),
- "Win Rate 1d": (
- f"{data.get('win_rate_1d', 0):.0f}%" if "win_rate_1d" in data else "N/A"
+ "Win Rate 1d": data.get("win_rate_1d") if "win_rate_1d" in data else None,
+ "Avg Ret 1d": data.get("avg_return_1d") if "avg_return_1d" in data else None,
+ "W/L 1d": (
+ f"{data.get('wins_1d', 0)}W/{data.get('losses_1d', 0)}L"
+ if data.get("wins_1d", 0) + data.get("losses_1d", 0) > 0
+ else "—"
),
- "Win Rate 7d": (
- f"{data.get('win_rate_7d', 0):.0f}%" if "win_rate_7d" in data else "N/A"
+ "Win Rate 7d": data.get("win_rate_7d") if "win_rate_7d" in data else None,
+ "Avg Ret 7d": data.get("avg_return_7d") if "avg_return_7d" in data else None,
+ "W/L 7d": (
+ f"{data.get('wins_7d', 0)}W/{data.get('losses_7d', 0)}L"
+ if data.get("wins_7d", 0) + data.get("losses_7d", 0) > 0
+ else "—"
),
- "Wins 1d": data.get("wins_1d", 0),
- "Losses 1d": data.get("losses_1d", 0),
- "Wins 7d": data.get("wins_7d", 0),
- "Losses 7d": data.get("losses_7d", 0),
}
)
if rows:
- st.dataframe(pd.DataFrame(rows), width="stretch", hide_index=True)
+ period_df = pd.DataFrame(rows).sort_values("Count", ascending=False)
+ st.dataframe(
+ period_df,
+ width="stretch",
+ hide_index=True,
+ column_config={
+ "Count": st.column_config.NumberColumn(format="%d"),
+ "Win Rate 1d": st.column_config.NumberColumn(format="%.1f%%"),
+ "Avg Ret 1d": st.column_config.NumberColumn(format="%+.2f%%"),
+ "Win Rate 7d": st.column_config.NumberColumn(format="%.1f%%"),
+ "Avg Ret 7d": st.column_config.NumberColumn(format="%+.2f%%"),
+ },
+ )
+
+ # ---- Recommendation History ----
+ _render_recommendation_history(template)
+
+
+# ---------------------------------------------------------------------------
+# Recommendation history helpers
+# ---------------------------------------------------------------------------
+
+
+def _return_cell(val) -> str:
+ """Format a return value as a colored HTML span."""
+ if val is None or (isinstance(val, float) and np.isnan(val)):
+ return '—'.format(c=COLORS["text_muted"])
+ color = pnl_color(val)
+ return f'{val:+.2f}%'
+
+
+def _win_dot(val) -> str:
+ """Green/red dot for win/loss boolean."""
+ if val is None or (isinstance(val, float) and np.isnan(val)):
+ return ""
+ color = COLORS["green"] if val else COLORS["red"]
+ return f'●'
+
+
+def _render_recommendation_history(template: dict) -> None:
+ """Full recommendation history with charts and filterable table."""
+ recs = load_performance_database()
+ if not recs:
+ return
+
+ st.markdown("", unsafe_allow_html=True)
+ st.markdown(
+ 'Recommendation History '
+ '// all picks
',
+ unsafe_allow_html=True,
+ )
+
+ # Build DataFrame
+ hist_df = pd.DataFrame(recs)
+
+ # Ensure numeric types
+ for col in ["return_1d", "return_7d", "return_30d", "return_pct", "final_score", "confidence"]:
+ if col in hist_df.columns:
+ hist_df[col] = pd.to_numeric(hist_df[col], errors="coerce")
+
+ # Parse dates
+ if "discovery_date" in hist_df.columns:
+ hist_df["discovery_date"] = pd.to_datetime(hist_df["discovery_date"], errors="coerce")
+
+ # ---- Filters row ----
+ filter_cols = st.columns([2, 2, 2, 1])
+
+ with filter_cols[0]:
+ strategies = sorted(hist_df["strategy_match"].dropna().unique())
+ selected_strategies = st.multiselect(
+ "Strategy",
+ strategies,
+ default=[],
+ placeholder="All strategies",
+ )
+
+ with filter_cols[1]:
+ dates = hist_df["discovery_date"].dropna().sort_values()
+ min_date = dates.min().date() if len(dates) > 0 else None
+ max_date = dates.max().date() if len(dates) > 0 else None
+ if min_date and max_date:
+ date_range = st.date_input(
+ "Date range",
+ value=(min_date, max_date),
+ min_value=min_date,
+ max_value=max_date,
+ )
+ else:
+ date_range = None
+
+ with filter_cols[2]:
+ outcome_filter = st.selectbox(
+ "Outcome (7d)",
+ ["All", "Winners", "Losers", "Pending"],
+ index=0,
+ )
+
+ with filter_cols[3]:
+ sort_by = st.selectbox("Sort", ["Date", "Return 1d", "Return 7d", "Score"], index=0)
+
+ # Apply filters
+ mask = pd.Series(True, index=hist_df.index)
+
+ if selected_strategies:
+ mask &= hist_df["strategy_match"].isin(selected_strategies)
+
+ if date_range and len(date_range) == 2:
+ start, end = pd.Timestamp(date_range[0]), pd.Timestamp(date_range[1])
+ mask &= (hist_df["discovery_date"] >= start) & (hist_df["discovery_date"] <= end)
+
+ if outcome_filter == "Winners":
+ mask &= hist_df.get("win_7d", pd.Series(dtype=bool)) == True # noqa: E712
+ elif outcome_filter == "Losers":
+ mask &= hist_df.get("win_7d", pd.Series(dtype=bool)) == False # noqa: E712
+ elif outcome_filter == "Pending":
+ mask &= hist_df.get("return_7d").isna() if "return_7d" in hist_df.columns else True
+
+ filtered = hist_df[mask].copy()
+
+ # Sort
+ sort_map = {
+ "Date": ("discovery_date", False),
+ "Return 1d": ("return_1d", False),
+ "Return 7d": ("return_7d", False),
+ "Score": ("final_score", False),
+ }
+ sort_col, sort_asc = sort_map.get(sort_by, ("discovery_date", False))
+ if sort_col in filtered.columns:
+ filtered = filtered.sort_values(sort_col, ascending=sort_asc, na_position="last")
+
+ st.caption(f"Showing {len(filtered)} of {len(hist_df)} recommendations")
+
+ # ---- Two-column charts ----
+ if len(filtered) > 0:
+ left_ch, right_ch = st.columns(2)
+
+ with left_ch:
+ st.markdown(
+ 'Return Distribution '
+ '// 1d vs 7d
',
+ unsafe_allow_html=True,
+ )
+ _render_return_distribution(filtered, template)
+
+ with right_ch:
+ st.markdown(
+ 'Cumulative P/L by Date '
+ '// equity curve
',
+ unsafe_allow_html=True,
+ )
+ _render_cumulative_pnl(filtered, template)
+
+ # ---- Full history table ----
+ st.markdown("", unsafe_allow_html=True)
+ st.markdown(
+ 'All Picks '
+ '// detail table
',
+ unsafe_allow_html=True,
+ )
+ _render_history_table(filtered)
+
+
+def _render_return_distribution(df: pd.DataFrame, template: dict) -> None:
+ """Box plot comparing 1d vs 7d return distributions."""
+ ret_data = []
+ for _, row in df.iterrows():
+ if pd.notna(row.get("return_1d")):
+ ret_data.append({"Period": "1-Day", "Return (%)": row["return_1d"]})
+ if pd.notna(row.get("return_7d")):
+ ret_data.append({"Period": "7-Day", "Return (%)": row["return_7d"]})
+
+ if not ret_data:
+ st.info("No return data available for the selected filters.")
+ return
+
+ ret_df = pd.DataFrame(ret_data)
+
+ fig = go.Figure()
+ for period, color in [("1-Day", COLORS["blue"]), ("7-Day", COLORS["cyan"])]:
+ subset = ret_df[ret_df["Period"] == period]["Return (%)"]
+ if len(subset) == 0:
+ continue
+ fig.add_trace(
+ go.Box(
+ y=subset,
+ name=period,
+ marker_color=color,
+ boxmean=True,
+ jitter=0.3,
+ pointpos=-1.5,
+ boxpoints="outliers",
+ )
+ )
+
+ fig.add_hline(y=0, line_dash="dot", line_color=COLORS["text_muted"], opacity=0.4)
+ fig.update_layout(
+ **template,
+ height=350,
+ showlegend=True,
+ legend=dict(orientation="h", y=1.02, x=0.5, xanchor="center"),
+ yaxis_title="Return (%)",
+ )
+ st.plotly_chart(fig, width="stretch")
+
+
+def _render_cumulative_pnl(df: pd.DataFrame, template: dict) -> None:
+ """Cumulative average return by discovery date (equity curve style)."""
+ if "discovery_date" not in df.columns:
+ st.info("No date data available.")
+ return
+
+ # Use 7d return where available, fall back to 1d
+ df_dated = df.dropna(subset=["discovery_date"]).copy()
+ df_dated["best_return"] = df_dated["return_7d"].fillna(df_dated.get("return_1d", 0))
+ df_dated = df_dated.dropna(subset=["best_return"])
+
+ if len(df_dated) == 0:
+ st.info("No return data available for equity curve.")
+ return
+
+ # Group by date, get mean return per day
+ daily = (
+ df_dated.groupby("discovery_date")["best_return"]
+ .mean()
+ .reset_index()
+ .sort_values("discovery_date")
+ )
+ daily.columns = ["Date", "Avg Return"]
+ daily["Cumulative"] = daily["Avg Return"].cumsum()
+
+ # Color based on cumulative being positive/negative
+ colors = [COLORS["green"] if v >= 0 else COLORS["red"] for v in daily["Cumulative"]]
+
+ fig = go.Figure()
+ fig.add_trace(
+ go.Scatter(
+ x=daily["Date"],
+ y=daily["Cumulative"],
+ mode="lines+markers",
+ line=dict(color=COLORS["green"], width=2),
+ marker=dict(color=colors, size=7, line=dict(color=COLORS["bg_card"], width=1)),
+ fill="tozeroy",
+ fillcolor="rgba(34, 197, 94, 0.08)",
+ hovertemplate="Date: %{x|%b %d}
Cumulative: %{y:+.2f}%",
+ )
+ )
+
+ fig.add_hline(y=0, line_dash="dot", line_color=COLORS["text_muted"], opacity=0.4)
+ fig.update_layout(
+ **template,
+ height=350,
+ showlegend=False,
+ yaxis_title="Cumulative Avg Return (%)",
+ xaxis_title="",
+ )
+ st.plotly_chart(fig, width="stretch")
+
+
+def _render_history_table(df: pd.DataFrame) -> None:
+ """Render the full recommendation history as a styled dataframe."""
+ if len(df) == 0:
+ st.info("No recommendations match the selected filters.")
+ return
+
+ # Build display dataframe with readable columns
+ display_rows = []
+ for _, row in df.iterrows():
+ disc_date = row.get("discovery_date")
+ date_str = disc_date.strftime("%Y-%m-%d") if pd.notna(disc_date) else "—"
+
+ display_rows.append(
+ {
+ "Date": date_str,
+ "Ticker": row.get("ticker", "—"),
+ "#": int(row["rank"]) if pd.notna(row.get("rank")) else 0,
+ "Strategy": row.get("strategy_match", "—"),
+ "Score": row.get("final_score"),
+ "Conf": int(row["confidence"]) if pd.notna(row.get("confidence")) else None,
+ "Entry $": row.get("entry_price"),
+ "Now $": row.get("current_price"),
+ "Ret 1d %": row.get("return_1d"),
+ "Ret 7d %": row.get("return_7d"),
+ "Ret 30d %": row.get("return_30d") if "return_30d" in row.index else None,
+ "Current %": row.get("return_pct"),
+ "Days": int(row["days_held"]) if pd.notna(row.get("days_held")) else None,
+ "Status": row.get("status", "—"),
+ }
+ )
+
+ table_df = pd.DataFrame(display_rows)
+
+ st.dataframe(
+ table_df,
+ width="stretch",
+ hide_index=True,
+ height=min(len(table_df) * 35 + 38, 600),
+ column_config={
+ "Date": st.column_config.TextColumn(width="small"),
+ "Ticker": st.column_config.TextColumn(width="small"),
+ "#": st.column_config.NumberColumn(format="%d", width="small"),
+ "Strategy": st.column_config.TextColumn(width="medium"),
+ "Score": st.column_config.NumberColumn(format="%.0f", width="small"),
+ "Conf": st.column_config.NumberColumn(format="%d/10", width="small"),
+ "Entry $": st.column_config.NumberColumn(format="$%.2f"),
+ "Now $": st.column_config.NumberColumn(format="$%.2f"),
+ "Ret 1d %": st.column_config.NumberColumn(format="%+.2f%%"),
+ "Ret 7d %": st.column_config.NumberColumn(format="%+.2f%%"),
+ "Ret 30d %": st.column_config.NumberColumn(format="%+.2f%%"),
+ "Current %": st.column_config.NumberColumn(format="%+.2f%%"),
+ "Days": st.column_config.NumberColumn(format="%d"),
+ "Status": st.column_config.TextColumn(width="small"),
+ },
+ )
diff --git a/tradingagents/ui/utils.py b/tradingagents/ui/utils.py
index f2042053..7fcde1c9 100644
--- a/tradingagents/ui/utils.py
+++ b/tradingagents/ui/utils.py
@@ -196,42 +196,73 @@ def load_performance_database() -> List[Dict[str, Any]]:
return []
+_STRATEGY_ALIASES: Dict[str, str] = {
+ "momentum": "momentum",
+ "momentum/hype": "momentum",
+ "momentum/hype / short squeeze": "momentum",
+ "insider play": "insider_buying",
+ "insider_buying": "insider_buying",
+ "earnings play": "earnings_play",
+ "earnings_play": "earnings_play",
+ "earnings_calendar": "earnings_calendar",
+ "news catalyst": "news_catalyst",
+ "news_catalyst": "news_catalyst",
+ "volume accumulation": "volume_accumulation",
+ "volume_accumulation": "volume_accumulation",
+ "contrarian value": "contrarian_value",
+ "contrarian_value": "contrarian_value",
+}
+
+
+def normalize_strategy(raw: str) -> str:
+ """Map strategy name variants to a canonical lowercase form."""
+ key = raw.strip().lower()
+ return _STRATEGY_ALIASES.get(key, key)
+
+
def load_strategy_metrics() -> List[Dict[str, Any]]:
"""
Build per-strategy metrics from the performance database if available.
Falls back to statistics.json when performance database is missing.
+
+ Normalizes strategy names so variants like 'Momentum', 'momentum',
+ and 'Momentum/Hype' all merge into a single bucket. Counts ALL
+ recommendations per strategy; win rate and avg return are computed
+ from the subset that has 7-day return data.
"""
recs = load_performance_database()
if recs:
metrics: Dict[str, Dict[str, float]] = {}
for rec in recs:
- strategy = rec.get("strategy_match", "unknown")
+ strategy = normalize_strategy(rec.get("strategy_match", "unknown"))
if strategy not in metrics:
metrics[strategy] = {
- "count": 0,
+ "total": 0,
+ "evaluated": 0,
"wins": 0,
"sum_return": 0.0,
}
- if "return_7d" in rec:
- metrics[strategy]["count"] += 1
+ metrics[strategy]["total"] += 1
+
+ if "return_7d" in rec and rec["return_7d"] is not None:
+ metrics[strategy]["evaluated"] += 1
metrics[strategy]["sum_return"] += float(rec.get("return_7d", 0.0) or 0.0)
if rec.get("win_7d"):
metrics[strategy]["wins"] += 1
results = []
for strategy, data in metrics.items():
- count = int(data["count"])
- if count == 0:
- continue
- win_rate = round((data["wins"] / count) * 100, 1)
- avg_return = round(data["sum_return"] / count, 2)
+ total = int(data["total"])
+ evaluated = int(data["evaluated"])
+ win_rate = round((data["wins"] / evaluated) * 100, 1) if evaluated > 0 else None
+ avg_return = round(data["sum_return"] / evaluated, 2) if evaluated > 0 else None
results.append(
{
"Strategy": strategy,
"Win Rate": win_rate,
"Avg Return": avg_return,
- "Count": count,
+ "Count": total,
}
)
return results
@@ -242,10 +273,10 @@ def load_strategy_metrics() -> List[Dict[str, Any]]:
for strategy, data in by_strategy.items():
win_rate = data.get("win_rate_7d") or data.get("win_rate", 0)
avg_return = data.get("avg_return_7d", 0)
- count = data.get("wins_7d", 0) + data.get("losses_7d", 0)
+ count = data.get("count", data.get("wins_7d", 0) + data.get("losses_7d", 0))
results.append(
{
- "Strategy": strategy,
+ "Strategy": normalize_strategy(strategy),
"Win Rate": win_rate,
"Avg Return": avg_return,
"Count": count,