diff --git a/data/recommendations/performance_database.json b/data/recommendations/performance_database.json
index 1565c9cc..fe63e7b4 100644
--- a/data/recommendations/performance_database.json
+++ b/data/recommendations/performance_database.json
@@ -1,7 +1,234 @@
{
- "last_updated": "2026-02-10 08:41:33",
- "total_recommendations": 170,
+ "last_updated": "2026-02-10 22:26:47",
+ "total_recommendations": 185,
"recommendations_by_date": {
+ "2026-02-10": [
+ {
+ "ticker": "GME",
+ "rank": 1,
+ "strategy_match": "momentum",
+ "final_score": 45,
+ "confidence": 10,
+ "reason": "GameStop leads the list with a high Quantitative Score of 45 and a robust ML Win Probability of 52.6%. The setup is primed for a squeeze with 16.1% short interest and significant recent insider buying, notably a $21 million purchase by CEO Ryan Cohen. Options sentiment is strongly bullish with a Put/Call ratio of 0.248, suggesting traders are positioning for upside. Technically, the stock is in an uptrend above its 200 SMA, and the convergence of retail momentum and insider conviction offers an asymmetric risk/reward profile.",
+ "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"
+ },
+ {
+ "ticker": "AVR",
+ "rank": 2,
+ "strategy_match": "momentum",
+ "final_score": 40,
+ "confidence": 8,
+ "reason": "Anteris Technologies aligns with the high-win-rate 'Insider Buying' strategy, featuring $28.75 million in recent insider purchases. The stock carries a high Quantitative Score of 40 and shows highly unusual bullish options activity with a Put/Call ratio of just 0.007. Trading above its 50 SMA, the stock displays technical resilience despite recent volatility. The combination of heavy institutional accumulation and aggressive options betting supports a strong short-term bounce thesis.",
+ "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"
+ },
+ {
+ "ticker": "PEGA",
+ "rank": 3,
+ "strategy_match": "momentum",
+ "final_score": 30,
+ "confidence": 9,
+ "reason": "Pegasystems is a compelling earnings play with an ML Win Probability of 48.4% and an exceptionally strong ADX trend reading of 67.1. With earnings scheduled for today, the stock's oversold Stochastic reading suggests potential for a sharp mean-reversion rally if results exceed depressed expectations. While the longer-term trend is down, the immediate catalyst and high win probability make this a viable tactical trade. Volatility is elevated, allowing for the targeted >5% return within the 7-day window.",
+ "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"
+ },
+ {
+ "ticker": "BLKB",
+ "rank": 4,
+ "strategy_match": "momentum",
+ "final_score": 30,
+ "confidence": 9,
+ "reason": "Blackbaud presents a deep value/reversion opportunity heading into earnings today, backed by an ML Win Probability of 48.4%. The stock is significantly oversold with an RSI of 29.5, yet shows bullish divergence in On-Balance Volume, indicating underlying accumulation. Options volume heavily favors calls (P/C 0.446), suggesting market participants anticipate a post-earnings recovery. The trade targets a snapback from extreme oversold conditions triggered by the earnings event.",
+ "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"
+ },
+ {
+ "ticker": "INMD",
+ "rank": 5,
+ "strategy_match": "momentum",
+ "final_score": 15,
+ "confidence": 8,
+ "reason": "InMode enters its earnings release today with a supportive technical structure, including a Golden Cross (50 SMA above 200 SMA) and a price holding above the 20 EMA. The ML model predicts a win with 47.2% probability, and the company maintains a healthy financial position with a Current Ratio of 9.75. Despite a recent pullback, the alignment of earnings volatility and a confirmed uptrend creates a favorable setup for a momentum breakout. Risk is managed by the stock's strong balance sheet.",
+ "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"
+ },
+ {
+ "ticker": "TMC",
+ "rank": 6,
+ "strategy_match": "momentum",
+ "final_score": 35,
+ "confidence": 8,
+ "reason": "TMC is a high-volatility momentum play driven by retail sentiment and Reddit due diligence, supported by a 45.5% ML Win Probability. The stock has a high short interest of 10.7% and bullish options flow (P/C ratio 0.208), creating the conditions for a potential squeeze. Technicals show a bullish Stochastic crossover, and the high ATR of 12.3% ensures sufficient volatility to hit profit targets quickly. The catalyst is continued speculative interest and short covering.",
+ "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"
+ },
+ {
+ "ticker": "DDOG",
+ "rank": 7,
+ "strategy_match": "volume_accumulation",
+ "final_score": 30,
+ "confidence": 8,
+ "reason": "Datadog is flagged for Volume Accumulation, a strategy with a historical 100% win rate, coinciding with its earnings release today. The ML Win Probability is solid at 43.6%, and the stock exhibits a bullish divergence in On-Balance Volume despite recent price weakness. Options sentiment is constructive with more call volume than puts. This setup suggests 'smart money' positioning ahead of the binary earnings event, offering a strong risk/reward for a reversal.",
+ "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"
+ },
+ {
+ "ticker": "PATH",
+ "rank": 8,
+ "strategy_match": "momentum",
+ "final_score": 35,
+ "confidence": 7,
+ "reason": "UiPath is a prime short squeeze candidate with 15.6% short interest and highly unusual bullish options activity (Put/Call ratio of 0.111). The ML model predicts a 43.9% win probability, and Reddit due diligence is fueling retail interest. While the stock is in a downtrend, the rising On-Balance Volume indicates accumulation. The trade thesis relies on a rapid repricing event driven by options gamma exposure and short covering.",
+ "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"
+ },
+ {
+ "ticker": "PMN",
+ "rank": 9,
+ "strategy_match": "momentum",
+ "final_score": 20,
+ "confidence": 7,
+ "reason": "ProMIS Neurosciences benefits from significant recent insider buying totaling over $11 million, a strong signal of internal confidence. The ML model assigns a 43.8% probability of a win, and the stock is trading above its 20 EMA, indicating short-term strength. With extremely high volatility (ATR >11%), the stock is capable of making the required >5% move rapidly. The investment case is built on following insider conviction in a high-beta biotech asset.",
+ "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"
+ },
+ {
+ "ticker": "IGV",
+ "rank": 10,
+ "strategy_match": "momentum",
+ "final_score": 25,
+ "confidence": 7,
+ "reason": "IGV offers a diversified way to play the potential rebound in the software sector, backed by a 42.3% ML Win Probability. The ETF is currently oversold with a Stochastic reading below 20, often a precursor to a technical bounce. Options flows are bullish, and the strong trend strength (ADX 63.9) suggests the sector remains active. This trade captures the broader momentum recovery thesis with lower single-stock risk.",
+ "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"
+ },
+ {
+ "ticker": "POET",
+ "rank": 11,
+ "strategy_match": "momentum",
+ "final_score": 35,
+ "confidence": 7,
+ "reason": "POET Technologies is a high-volatility momentum play (ATR >10%) with a 41.1% ML Win Probability. The stock is the subject of Reddit due diligence and shows a bullish options Put/Call ratio of 0.224. Despite a downtrend, a recent bullish stochastic crossover signals potential for a relief rally. The thesis rests on speculative retail interest and high volatility driving a short-term price spike.",
+ "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"
+ },
+ {
+ "ticker": "ASTS",
+ "rank": 12,
+ "strategy_match": "momentum",
+ "final_score": 35,
+ "confidence": 7,
+ "reason": "AST SpaceMobile remains a favorite among momentum traders with 18.5% short interest and a strong long-term uptrend. The stock is trading well above major moving averages, and the ML model gives it a 40.5% chance of success. Options volume is call-heavy, supporting a bullish outlook. The trade targets a continuation of the volatility-driven uptrend, aided by potential short covering.",
+ "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"
+ },
+ {
+ "ticker": "APH",
+ "rank": 13,
+ "strategy_match": "momentum",
+ "final_score": 20,
+ "confidence": 7,
+ "reason": "Amphenol represents a high-quality momentum play, confirmed by insider buying and a strong technical uptrend. The stock is trading above both its 50 and 200 SMAs and recently flashed a Golden Cross signal. With an ML Win Probability of 41.1%, it offers a favorable balance of probability and trend stability. The catalyst is the continued institutional support indicated by price action relative to VWAP.",
+ "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"
+ },
+ {
+ "ticker": "META",
+ "rank": 14,
+ "strategy_match": "momentum",
+ "final_score": 10,
+ "confidence": 6,
+ "reason": "Meta Platforms is predicted to WIN by the ML model (39.6%) and maintains a solid technical uptrend, holding above its 20 EMA and VWAP. Despite some insider selling, the fundamental backdrop of revenue growth and profitability remains a tailwind. Options positioning is constructive, and the stock is not overbought. This trade is a bet on large-cap tech resilience and continued momentum.",
+ "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"
+ },
+ {
+ "ticker": "WRB",
+ "rank": 15,
+ "strategy_match": "momentum",
+ "final_score": 25,
+ "confidence": 6,
+ "reason": "W. R. Berkley makes the list primarily due to massive insider buying totaling over $308 million, aligning with a historically high-win-rate strategy. The stock is in a technical uptrend and trading above its 20 EMA. While the ML prediction is neutral, the sheer scale of insider conviction provides a strong floor and potential upside catalyst. The trade follows the 'smart money' signal in a stable insurance play.",
+ "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"
+ }
+ ],
"2026-02-06": [
{
"ticker": "GME",
@@ -13,11 +240,11 @@
"entry_price": 24.93000030517578,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 24.924999237060547,
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+ "current_price": 24.81999969482422,
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"days_held": 4,
"last_updated": "2026-02-10",
- "return_1d": -0.02,
+ "return_1d": -0.44,
"win_1d": false
},
{
@@ -30,11 +257,11 @@
"entry_price": 12.289999961853027,
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"status": "open",
- "current_price": 12.0,
- "return_pct": -2.36,
+ "current_price": 10.5,
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"last_updated": "2026-02-10",
- "return_1d": -2.36,
+ "return_1d": -14.56,
"win_1d": false
},
{
@@ -47,11 +274,11 @@
"entry_price": 394.7300109863281,
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"status": "open",
- "current_price": 420.3800048828125,
- "return_pct": 6.5,
+ "current_price": 413.2699890136719,
+ "return_pct": 4.7,
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"last_updated": "2026-02-10",
- "return_1d": 6.5,
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"win_1d": true
},
{
@@ -64,11 +291,11 @@
"entry_price": 410.4100036621094,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 423.2450866699219,
- "return_pct": 3.13,
+ "current_price": 425.2099914550781,
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"last_updated": "2026-02-10",
- "return_1d": 3.13,
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"win_1d": true
},
{
@@ -81,11 +308,11 @@
"entry_price": 279.0199890136719,
"discovery_date": "2026-02-06",
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- "current_price": 273.3949890136719,
- "return_pct": -2.02,
+ "current_price": 273.67999267578125,
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"last_updated": "2026-02-10",
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"win_1d": false
},
{
@@ -98,11 +325,11 @@
"entry_price": 5.569900035858154,
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"status": "open",
- "current_price": 6.114999771118164,
- "return_pct": 9.79,
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+ "return_1d": 4.85,
"win_1d": true
},
{
@@ -115,11 +342,11 @@
"entry_price": 35.709999084472656,
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"status": "open",
- "current_price": 37.689998626708984,
- "return_pct": 5.54,
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"last_updated": "2026-02-10",
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+ "return_1d": 13.27,
"win_1d": true
},
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@@ -132,11 +359,11 @@
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- "return_pct": 2.63,
+ "current_price": 112.27999877929688,
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"win_1d": true
},
{
@@ -149,11 +376,11 @@
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"discovery_date": "2026-02-06",
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- "current_price": 208.31500244140625,
- "return_pct": -5.64,
+ "current_price": 206.5800018310547,
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{
@@ -166,11 +393,11 @@
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- "return_pct": 1.69,
+ "current_price": 274.07000732421875,
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"win_1d": true
},
{
@@ -183,11 +410,11 @@
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"discovery_date": "2026-02-06",
"status": "open",
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"last_updated": "2026-02-10",
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+ "return_1d": -1.48,
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},
{
@@ -200,11 +427,11 @@
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+ "current_price": 188.5399932861328,
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"last_updated": "2026-02-10",
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"win_1d": true
},
{
@@ -217,12 +444,12 @@
"entry_price": 150.97999572753906,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 152.57000732421875,
- "return_pct": 1.05,
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"last_updated": "2026-02-10",
- "return_1d": 1.05,
- "win_1d": true
+ "return_1d": -1.35,
+ "win_1d": false
},
{
"ticker": "PATH",
@@ -234,11 +461,11 @@
"entry_price": 12.345000267028809,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 13.055000305175781,
- "return_pct": 5.75,
+ "current_price": 12.949999809265137,
+ "return_pct": 4.9,
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"last_updated": "2026-02-10",
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+ "return_1d": 4.9,
"win_1d": true
},
{
@@ -251,11 +478,11 @@
"entry_price": 322.0899963378906,
"discovery_date": "2026-02-06",
"status": "open",
- "current_price": 317.9800109863281,
- "return_pct": -1.28,
+ "current_price": 318.5799865722656,
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"last_updated": "2026-02-10",
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+ "return_1d": -1.09,
"win_1d": false
}
],
@@ -270,13 +497,13 @@
"entry_price": 718.7100219726562,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 670.8400268554688,
- "return_pct": -6.66,
+ "current_price": 670.719970703125,
+ "return_pct": -6.68,
"days_held": 11,
"last_updated": "2026-02-10",
- "return_1d": -6.66,
+ "return_1d": -6.68,
"win_1d": false,
- "return_7d": -6.66,
+ "return_7d": -6.68,
"win_7d": false
},
{
@@ -289,13 +516,13 @@
"entry_price": 56.5,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 47.79499816894531,
- "return_pct": -15.41,
+ "current_price": 47.54999923706055,
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"last_updated": "2026-02-10",
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"win_1d": false,
- "return_7d": -15.41,
+ "return_7d": -15.84,
"win_7d": false
},
{
@@ -308,13 +535,13 @@
"entry_price": 22.950000762939453,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 22.770000457763672,
- "return_pct": -0.78,
+ "current_price": 21.8799991607666,
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"last_updated": "2026-02-10",
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+ "return_7d": -4.66,
"win_7d": false
},
{
@@ -327,13 +554,13 @@
"entry_price": 78.58000183105469,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 87.50350189208984,
- "return_pct": 11.36,
+ "current_price": 86.29000091552734,
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"last_updated": "2026-02-10",
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"win_1d": true,
- "return_7d": 11.36,
+ "return_7d": 9.81,
"win_7d": true
},
{
@@ -346,13 +573,13 @@
"entry_price": 37.064998626708984,
"discovery_date": "2026-01-30",
"status": "open",
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- "return_pct": 12.48,
+ "current_price": 41.61000061035156,
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"last_updated": "2026-02-10",
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"win_1d": true,
- "return_7d": 12.48,
+ "return_7d": 12.26,
"win_7d": true
},
{
@@ -365,13 +592,13 @@
"entry_price": 192.41000366210938,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 189.24000549316406,
- "return_pct": -1.65,
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"win_1d": false,
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+ "return_7d": -2.01,
"win_7d": false
},
{
@@ -384,13 +611,13 @@
"entry_price": 5.989999771118164,
"discovery_date": "2026-01-30",
"status": "open",
- "current_price": 7.074999809265137,
- "return_pct": 18.11,
+ "current_price": 6.840000152587891,
+ "return_pct": 14.19,
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{
"ticker": "RTX",
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{
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{
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{
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{
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{
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{
"ticker": "MCD",
@@ -2584,11 +2811,11 @@
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},
{
@@ -2601,11 +2828,11 @@
"entry_price": 32.88159942626953,
"discovery_date": "2026-02-04",
"status": "open",
- "current_price": 34.150001525878906,
- "return_pct": 3.86,
+ "current_price": 33.33000183105469,
+ "return_pct": 1.36,
"days_held": 6,
"last_updated": "2026-02-10",
- "return_1d": 3.86,
+ "return_1d": 1.36,
"win_1d": true
},
{
@@ -2618,11 +2845,11 @@
"entry_price": 198.91000366210938,
"discovery_date": "2026-02-04",
"status": "open",
- "current_price": 202.75,
- "return_pct": 1.93,
+ "current_price": 202.5800018310547,
+ "return_pct": 1.85,
"days_held": 6,
"last_updated": "2026-02-10",
- "return_1d": 1.93,
+ "return_1d": 1.85,
"win_1d": true
}
],
@@ -2637,11 +2864,11 @@
"entry_price": 24.639999389648438,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 24.950000762939453,
- "return_pct": 1.26,
+ "current_price": 24.81999969482422,
+ "return_pct": 0.73,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 1.26,
+ "return_1d": 0.73,
"win_1d": true
},
{
@@ -2654,11 +2881,11 @@
"entry_price": 8.699999809265137,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 9.010000228881836,
- "return_pct": 3.56,
+ "current_price": 8.75,
+ "return_pct": 0.57,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 3.56,
+ "return_1d": 0.57,
"win_1d": true
},
{
@@ -2671,12 +2898,12 @@
"entry_price": 194.02999877929688,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 196.1999969482422,
- "return_pct": 1.12,
+ "current_price": 193.4499969482422,
+ "return_pct": -0.3,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 1.12,
- "win_1d": true
+ "return_1d": -0.3,
+ "win_1d": false
},
{
"ticker": "WRB",
@@ -2688,12 +2915,12 @@
"entry_price": 69.25,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 69.04000091552734,
- "return_pct": -0.3,
+ "current_price": 69.91999816894531,
+ "return_pct": 0.97,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": -0.3,
- "win_1d": false
+ "return_1d": 0.97,
+ "win_1d": true
},
{
"ticker": "TDOC",
@@ -2705,12 +2932,12 @@
"entry_price": 4.980000019073486,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 5.034999847412109,
- "return_pct": 1.1,
+ "current_price": 4.849999904632568,
+ "return_pct": -2.61,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 1.1,
- "win_1d": true
+ "return_1d": -2.61,
+ "win_1d": false
},
{
"ticker": "CZR",
@@ -2722,11 +2949,11 @@
"entry_price": 20.649999618530273,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 21.05500030517578,
- "return_pct": 1.96,
+ "current_price": 20.75,
+ "return_pct": 0.48,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 1.96,
+ "return_1d": 0.48,
"win_1d": true
},
{
@@ -2739,12 +2966,12 @@
"entry_price": 2.549999952316284,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 2.619999885559082,
- "return_pct": 2.75,
+ "current_price": 2.5299999713897705,
+ "return_pct": -0.78,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 2.75,
- "win_1d": true
+ "return_1d": -0.78,
+ "win_1d": false
},
{
"ticker": "UWMC",
@@ -2756,11 +2983,11 @@
"entry_price": 4.630000114440918,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 4.855000019073486,
- "return_pct": 4.86,
+ "current_price": 4.840000152587891,
+ "return_pct": 4.54,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 4.86,
+ "return_1d": 4.54,
"win_1d": true
},
{
@@ -2773,12 +3000,12 @@
"entry_price": 7.909999847412109,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 7.945000171661377,
- "return_pct": 0.44,
+ "current_price": 7.809999942779541,
+ "return_pct": -1.26,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 0.44,
- "win_1d": true
+ "return_1d": -1.26,
+ "win_1d": false
},
{
"ticker": "PMN",
@@ -2790,11 +3017,11 @@
"entry_price": 13.050000190734863,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 13.550000190734863,
- "return_pct": 3.83,
+ "current_price": 14.289999961853027,
+ "return_pct": 9.5,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 3.83,
+ "return_1d": 9.5,
"win_1d": true
},
{
@@ -2807,11 +3034,11 @@
"entry_price": 4.349999904632568,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 4.25,
- "return_pct": -2.3,
+ "current_price": 3.9600000381469727,
+ "return_pct": -8.97,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": -2.3,
+ "return_1d": -8.97,
"win_1d": false
},
{
@@ -2824,11 +3051,11 @@
"entry_price": 102.12000274658203,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 99.86000061035156,
- "return_pct": -2.21,
+ "current_price": 96.2699966430664,
+ "return_pct": -5.73,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": -2.21,
+ "return_1d": -5.73,
"win_1d": false
},
{
@@ -2841,11 +3068,11 @@
"entry_price": 6.210000038146973,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 6.119999885559082,
- "return_pct": -1.45,
+ "current_price": 5.840000152587891,
+ "return_pct": -5.96,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": -1.45,
+ "return_1d": -5.96,
"win_1d": false
},
{
@@ -2858,11 +3085,11 @@
"entry_price": 43.779998779296875,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 45.625,
- "return_pct": 4.21,
+ "current_price": 44.880001068115234,
+ "return_pct": 2.51,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 4.21,
+ "return_1d": 2.51,
"win_1d": true
},
{
@@ -2875,11 +3102,11 @@
"entry_price": 5.610000133514404,
"discovery_date": "2026-02-09",
"status": "open",
- "current_price": 5.849999904632568,
- "return_pct": 4.28,
+ "current_price": 5.789999961853027,
+ "return_pct": 3.21,
"days_held": 1,
"last_updated": "2026-02-10",
- "return_1d": 4.28,
+ "return_1d": 3.21,
"win_1d": true
}
],
@@ -2894,11 +3121,11 @@
"entry_price": 24.690000534057617,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 24.950000762939453,
- "return_pct": 1.05,
+ "current_price": 24.81999969482422,
+ "return_pct": 0.53,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 1.05,
+ "return_1d": 0.53,
"win_1d": true
},
{
@@ -2911,11 +3138,11 @@
"entry_price": 44.369998931884766,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 47.92499923706055,
- "return_pct": 8.01,
+ "current_price": 48.0,
+ "return_pct": 8.18,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 8.01,
+ "return_1d": 8.18,
"win_1d": true
},
{
@@ -2928,11 +3155,11 @@
"entry_price": 30.059999465942383,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 31.510000228881836,
- "return_pct": 4.82,
+ "current_price": 31.600000381469727,
+ "return_pct": 5.12,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 4.82,
+ "return_1d": 5.12,
"win_1d": true
},
{
@@ -2945,11 +3172,11 @@
"entry_price": 104.41000366210938,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 110.5999984741211,
- "return_pct": 5.93,
+ "current_price": 109.6500015258789,
+ "return_pct": 5.02,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 5.93,
+ "return_1d": 5.02,
"win_1d": true
},
{
@@ -2962,11 +3189,11 @@
"entry_price": 103.5,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 104.08000183105469,
- "return_pct": 0.56,
+ "current_price": 104.0,
+ "return_pct": 0.48,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 0.56,
+ "return_1d": 0.48,
"win_1d": true
},
{
@@ -2979,11 +3206,11 @@
"entry_price": 42.36000061035156,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 39.80500030517578,
- "return_pct": -6.03,
+ "current_price": 39.90999984741211,
+ "return_pct": -5.78,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": -6.03,
+ "return_1d": -5.78,
"win_1d": false
},
{
@@ -2996,11 +3223,11 @@
"entry_price": 406.70001220703125,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 413.5,
- "return_pct": 1.67,
+ "current_price": 412.6000061035156,
+ "return_pct": 1.45,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 1.67,
+ "return_1d": 1.45,
"win_1d": true
},
{
@@ -3013,11 +3240,11 @@
"entry_price": 397.2099914550781,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 423.6000061035156,
- "return_pct": 6.64,
+ "current_price": 425.2099914550781,
+ "return_pct": 7.05,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 6.64,
+ "return_1d": 7.05,
"win_1d": true
},
{
@@ -3030,12 +3257,12 @@
"entry_price": 37.81999969482422,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 37.81999969482422,
- "return_pct": 0.0,
+ "current_price": 37.970001220703125,
+ "return_pct": 0.4,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 0.0,
- "win_1d": false
+ "return_1d": 0.4,
+ "win_1d": true
},
{
"ticker": "STE",
@@ -3047,11 +3274,11 @@
"entry_price": 243.80999755859375,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 247.11000061035156,
- "return_pct": 1.35,
+ "current_price": 244.6699981689453,
+ "return_pct": 0.35,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 1.35,
+ "return_1d": 0.35,
"win_1d": true
},
{
@@ -3064,11 +3291,11 @@
"entry_price": 147.47999572753906,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 152.57000732421875,
- "return_pct": 3.45,
+ "current_price": 148.94000244140625,
+ "return_pct": 0.99,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 3.45,
+ "return_1d": 0.99,
"win_1d": true
},
{
@@ -3081,11 +3308,11 @@
"entry_price": 37.15999984741211,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 37.689998626708984,
- "return_pct": 1.43,
+ "current_price": 40.45000076293945,
+ "return_pct": 8.85,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 1.43,
+ "return_1d": 8.85,
"win_1d": true
},
{
@@ -3098,11 +3325,11 @@
"entry_price": 148.5399932861328,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 165.77000427246094,
- "return_pct": 11.6,
+ "current_price": 166.0,
+ "return_pct": 11.75,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 11.6,
+ "return_1d": 11.75,
"win_1d": true
},
{
@@ -3115,11 +3342,11 @@
"entry_price": 4.690000057220459,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 4.820000171661377,
- "return_pct": 2.77,
+ "current_price": 4.699999809265137,
+ "return_pct": 0.21,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 2.77,
+ "return_1d": 0.21,
"win_1d": true
},
{
@@ -3132,11 +3359,11 @@
"entry_price": 187.77999877929688,
"discovery_date": "2026-02-05",
"status": "open",
- "current_price": 199.55999755859375,
- "return_pct": 6.27,
+ "current_price": 198.5,
+ "return_pct": 5.71,
"days_held": 5,
"last_updated": "2026-02-10",
- "return_1d": 6.27,
+ "return_1d": 5.71,
"win_1d": true
}
]
diff --git a/data/recommendations/statistics.json b/data/recommendations/statistics.json
index 705e9b10..c758d7d1 100644
--- a/data/recommendations/statistics.json
+++ b/data/recommendations/statistics.json
@@ -1,33 +1,47 @@
{
- "total_recommendations": 170,
+ "total_recommendations": 185,
"by_strategy": {
"momentum": {
- "count": 35,
- "wins_1d": 29,
- "losses_1d": 6,
- "wins_7d": 5,
+ "count": 92,
+ "wins_1d": 45,
+ "losses_1d": 33,
+ "wins_7d": 25,
+ "losses_7d": 23,
+ "wins_30d": 0,
+ "losses_30d": 0,
+ "avg_return_1d": 1.0,
+ "avg_return_7d": 0.71,
+ "avg_return_30d": 0,
+ "win_rate_1d": 57.7,
+ "win_rate_7d": 52.1
+ },
+ "volume_accumulation": {
+ "count": 2,
+ "wins_1d": 1,
+ "losses_1d": 0,
+ "wins_7d": 1,
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": 19.7,
+ "avg_return_7d": 19.7,
"avg_return_30d": 0,
- "win_rate_1d": 82.9,
+ "win_rate_1d": 100.0,
"win_rate_7d": 100.0
},
"insider_buying": {
- "count": 8,
- "wins_1d": 6,
- "losses_1d": 2,
- "wins_7d": 2,
- "losses_7d": 0,
+ "count": 21,
+ "wins_1d": 15,
+ "losses_1d": 6,
+ "wins_7d": 10,
+ "losses_7d": 5,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": 0.84,
+ "avg_return_7d": 0.18,
"avg_return_30d": 0,
- "win_rate_1d": 75.0,
- "win_rate_7d": 100.0
+ "win_rate_1d": 71.4,
+ "win_rate_7d": 66.7
},
"options_flow": {
"count": 5,
@@ -37,53 +51,26 @@
"losses_7d": 0,
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"losses_30d": 0,
- "avg_return_1d": 0,
+ "avg_return_1d": 3.09,
"avg_return_7d": 0,
"avg_return_30d": 0,
"win_rate_1d": 80.0
},
"earnings_calendar": {
- "count": 4,
- "wins_1d": 1,
- "losses_1d": 3,
- "wins_7d": 0,
- "losses_7d": 0,
+ "count": 17,
+ "wins_1d": 6,
+ "losses_1d": 11,
+ "wins_7d": 4,
+ "losses_7d": 8,
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"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": -0.23,
+ "avg_return_7d": 0.36,
"avg_return_30d": 0,
- "win_rate_1d": 25.0
+ "win_rate_1d": 35.3,
+ "win_rate_7d": 33.3
},
- "Momentum": {
- "count": 40,
- "wins_1d": 18,
- "losses_1d": 22,
- "wins_7d": 18,
- "losses_7d": 22,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 45.0,
- "win_rate_7d": 45.0
- },
- "Insider Play": {
- "count": 13,
- "wins_1d": 7,
- "losses_1d": 6,
- "wins_7d": 7,
- "losses_7d": 6,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 53.8,
- "win_rate_7d": 53.8
- },
- "Contrarian Value": {
+ "contrarian_value": {
"count": 6,
"wins_1d": 3,
"losses_1d": 3,
@@ -91,67 +78,39 @@
"losses_7d": 3,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": -4.91,
+ "avg_return_7d": -4.91,
"avg_return_30d": 0,
"win_rate_1d": 50.0,
"win_rate_7d": 50.0
},
- "Earnings Play": {
+ "news_catalyst": {
"count": 3,
- "wins_1d": 1,
- "losses_1d": 2,
- "wins_7d": 1,
- "losses_7d": 2,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 33.3,
- "win_rate_7d": 33.3
- },
- "News Catalyst": {
- "count": 1,
"wins_1d": 0,
- "losses_1d": 1,
+ "losses_1d": 3,
"wins_7d": 0,
- "losses_7d": 1,
+ "losses_7d": 3,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": -13.55,
+ "avg_return_7d": -13.55,
"avg_return_30d": 0,
"win_rate_1d": 0.0,
"win_rate_7d": 0.0
},
- "Volume Accumulation": {
- "count": 1,
- "wins_1d": 1,
- "losses_1d": 0,
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- "losses_7d": 0,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 100.0,
- "win_rate_7d": 100.0
- },
"short_squeeze": {
- "count": 7,
- "wins_1d": 4,
- "losses_1d": 3,
- "wins_7d": 2,
- "losses_7d": 2,
+ "count": 10,
+ "wins_1d": 5,
+ "losses_1d": 5,
+ "wins_7d": 4,
+ "losses_7d": 3,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": 0.56,
+ "avg_return_7d": 0.85,
"avg_return_30d": 0,
- "win_rate_1d": 57.1,
- "win_rate_7d": 50.0
+ "win_rate_1d": 50.0,
+ "win_rate_7d": 57.1
},
"early_accumulation": {
"count": 1,
@@ -161,8 +120,8 @@
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": 20.41,
+ "avg_return_7d": 20.41,
"avg_return_30d": 0,
"win_rate_1d": 100.0,
"win_rate_7d": 100.0
@@ -175,26 +134,12 @@
"losses_7d": 4,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": -2.0,
+ "avg_return_7d": -2.06,
"avg_return_30d": 0,
"win_rate_1d": 28.6,
"win_rate_7d": 33.3
},
- "earnings_play": {
- "count": 10,
- "wins_1d": 4,
- "losses_1d": 6,
- "wins_7d": 3,
- "losses_7d": 6,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 40.0,
- "win_rate_7d": 33.3
- },
"analyst_upgrade": {
"count": 8,
"wins_1d": 6,
@@ -203,8 +148,8 @@
"losses_7d": 2,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": 1.32,
+ "avg_return_7d": -0.1,
"avg_return_30d": 0,
"win_rate_1d": 75.0,
"win_rate_7d": 66.7
@@ -217,8 +162,8 @@
"losses_7d": 1,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": -19.2,
+ "avg_return_7d": -19.2,
"avg_return_30d": 0,
"win_rate_1d": 0.0,
"win_rate_7d": 0.0
@@ -231,26 +176,12 @@
"losses_7d": 2,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": -8.9,
+ "avg_return_7d": -8.9,
"avg_return_30d": 0,
"win_rate_1d": 0.0,
"win_rate_7d": 0.0
},
- "news_catalyst": {
- "count": 2,
- "wins_1d": 1,
- "losses_1d": 1,
- "wins_7d": 1,
- "losses_7d": 1,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 50.0,
- "win_rate_7d": 50.0
- },
"undiscovered_dd": {
"count": 2,
"wins_1d": 2,
@@ -259,36 +190,8 @@
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 100.0,
- "win_rate_7d": 100.0
- },
- "Momentum/Hype": {
- "count": 3,
- "wins_1d": 3,
- "losses_1d": 0,
- "wins_7d": 3,
- "losses_7d": 0,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- "win_rate_1d": 100.0,
- "win_rate_7d": 100.0
- },
- "Momentum/Hype / Short Squeeze": {
- "count": 3,
- "wins_1d": 3,
- "losses_1d": 0,
- "wins_7d": 3,
- "losses_7d": 0,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_1d": 0,
- "avg_return_7d": 0,
+ "avg_return_1d": 6.44,
+ "avg_return_7d": 6.44,
"avg_return_30d": 0,
"win_rate_1d": 100.0,
"win_rate_7d": 100.0
@@ -301,7 +204,7 @@
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
+ "avg_return_1d": -3.38,
"avg_return_7d": 0,
"avg_return_30d": 0,
"win_rate_1d": 50.0
@@ -314,7 +217,7 @@
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
+ "avg_return_1d": 0.93,
"avg_return_7d": 0,
"avg_return_30d": 0,
"win_rate_1d": 50.0
@@ -327,7 +230,7 @@
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
+ "avg_return_1d": -5.11,
"avg_return_7d": 0,
"avg_return_30d": 0,
"win_rate_1d": 0.0
@@ -340,7 +243,7 @@
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
+ "avg_return_1d": -1.47,
"avg_return_7d": 0,
"avg_return_30d": 0,
"win_rate_1d": 50.0
@@ -353,7 +256,7 @@
"losses_7d": 0,
"wins_30d": 0,
"losses_30d": 0,
- "avg_return_1d": 0,
+ "avg_return_1d": 1.36,
"avg_return_7d": 0,
"avg_return_30d": 0,
"win_rate_1d": 100.0
@@ -361,15 +264,15 @@
},
"overall_1d": {
"count": 170,
- "wins": 100,
- "avg_return": 0.95,
- "win_rate": 58.8
+ "wins": 94,
+ "avg_return": 0.26,
+ "win_rate": 55.3
},
"overall_7d": {
"count": 110,
- "wins": 58,
- "avg_return": 0.45,
- "win_rate": 52.7
+ "wins": 56,
+ "avg_return": -0.18,
+ "win_rate": 50.9
},
"overall_30d": {
"count": 0,
diff --git a/scripts/build_historical_memories.py b/scripts/build_historical_memories.py
index e91b6e49..d1eb40e0 100644
--- a/scripts/build_historical_memories.py
+++ b/scripts/build_historical_memories.py
@@ -27,11 +27,13 @@ logger = get_logger(__name__)
def main():
- logger.info("""
+ logger.info(
+ """
╔══════════════════════════════════════════════════════════════╗
║ TradingAgents - Historical Memory Builder ║
╚══════════════════════════════════════════════════════════════╝
- """)
+ """
+ )
# Configuration
tickers = [
diff --git a/scripts/build_strategy_specific_memories.py b/scripts/build_strategy_specific_memories.py
index 2849367c..d91dd050 100644
--- a/scripts/build_strategy_specific_memories.py
+++ b/scripts/build_strategy_specific_memories.py
@@ -89,7 +89,8 @@ def build_strategy_memories(strategy_name: str, config: dict):
strategy = STRATEGIES[strategy_name]
- logger.info(f"""
+ logger.info(
+ f"""
╔══════════════════════════════════════════════════════════════╗
║ Building Memories: {strategy_name.upper().replace('_', ' ')}
╚══════════════════════════════════════════════════════════════╝
@@ -98,7 +99,8 @@ Strategy: {strategy['description']}
Lookforward: {strategy['lookforward_days']} days
Sampling: Every {strategy['interval_days']} days
Tickers: {', '.join(strategy['tickers'])}
- """)
+ """
+ )
# Date range - last 2 years
end_date = datetime.now()
@@ -157,7 +159,8 @@ Tickers: {', '.join(strategy['tickers'])}
def main():
- logger.info("""
+ logger.info(
+ """
╔══════════════════════════════════════════════════════════════╗
║ TradingAgents - Strategy-Specific Memory Builder ║
╚══════════════════════════════════════════════════════════════╝
@@ -168,7 +171,8 @@ This script builds optimized memories for different trading styles:
2. Swing Trading - 7-day returns, weekly samples
3. Position Trading - 30-day returns, monthly samples
4. Long-term - 90-day returns, quarterly samples
- """)
+ """
+ )
logger.info("Available strategies:")
for i, (name, config) in enumerate(STRATEGIES.items(), 1):
@@ -216,11 +220,13 @@ This script builds optimized memories for different trading styles:
logger.info("\n" + "=" * 70)
logger.info("\n💡 TIP: To use a specific strategy's memories, update your config:")
- logger.info("""
+ logger.info(
+ """
config = DEFAULT_CONFIG.copy()
config["memory_dir"] = "data/memories/swing_trading" # or your strategy
config["load_historical_memories"] = True
- """)
+ """
+ )
if __name__ == "__main__":
diff --git a/scripts/track_recommendation_performance.py b/scripts/track_recommendation_performance.py
index 098faf27..dcf349f1 100644
--- a/scripts/track_recommendation_performance.py
+++ b/scripts/track_recommendation_performance.py
@@ -29,30 +29,73 @@ logger = get_logger(__name__)
def load_recommendations() -> List[Dict[str, Any]]:
- """Load all historical recommendations from the recommendations directory."""
+ """Load all historical recommendations, preferring the performance database.
+
+ The performance database preserves accumulated return data (return_1d,
+ return_7d, win_1d, etc.) across runs. Raw date files are only used to
+ pick up new recommendations not yet in the database.
+ """
recommendations_dir = "data/recommendations"
if not os.path.exists(recommendations_dir):
logger.warning(f"No recommendations directory found at {recommendations_dir}")
return []
- all_recs = []
- pattern = os.path.join(recommendations_dir, "*.json")
+ # Step 1: Load existing accumulated data from the performance database
+ existing: Dict[str, Dict[str, Any]] = {}
+ db_path = os.path.join(recommendations_dir, "performance_database.json")
+ if os.path.exists(db_path):
+ try:
+ with open(db_path, "r") as f:
+ db = json.load(f)
+ for recs in db.get("recommendations_by_date", {}).values():
+ if isinstance(recs, list):
+ for rec in recs:
+ key = f"{rec.get('ticker')}|{rec.get('discovery_date')}"
+ existing[key] = rec
+ logger.info(f"Loaded {len(existing)} records from performance database")
+ except Exception as e:
+ logger.error(f"Error loading performance database: {e}")
- for filepath in glob.glob(pattern):
+ # Step 2: Scan raw date files for any new recommendations
+ new_count = 0
+ for filepath in glob.glob(os.path.join(recommendations_dir, "*.json")):
+ basename = os.path.basename(filepath)
+ if basename in ("performance_database.json", "statistics.json"):
+ continue
try:
with open(filepath, "r") as f:
data = json.load(f)
- # Each file contains recommendations from one discovery run
- recs = data.get("recommendations", [])
- run_date = data.get("date", os.path.basename(filepath).replace(".json", ""))
-
- for rec in recs:
- rec["discovery_date"] = run_date
- all_recs.append(rec)
+ recs = data.get("recommendations", [])
+ run_date = data.get("date", basename.replace(".json", ""))
+ for rec in recs:
+ rec["discovery_date"] = run_date
+ key = f"{rec.get('ticker')}|{run_date}"
+ if key not in existing:
+ existing[key] = rec
+ new_count += 1
except Exception as e:
logger.error(f"Error loading {filepath}: {e}")
- return all_recs
+ if new_count:
+ logger.info(f"Merged {new_count} new recommendations from raw files")
+
+ return list(existing.values())
+
+
+def _parse_price(raw) -> float | None:
+ """Extract a numeric price from get_stock_price output.
+
+ The function may return a float directly or a markdown string like
+ "**Current Price**: $123.45". Handle both cases.
+ """
+ if raw is None:
+ return None
+ if isinstance(raw, (int, float)):
+ return float(raw)
+ import re
+
+ m = re.search(r"\$([0-9,.]+)", str(raw))
+ return float(m.group(1).replace(",", "")) if m else None
def update_performance(recommendations: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
@@ -67,52 +110,37 @@ def update_performance(recommendations: List[Dict[str, Any]]) -> List[Dict[str,
if not all([ticker, discovery_date, entry_price]):
continue
- # Skip if already marked as closed
if rec.get("status") == "closed":
continue
try:
- # Get current price
- current_price_data = get_stock_price(ticker, curr_date=today)
-
- # Parse the price from the response (it returns a markdown report)
- # Format is typically: "**Current Price**: $XXX.XX"
- import re
-
- price_match = re.search(r"\$([0-9,.]+)", current_price_data)
- if price_match:
- current_price = float(price_match.group(1).replace(",", ""))
- else:
- logger.warning(f"Could not parse price for {ticker}")
+ current_price = _parse_price(get_stock_price(ticker, curr_date=today))
+ if current_price is None:
+ logger.warning(f"Could not get price for {ticker}")
continue
- # Calculate days since recommendation
rec_date = datetime.strptime(discovery_date, "%Y-%m-%d")
days_held = (datetime.now() - rec_date).days
-
- # Calculate return
return_pct = ((current_price - entry_price) / entry_price) * 100
- # Update metrics
rec["current_price"] = current_price
rec["return_pct"] = round(return_pct, 2)
rec["days_held"] = days_held
rec["last_updated"] = today
- # Check specific time periods
+ # Capture milestone returns (only once per milestone)
+ if days_held >= 1 and "return_1d" not in rec:
+ rec["return_1d"] = round(return_pct, 2)
+ rec["win_1d"] = return_pct > 0
+
if days_held >= 7 and "return_7d" not in rec:
rec["return_7d"] = round(return_pct, 2)
+ rec["win_7d"] = return_pct > 0
if days_held >= 30 and "return_30d" not in rec:
rec["return_30d"] = round(return_pct, 2)
- rec["status"] = "closed" # Mark as complete after 30 days
-
- # Determine win/loss for completed periods
- if "return_7d" in rec:
- rec["win_7d"] = rec["return_7d"] > 0
-
- if "return_30d" in rec:
- rec["win_30d"] = rec["return_30d"] > 0
+ rec["win_30d"] = return_pct > 0
+ rec["status"] = "closed"
logger.info(
f"✓ {ticker}: Entry ${entry_price:.2f} → Current ${current_price:.2f} ({return_pct:+.1f}%) [{days_held}d]"
@@ -149,80 +177,15 @@ def save_performance_database(recommendations: List[Dict[str, Any]]):
def calculate_statistics(recommendations: List[Dict[str, Any]]) -> Dict[str, Any]:
- """Calculate aggregate statistics from historical performance."""
- stats = {
- "total_recommendations": len(recommendations),
- "by_strategy": {},
- "overall_7d": {"count": 0, "wins": 0, "avg_return": 0},
- "overall_30d": {"count": 0, "wins": 0, "avg_return": 0},
- }
+ """Calculate aggregate statistics from historical performance.
- # Calculate by strategy
- for rec in recommendations:
- strategy = rec.get("strategy_match", "unknown")
+ Delegates to DiscoveryAnalytics.calculate_statistics so there is a single
+ source of truth for strategy normalization and metric calculation.
+ """
+ from tradingagents.dataflows.discovery.analytics import DiscoveryAnalytics
- if strategy not in stats["by_strategy"]:
- stats["by_strategy"][strategy] = {
- "count": 0,
- "wins_7d": 0,
- "losses_7d": 0,
- "wins_30d": 0,
- "losses_30d": 0,
- "avg_return_7d": 0,
- "avg_return_30d": 0,
- }
-
- stats["by_strategy"][strategy]["count"] += 1
-
- # 7-day stats
- if "return_7d" in rec:
- stats["overall_7d"]["count"] += 1
- if rec.get("win_7d"):
- stats["overall_7d"]["wins"] += 1
- stats["by_strategy"][strategy]["wins_7d"] += 1
- else:
- stats["by_strategy"][strategy]["losses_7d"] += 1
- stats["overall_7d"]["avg_return"] += rec["return_7d"]
-
- # 30-day stats
- if "return_30d" in rec:
- stats["overall_30d"]["count"] += 1
- if rec.get("win_30d"):
- stats["overall_30d"]["wins"] += 1
- stats["by_strategy"][strategy]["wins_30d"] += 1
- else:
- stats["by_strategy"][strategy]["losses_30d"] += 1
- stats["overall_30d"]["avg_return"] += rec["return_30d"]
-
- # Calculate averages and win rates
- if stats["overall_7d"]["count"] > 0:
- stats["overall_7d"]["win_rate"] = round(
- (stats["overall_7d"]["wins"] / stats["overall_7d"]["count"]) * 100, 1
- )
- stats["overall_7d"]["avg_return"] = round(
- stats["overall_7d"]["avg_return"] / stats["overall_7d"]["count"], 2
- )
-
- if stats["overall_30d"]["count"] > 0:
- stats["overall_30d"]["win_rate"] = round(
- (stats["overall_30d"]["wins"] / stats["overall_30d"]["count"]) * 100, 1
- )
- stats["overall_30d"]["avg_return"] = round(
- stats["overall_30d"]["avg_return"] / stats["overall_30d"]["count"], 2
- )
-
- # Calculate per-strategy stats
- for strategy, data in stats["by_strategy"].items():
- total_7d = data["wins_7d"] + data["losses_7d"]
- total_30d = data["wins_30d"] + data["losses_30d"]
-
- if total_7d > 0:
- data["win_rate_7d"] = round((data["wins_7d"] / total_7d) * 100, 1)
-
- if total_30d > 0:
- data["win_rate_30d"] = round((data["wins_30d"] / total_30d) * 100, 1)
-
- return stats
+ analytics = DiscoveryAnalytics()
+ return analytics.calculate_statistics(recommendations)
def print_statistics(stats: Dict[str, Any]):
diff --git a/scripts/update_positions.py b/scripts/update_positions.py
index 7bb99b60..d727d143 100755
--- a/scripts/update_positions.py
+++ b/scripts/update_positions.py
@@ -129,10 +129,12 @@ def main():
6. Save updated positions
7. Print progress messages
"""
- logger.info("""
+ logger.info(
+ """
╔══════════════════════════════════════════════════════════════╗
║ TradingAgents - Position Updater ║
-╚══════════════════════════════════════════════════════════════╝""".strip())
+╚══════════════════════════════════════════════════════════════╝""".strip()
+ )
# Initialize position tracker
tracker = PositionTracker(data_dir="data")
diff --git a/tradingagents/dataflows/discovery/analytics.py b/tradingagents/dataflows/discovery/analytics.py
index 47c2dc55..33bb27f9 100644
--- a/tradingagents/dataflows/discovery/analytics.py
+++ b/tradingagents/dataflows/discovery/analytics.py
@@ -20,24 +20,40 @@ class DiscoveryAnalytics:
self.recommendations_dir = self.data_dir / "recommendations"
self.recommendations_dir.mkdir(parents=True, exist_ok=True)
- def update_performance_tracking(self):
- """Update performance metrics for all open recommendations."""
- logger.info("📊 Updating recommendation performance tracking...")
+ def _load_existing_database(self) -> Dict[str, Dict]:
+ """Load existing performance database keyed by (ticker, discovery_date).
- if not self.recommendations_dir.exists():
- logger.info("No historical recommendations to track yet.")
- return
+ Returns a dict mapping "TICKER|DATE" -> rec dict, preserving accumulated
+ return data (return_1d, return_7d, etc.) across runs.
+ """
+ db_path = self.recommendations_dir / "performance_database.json"
+ if not db_path.exists():
+ return {}
- # Load all recommendations
+ try:
+ with open(db_path, "r") as f:
+ data = json.load(f)
+ except Exception as e:
+ logger.warning(f"Error loading performance database: {e}")
+ return {}
+
+ existing = {}
+ by_date = data.get("recommendations_by_date", {})
+ for recs in by_date.values():
+ if isinstance(recs, list):
+ for rec in recs:
+ key = f"{rec.get('ticker')}|{rec.get('discovery_date')}"
+ existing[key] = rec
+ return existing
+
+ def _load_raw_recommendations(self) -> List[Dict]:
+ """Load recommendations from raw date files."""
all_recs = []
- # Use glob directly on the path object if python 3.10+ otherwise str()
pattern = str(self.recommendations_dir / "*.json")
for filepath in glob.glob(pattern):
- # Skip the database and stats files
if "performance_database" in filepath or "statistics" in filepath:
continue
-
try:
with open(filepath, "r") as f:
data = json.load(f)
@@ -49,16 +65,46 @@ class DiscoveryAnalytics:
all_recs.append(rec)
except Exception as e:
logger.warning(f"Error loading {filepath}: {e}")
+ return all_recs
- if not all_recs:
+ def update_performance_tracking(self):
+ """Update performance metrics for all recommendations.
+
+ Loads accumulated data from performance_database.json first, merges in
+ any new recs from raw date files, then updates prices for open positions.
+ This preserves return_1d/return_7d/return_30d across runs.
+ """
+ logger.info("📊 Updating recommendation performance tracking...")
+
+ if not self.recommendations_dir.exists():
+ logger.info("No historical recommendations to track yet.")
+ return
+
+ # Step 1: Load existing database (preserves accumulated return data)
+ existing = self._load_existing_database()
+ logger.info(f"Loaded {len(existing)} existing records from performance database")
+
+ # Step 2: Load raw recommendation files and merge new ones
+ raw_recs = self._load_raw_recommendations()
+ new_count = 0
+ for rec in raw_recs:
+ key = f"{rec.get('ticker')}|{rec.get('discovery_date')}"
+ if key not in existing:
+ existing[key] = rec
+ new_count += 1
+
+ if not existing:
logger.info("No recommendations found to track.")
return
- # Filter to only track open positions
+ if new_count > 0:
+ logger.info(f"Added {new_count} new recommendations")
+
+ all_recs = list(existing.values())
open_recs = [r for r in all_recs if r.get("status") != "closed"]
logger.info(f"Tracking {len(open_recs)} open positions (out of {len(all_recs)} total)...")
- # Update performance
+ # Step 3: Update prices for open positions
today = datetime.now().strftime("%Y-%m-%d")
updated_count = 0
@@ -67,13 +113,10 @@ class DiscoveryAnalytics:
discovery_date = rec.get("discovery_date")
entry_price = rec.get("entry_price")
- # Skip if already closed or missing data
if rec.get("status") == "closed" or not all([ticker, discovery_date, entry_price]):
continue
try:
- # Get current price
- # We interpret this import here to avoid circular dependency if this class is imported early
from tradingagents.dataflows.y_finance import get_stock_price
current_price = get_stock_price(ticker, curr_date=today)
@@ -81,18 +124,16 @@ class DiscoveryAnalytics:
if current_price is None:
continue
- # Calculate metrics
rec_date = datetime.strptime(discovery_date, "%Y-%m-%d")
days_held = (datetime.now() - rec_date).days
return_pct = ((current_price - entry_price) / entry_price) * 100
- # Update
rec["current_price"] = current_price
rec["return_pct"] = round(return_pct, 2)
rec["days_held"] = days_held
rec["last_updated"] = today
- # Capture specific time periods (1d, 7d, 30d)
+ # Capture milestone returns (only once, at the first eligible run)
if days_held >= 1 and "return_1d" not in rec:
rec["return_1d"] = round(return_pct, 2)
rec["win_1d"] = return_pct > 0
@@ -109,11 +150,11 @@ class DiscoveryAnalytics:
updated_count += 1
except Exception:
- # Silently skip errors to not interrupt discovery
pass
- if updated_count > 0:
- logger.info(f"Updated {updated_count} positions")
+ # Step 4: Always save — even if no price updates, the merge may have added new recs
+ if updated_count > 0 or new_count > 0:
+ logger.info(f"Updated {updated_count} positions, {new_count} new recs")
self._save_performance_db(all_recs)
else:
logger.info("No updates needed")
@@ -148,6 +189,35 @@ class DiscoveryAnalytics:
logger.info("💾 Updated performance database and statistics")
+ @staticmethod
+ def _normalize_strategy(name: str) -> str:
+ """Normalize strategy names to snake_case canonical form.
+
+ Merges duplicates like 'Momentum' / 'momentum', 'Insider Play' / 'insider_buying'.
+ """
+ import re
+
+ if not name:
+ return "unknown"
+
+ # Lowercase and replace separators with underscore
+ normalized = name.strip().lower()
+ normalized = re.sub(r"[\s/]+", "_", normalized)
+ # Collapse multiple underscores
+ normalized = re.sub(r"_+", "_", normalized).strip("_")
+
+ # Map known aliases to canonical names
+ aliases = {
+ "insider_play": "insider_buying",
+ "earnings_play": "earnings_calendar",
+ "contrarian_value": "contrarian_value",
+ "news_catalyst": "news_catalyst",
+ "volume_accumulation": "volume_accumulation",
+ "momentum_hype": "momentum",
+ "momentum_hype_short_squeeze": "short_squeeze",
+ }
+ return aliases.get(normalized, normalized)
+
def calculate_statistics(self, recommendations: list) -> dict:
"""Calculate aggregate statistics from historical performance."""
stats = {
@@ -158,12 +228,9 @@ class DiscoveryAnalytics:
"overall_30d": {"count": 0, "wins": 0, "avg_return": 0},
}
- # Calculate by strategy
- for rec in recommendations:
- strategy = rec.get("strategy_match", "unknown")
-
- if strategy not in stats["by_strategy"]:
- stats["by_strategy"][strategy] = {
+ def _get_strategy_bucket(strategy_name):
+ if strategy_name not in stats["by_strategy"]:
+ stats["by_strategy"][strategy_name] = {
"count": 0,
"wins_1d": 0,
"losses_1d": 0,
@@ -175,45 +242,53 @@ class DiscoveryAnalytics:
"avg_return_7d": 0,
"avg_return_30d": 0,
}
+ return stats["by_strategy"][strategy_name]
- stats["by_strategy"][strategy]["count"] += 1
+ # Calculate by strategy
+ for rec in recommendations:
+ strategy = self._normalize_strategy(rec.get("strategy_match", "unknown"))
+ bucket = _get_strategy_bucket(strategy)
+ bucket["count"] += 1
# 1-day stats
if "return_1d" in rec:
stats["overall_1d"]["count"] += 1
+ bucket["avg_return_1d"] += rec["return_1d"]
if rec.get("win_1d"):
stats["overall_1d"]["wins"] += 1
- stats["by_strategy"][strategy]["wins_1d"] += 1
+ bucket["wins_1d"] += 1
else:
- stats["by_strategy"][strategy]["losses_1d"] += 1
+ bucket["losses_1d"] += 1
stats["overall_1d"]["avg_return"] += rec["return_1d"]
# 7-day stats
if "return_7d" in rec:
stats["overall_7d"]["count"] += 1
+ bucket["avg_return_7d"] += rec["return_7d"]
if rec.get("win_7d"):
stats["overall_7d"]["wins"] += 1
- stats["by_strategy"][strategy]["wins_7d"] += 1
+ bucket["wins_7d"] += 1
else:
- stats["by_strategy"][strategy]["losses_7d"] += 1
+ bucket["losses_7d"] += 1
stats["overall_7d"]["avg_return"] += rec["return_7d"]
# 30-day stats
if "return_30d" in rec:
stats["overall_30d"]["count"] += 1
+ bucket["avg_return_30d"] += rec["return_30d"]
if rec.get("win_30d"):
stats["overall_30d"]["wins"] += 1
- stats["by_strategy"][strategy]["wins_30d"] += 1
+ bucket["wins_30d"] += 1
else:
- stats["by_strategy"][strategy]["losses_30d"] += 1
+ bucket["losses_30d"] += 1
stats["overall_30d"]["avg_return"] += rec["return_30d"]
- # Calculate averages and win rates
+ # Calculate overall averages and win rates
self._calculate_metric_averages(stats["overall_1d"])
self._calculate_metric_averages(stats["overall_7d"])
self._calculate_metric_averages(stats["overall_30d"])
- # Calculate per-strategy stats
+ # Calculate per-strategy win rates and avg returns
for strategy, data in stats["by_strategy"].items():
total_1d = data["wins_1d"] + data["losses_1d"]
total_7d = data["wins_7d"] + data["losses_7d"]
@@ -221,12 +296,15 @@ class DiscoveryAnalytics:
if total_1d > 0:
data["win_rate_1d"] = round((data["wins_1d"] / total_1d) * 100, 1)
+ data["avg_return_1d"] = round(data["avg_return_1d"] / total_1d, 2)
if total_7d > 0:
data["win_rate_7d"] = round((data["wins_7d"] / total_7d) * 100, 1)
+ data["avg_return_7d"] = round(data["avg_return_7d"] / total_7d, 2)
if total_30d > 0:
data["win_rate_30d"] = round((data["wins_30d"] / total_30d) * 100, 1)
+ data["avg_return_30d"] = round(data["avg_return_30d"] / total_30d, 2)
return stats
diff --git a/tradingagents/ui/dashboard.py b/tradingagents/ui/dashboard.py
index 82b0744a..ffedf114 100644
--- a/tradingagents/ui/dashboard.py
+++ b/tradingagents/ui/dashboard.py
@@ -1,21 +1,23 @@
"""
Main Streamlit app entry point for the Trading Agents Dashboard.
-This module sets up the dashboard page configuration, sidebar navigation,
-and routing to different pages based on user selection.
+Dark terminal-inspired trading interface with sidebar navigation.
"""
+from datetime import datetime
+
import streamlit as st
from tradingagents.ui import pages
+from tradingagents.ui.theme import COLORS, GLOBAL_CSS
from tradingagents.ui.utils import load_quick_stats
def setup_page_config():
"""Configure the Streamlit page settings."""
st.set_page_config(
- page_title="Trading Agents Dashboard",
- page_icon="📊",
+ page_title="Trading Agents",
+ page_icon="",
layout="wide",
initial_sidebar_state="expanded",
)
@@ -24,46 +26,101 @@ def setup_page_config():
def render_sidebar():
"""Render the sidebar with navigation and quick stats."""
with st.sidebar:
- st.title("Trading Agents")
+ # Brand header
+ st.markdown(
+ f"""
+
+
+ TRADINGAGENTS
+
+
+ v2.0 — {datetime.now().strftime('%b %d, %Y')}
+
+
+
+ Quick Stats
+
+
+
+
+ {open_positions}
+
+
+ Open
+
+
+
+
+ {win_rate:.0f}%
+
+
+ Win Rate
+
+
+
+
Strategy Performance // scatter
',
+ unsafe_allow_html=True,
)
- with col2:
- st.metric(
- label="Open Positions",
- value=open_positions_count,
- )
+ if strategy_metrics:
+ df = pd.DataFrame(strategy_metrics)
+ template = get_plotly_template()
- with col3:
- st.metric(
- label="Avg Return (7d)",
- value=f"{avg_return_7d:.2f}%",
- delta=f"{avg_return_7d:.2f}%" if avg_return_7d > 0 else None,
- )
-
- with col4:
- if best_strategy:
- st.metric(
- label="Best Strategy",
- value=best_strategy["name"],
- delta=f"{best_strategy['win_rate']:.1f}% WR",
+ fig = px.scatter(
+ df,
+ x="Win Rate",
+ y="Avg Return",
+ size="Count",
+ color="Strategy",
+ hover_name="Strategy",
+ hover_data={"Win Rate": ":.1f", "Avg Return": ":.2f", "Count": True, "Strategy": False},
+ labels={"Win Rate": "Win Rate (%)", "Avg Return": "Avg Return (%)"},
+ size_max=40,
)
+
+ fig.add_hline(y=0, line_dash="dot", line_color=COLORS["text_muted"], opacity=0.4)
+ fig.add_vline(x=50, line_dash="dot", line_color=COLORS["text_muted"], opacity=0.4)
+ fig.add_annotation(x=75, y=5, text="WINNERS", showarrow=False, font=dict(size=10, color=COLORS["green"], family="JetBrains Mono"), opacity=0.3)
+ fig.add_annotation(x=25, y=-5, text="LOSERS", showarrow=False, font=dict(size=10, color=COLORS["red"], family="JetBrains Mono"), opacity=0.3)
+
+ fig.update_layout(
+ **template,
+ height=380,
+ showlegend=True,
+ legend=dict(bgcolor="rgba(0,0,0,0)", font=dict(size=10), orientation="h", yanchor="bottom", y=-0.25),
+ )
+ st.plotly_chart(fig, width="stretch")
else:
- st.metric(
- label="Best Strategy",
- value="N/A",
- )
+ st.info("Run the discovery pipeline to generate strategy data.")
- # Strategy Performance scatter plot
- st.subheader("Strategy Performance")
+ with right_col:
+ st.markdown(
+ 'Today\'s Signals // latest
',
+ unsafe_allow_html=True,
+ )
+ today = datetime.now().strftime("%Y-%m-%d")
+ recs = load_recommendations(today)
+
+ if recs:
+ for rec in recs[:6]:
+ ticker = rec.get("ticker", "???")
+ score = rec.get("final_score", 0)
+ conf = rec.get("confidence", 0)
+ strat = (rec.get("strategy_match") or "momentum").upper()
+ entry = rec.get("entry_price")
+ entry_str = f"${entry:.2f}" if entry else "N/A"
+
+ score_color = COLORS["green"] if score >= 35 else (COLORS["amber"] if score >= 20 else COLORS["text_muted"])
+ conf_bar_w = conf * 10
+ conf_color = COLORS["green"] if conf >= 8 else (COLORS["amber"] if conf >= 6 else COLORS["red"])
+
+ st.markdown(
+ f"""
+
+
+
+ {ticker}
+ {strat}
+
+
{score}
+
+
+
Strategy Breakdown // table
',
+ unsafe_allow_html=True,
+ )
+ df_table = pd.DataFrame(strategy_metrics).sort_values("Win Rate", ascending=False)
+ st.dataframe(
+ df_table,
+ width="stretch",
+ hide_index=True,
+ column_config={
+ "Win Rate": st.column_config.NumberColumn(format="%.1f%%"),
+ "Avg Return": st.column_config.NumberColumn(format="%+.2f%%"),
+ "Count": st.column_config.NumberColumn(format="%d"),
},
)
-
- # Add quadrant lines at y=0 (breakeven) and x=50 (50% win rate)
- fig.add_hline(y=0, line_dash="dash", line_color="gray", opacity=0.5)
- fig.add_vline(x=50, line_dash="dash", line_color="gray", opacity=0.5)
-
- # Update layout for better visibility
- fig.update_layout(
- height=400,
- showlegend=True,
- hovermode="closest",
- )
-
- st.plotly_chart(fig, use_container_width=True)
- else:
- st.info("No strategy data available for visualization.")
diff --git a/tradingagents/ui/pages/performance.py b/tradingagents/ui/pages/performance.py
index ef6c6bb2..f5ae1f20 100644
--- a/tradingagents/ui/pages/performance.py
+++ b/tradingagents/ui/pages/performance.py
@@ -1,50 +1,73 @@
"""
-Performance analytics page for the Trading Agents Dashboard.
+Performance analytics page — strategy comparison and win/loss analysis.
-This module displays performance metrics and visualization for different scanners,
-including win rates, average returns, and trading volume analysis.
+Shows strategy scatter plot with themed Plotly charts, per-strategy
+breakdown table, and win rate distribution.
"""
import pandas as pd
import plotly.express as px
+import plotly.graph_objects as go
import streamlit as st
-from tradingagents.ui.utils import load_strategy_metrics
+from tradingagents.ui.theme import COLORS, get_plotly_template, page_header
+from tradingagents.ui.utils import load_performance_database, load_statistics, load_strategy_metrics
def render() -> None:
- """
- Render the performance analytics page.
+ """Render the performance analytics page."""
+ st.markdown(page_header("Performance", "Strategy analytics & win/loss breakdown"), unsafe_allow_html=True)
- Displays:
- - Page title
- - Warning if no statistics available
- - Scanner Performance heatmap with scatter plot showing:
- - Win Rate (x-axis) vs Avg Return (y-axis)
- - Bubble size = Trade count
- - Color = Win Rate (RdYlGn colorscale)
- - Quadrant lines at y=0 and x=50
- """
- # Page title
- st.title("📊 Performance Analytics")
-
- # Load data
strategy_metrics = load_strategy_metrics()
+ stats = load_statistics()
- # Check if data is available
if not strategy_metrics:
- st.warning(
- "No strategy performance data available. Run performance tracking to generate data."
- )
+ st.warning("No performance data available yet. Run the discovery pipeline and track outcomes.")
return
- # Strategy Performance section
- st.subheader("Strategy Performance")
+ template = get_plotly_template()
+ df = pd.DataFrame(strategy_metrics)
- if strategy_metrics:
- df = pd.DataFrame(strategy_metrics)
+ # ---- 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
+ n_strategies = len(df)
+
+ cols = st.columns(4)
+ summaries = [
+ ("Total Trades", str(int(total_trades))),
+ ("Weighted Win Rate", f"{avg_wr:.1f}%"),
+ ("Weighted Avg Return", f"{avg_ret:+.2f}%"),
+ ("Active Strategies", str(n_strategies)),
+ ]
+ for col, (label, val) in zip(cols, summaries):
+ with col:
+ st.markdown(
+ f"""
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ st.markdown("", unsafe_allow_html=True)
+
+ # ---- Two-column: scatter + bar chart ----
+ left_col, right_col = st.columns(2)
+
+ with left_col:
+ st.markdown(
+ 'Win Rate vs Return // scatter
',
+ unsafe_allow_html=True,
+ )
- # Create scatter plot with plotly
fig = px.scatter(
df,
x="Win Rate",
@@ -52,31 +75,99 @@ def render() -> None:
size="Count",
color="Win Rate",
hover_name="Strategy",
- hover_data={
- "Win Rate": ":.1f",
- "Avg Return": ":.2f",
- "Count": True,
- "Strategy": False,
- },
- title="Strategy Performance Analysis",
- labels={
- "Win Rate": "Win Rate (%)",
- "Avg Return": "Avg Return (%)",
- },
- color_continuous_scale="RdYlGn",
+ hover_data={"Win Rate": ":.1f", "Avg Return": ":.2f", "Count": True},
+ labels={"Win Rate": "Win Rate (%)", "Avg Return": "Avg Return (%)"},
+ color_continuous_scale=[
+ [0, COLORS["red"]],
+ [0.5, COLORS["amber"]],
+ [1.0, COLORS["green"]],
+ ],
+ size_max=45,
)
- # Add quadrant lines at y=0 (breakeven) and x=50 (50% win rate)
- fig.add_hline(y=0, line_dash="dash", line_color="gray", opacity=0.5)
- fig.add_vline(x=50, line_dash="dash", line_color="gray", opacity=0.5)
+ fig.add_hline(y=0, line_dash="dot", line_color=COLORS["text_muted"], opacity=0.4)
+ fig.add_vline(x=50, line_dash="dot", line_color=COLORS["text_muted"], opacity=0.4)
- # Update layout for better visibility
fig.update_layout(
- height=500,
- showlegend=True,
- hovermode="closest",
+ **template,
+ height=400,
+ showlegend=False,
+ coloraxis_showscale=False,
+ )
+ st.plotly_chart(fig, width="stretch")
+
+ with right_col:
+ st.markdown(
+ 'Win Rate by Strategy // bar
',
+ unsafe_allow_html=True,
)
- st.plotly_chart(fig, use_container_width=True)
- else:
- st.info("No strategy data available for visualization.")
+ 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"]]
+
+ fig_bar = go.Figure(go.Bar(
+ x=df_sorted["Win Rate"],
+ y=df_sorted["Strategy"],
+ orientation="h",
+ marker_color=colors,
+ text=[f"{wr:.0f}%" for wr in df_sorted["Win Rate"]],
+ textposition="auto",
+ textfont=dict(family="JetBrains Mono", size=11, color=COLORS["text_primary"]),
+ ))
+
+ fig_bar.add_vline(x=50, line_dash="dot", line_color=COLORS["text_muted"], opacity=0.5)
+
+ fig_bar.update_layout(
+ **template,
+ height=400,
+ xaxis_title="Win Rate (%)",
+ yaxis_title="",
+ )
+ fig_bar.update_yaxes(tickfont=dict(family="JetBrains Mono", size=11))
+ st.plotly_chart(fig_bar, width="stretch")
+
+ # ---- Strategy breakdown table ----
+ st.markdown("", unsafe_allow_html=True)
+ st.markdown(
+ 'Detailed Breakdown // table
',
+ unsafe_allow_html=True,
+ )
+
+ display_df = df.copy()
+ display_df = display_df.sort_values("Win Rate", ascending=False)
+ display_df["Count"] = display_df["Count"].astype(int)
+ st.dataframe(
+ display_df,
+ width="stretch",
+ hide_index=True,
+ column_config={
+ "Win Rate": st.column_config.NumberColumn(format="%.1f%%"),
+ "Avg Return": st.column_config.NumberColumn(format="%+.2f%%"),
+ "Count": st.column_config.NumberColumn(format="%d"),
+ },
+ )
+
+ # ---- Per-strategy stats from statistics.json ----
+ if stats and stats.get("by_strategy"):
+ st.markdown("", unsafe_allow_html=True)
+ st.markdown(
+ 'Time-Period Breakdown // 1d / 7d / 30d
',
+ unsafe_allow_html=True,
+ )
+
+ by_strat = stats["by_strategy"]
+ rows = []
+ for strat_name, data in by_strat.items():
+ rows.append({
+ "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 7d": f"{data.get('win_rate_7d', 0):.0f}%" if "win_rate_7d" in data else "N/A",
+ "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)
diff --git a/tradingagents/ui/pages/portfolio.py b/tradingagents/ui/pages/portfolio.py
index 5b7897f8..09655251 100644
--- a/tradingagents/ui/pages/portfolio.py
+++ b/tradingagents/ui/pages/portfolio.py
@@ -1,90 +1,169 @@
-"""Portfolio tracker."""
+"""
+Portfolio page — position tracker with P/L visualization.
+
+Shows portfolio summary KPIs and individual position rows
+with color-coded P/L indicators.
+"""
from datetime import datetime
import pandas as pd
import streamlit as st
+from tradingagents.ui.theme import COLORS, kpi_card, page_header, pnl_color
from tradingagents.ui.utils import load_open_positions
def render():
- st.title("💼 Portfolio Tracker")
+ st.markdown(page_header("Portfolio", "Open positions & P/L tracker"), unsafe_allow_html=True)
- # Manual add form
- with st.expander("➕ Add Position"):
+ # ---- Add position form ----
+ with st.expander("Add Position"):
col1, col2, col3, col4 = st.columns(4)
with col1:
- ticker = st.text_input("Ticker")
+ ticker = st.text_input("Ticker", placeholder="AAPL")
with col2:
- entry_price = st.number_input("Entry Price", min_value=0.0)
+ entry_price = st.number_input("Entry Price", min_value=0.0, format="%.2f")
with col3:
shares = st.number_input("Shares", min_value=0, step=1)
with col4:
- st.write("") # Spacing
- if st.button("Add"):
+ st.write("")
+ if st.button("Add Position"):
if ticker and entry_price > 0 and shares > 0:
- from tradingagents.dataflows.discovery.performance.position_tracker import (
- PositionTracker,
- )
+ from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker
tracker = PositionTracker()
- pos = tracker.create_position(
- {
- "ticker": ticker.upper(),
- "entry_price": entry_price,
- "shares": shares,
- "recommendation_date": datetime.now().isoformat(),
- "pipeline": "manual",
- "scanner": "manual",
- "strategy_match": "manual",
- "confidence": 5,
- }
- )
+ pos = tracker.create_position({
+ "ticker": ticker.upper(),
+ "entry_price": entry_price,
+ "shares": shares,
+ "recommendation_date": datetime.now().isoformat(),
+ "pipeline": "manual",
+ "scanner": "manual",
+ "strategy_match": "manual",
+ "confidence": 5,
+ })
tracker.save_position(pos)
st.success(f"Added {ticker.upper()}")
st.rerun()
- # Load positions
positions = load_open_positions()
if not positions:
- st.info("No open positions")
+ st.markdown(
+ f"""
+
+
No open positions
+
+ Enter positions manually above or run the discovery pipeline.
+
+
Open Positions // live
',
+ unsafe_allow_html=True,
+ )
- data = []
for p in positions:
- pnl = (p["metrics"]["current_price"] - p["entry_price"]) * p.get("shares", 0)
- data.append(
- {
- "Ticker": p["ticker"],
- "Entry": f"${p['entry_price']:.2f}",
- "Current": f"${p['metrics']['current_price']:.2f}",
- "Shares": p.get("shares", 0),
- "P/L": f"${pnl:.2f}",
- "P/L %": f"{p['metrics']['current_return']:+.1f}%",
- "Days": p["metrics"]["days_held"],
- }
+ ticker = p["ticker"]
+ entry = p["entry_price"]
+ current = p["metrics"]["current_price"]
+ shares = p.get("shares", 0)
+ pnl_dollar = (current - entry) * shares
+ pnl_pct = p["metrics"]["current_return"]
+ days = p["metrics"]["days_held"]
+ color = pnl_color(pnl_pct)
+
+ st.markdown(
+ f"""
+
+
+
+ {ticker}
+
+ {shares} shares · {days}d
+
+
+
+
+ {pnl_pct:+.1f}%
+
+
+ ${pnl_dollar:+,.0f}
+
+
+
+
+
+ Entry ${entry:.2f}
+
+
+ Current ${current:.2f}
+
+
+
Discovery Settings // core
',
+ unsafe_allow_html=True,
+ )
- # Show key discovery settings
- col1, col2, col3 = st.columns(3)
+ settings_grid = [
+ ("Discovery Mode", discovery_config.get("discovery_mode", "N/A")),
+ ("Max Candidates", str(discovery_config.get("max_candidates_to_analyze", "N/A"))),
+ ("Final Recommendations", str(discovery_config.get("final_recommendations", "N/A"))),
+ ("Deep Dive Workers", str(discovery_config.get("deep_dive_max_workers", "N/A"))),
+ ]
- with col1:
- st.metric(
- label="Discovery Mode",
- value=discovery_config.get("discovery_mode", "N/A"),
+ cols = st.columns(len(settings_grid))
+ for col, (label, val) in zip(cols, settings_grid):
+ with col:
+ st.markdown(
+ f"""
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ st.markdown("", unsafe_allow_html=True)
+
+ # ---- Pipelines ----
+ left_col, right_col = st.columns(2)
+
+ with left_col:
+ st.markdown(
+ 'Pipelines // routing
',
+ unsafe_allow_html=True,
)
- with col2:
- st.metric(
- label="Max Candidates",
- value=discovery_config.get("max_candidates_to_analyze", "N/A"),
+ pipelines = discovery_config.get("pipelines", {})
+ for name, cfg in pipelines.items():
+ enabled = cfg.get("enabled", False)
+ priority = cfg.get("priority", "N/A")
+ budget = cfg.get("deep_dive_budget", "N/A")
+ status_color = COLORS["green"] if enabled else COLORS["red"]
+ status_dot = f''
+
+ st.markdown(
+ f"""
+
+
+
+ {status_dot}{name}
+
+
+ P:{priority}
+ B:{budget}
+
+
+
Scanners // sources
',
+ unsafe_allow_html=True,
)
- with col3:
- st.metric(
- label="Final Recommendations",
- value=discovery_config.get("final_recommendations", "N/A"),
- )
+ scanners = discovery_config.get("scanners", {})
+ for name, cfg in scanners.items():
+ enabled = cfg.get("enabled", False)
+ pipeline = cfg.get("pipeline", "N/A")
+ limit = cfg.get("limit", "N/A")
+ status_color = COLORS["green"] if enabled else COLORS["red"]
+ status_dot = f''
- # Pipelines section
- st.subheader("🔄 Pipelines")
+ st.markdown(
+ f"""
+
+
+
+ {status_dot}{name.replace('_', ' ')}
+
+
+ {pipeline}
+ limit:{limit}
+
+
+
Data Sources // vendors
',
+ unsafe_allow_html=True,
+ )
data_vendors = config.get("data_vendors", {})
-
if data_vendors:
- for vendor_type, vendor_name in data_vendors.items():
- st.write(f"**{vendor_type.replace('_', ' ').title()}**: {vendor_name}")
+ cols = st.columns(3)
+ for i, (vendor_type, vendor_name) in enumerate(data_vendors.items()):
+ with cols[i % 3]:
+ st.markdown(
+ f"""
+
+
{vendor_type.replace('_', ' ')}
+
+ {vendor_name}
+
+
+ Showing
+ {len(filtered)} of {len(recommendations)} signals
+
+
+ {display_date}
+
+
+ """,
+ unsafe_allow_html=True,
+ )
- # Display recommendations
- for i, rec in enumerate(filtered, 1):
- ticker = rec.get("ticker", "UNKNOWN")
- score = rec.get("final_score", 0)
- confidence = rec.get("confidence", 0)
- pipeline = (rec.get("pipeline") or rec.get("strategy_match") or "unknown").title()
- scanner = rec.get("scanner") or rec.get("strategy_match") or "unknown"
- entry_price = rec.get("entry_price")
- current_price = rec.get("current_price")
+ # ---- Signal cards in 2-column grid ----
+ for i in range(0, len(filtered), 2):
+ cols = st.columns(2)
+ for j, col in enumerate(cols):
+ idx = i + j
+ if idx >= len(filtered):
+ break
+ rec = filtered[idx]
+ ticker = rec.get("ticker", "UNKNOWN")
+ rank = rec.get("rank", idx + 1)
+ score = rec.get("final_score", 0)
+ confidence = rec.get("confidence", 0)
+ strategy = (rec.get("pipeline") or rec.get("strategy_match") or "unknown").title()
+ entry_price = rec.get("entry_price", 0)
+ reason = rec.get("reason", "No thesis provided.")
- with st.expander(
- f"#{i} {ticker} - {rec.get('company_name', '')} (Score: {score}, Conf: {confidence}/10)"
- ):
- col1, col2 = st.columns([2, 1])
+ with col:
+ st.markdown(
+ signal_card(rank, ticker, score, confidence, strategy, entry_price, reason),
+ unsafe_allow_html=True,
+ )
- with col1:
- st.write(f"**Pipeline:** {pipeline}")
- st.write(f"**Scanner/Strategy:** {scanner}")
- if entry_price is not None:
- st.write(f"**Entry Price:** ${entry_price:.2f}")
- if current_price is not None:
- st.write(f"**Current Price:** ${current_price:.2f}")
- st.write(f"**Thesis:** {rec.get('reason', 'N/A')}")
if show_charts:
history = _load_price_history(ticker, chart_window)
- if history.empty:
- st.caption("Price history unavailable.")
- else:
- last_close = history["close"].iloc[-1]
- st.caption(f"Last close: ${last_close:.2f}")
- fig = px.line(
- history,
- x="date",
- y="close",
- title=None,
- labels={"date": "", "close": "Price"},
- )
- fig.update_traces(line=dict(color="#1f77b4", width=2))
- fig.update_layout(
- height=260,
- margin=dict(l=10, r=10, t=10, b=10),
- xaxis=dict(showgrid=False),
- yaxis=dict(showgrid=True, gridcolor="rgba(0,0,0,0.08)"),
- hovermode="x unified",
- )
- fig.update_yaxes(tickprefix="$")
- st.plotly_chart(fig, use_container_width=True)
+ if not history.empty:
+ template = get_plotly_template()
+ fig = px.line(history, x="date", y="close", labels={"date": "", "close": "Price"})
- with col2:
- if st.button("✅ Enter Position", key=f"enter_{ticker}"):
- st.info("Position entry modal (TODO)")
- if st.button("👀 Watch", key=f"watch_{ticker}"):
- st.success(f"Added {ticker} to watchlist")
+ # Color line green if trending up, red if down
+ first_close = history["close"].iloc[0]
+ last_close = history["close"].iloc[-1]
+ line_color = COLORS["green"] if last_close >= first_close else COLORS["red"]
+
+ fig.update_traces(line=dict(color=line_color, width=1.5))
+ fig.update_layout(
+ **template,
+ height=160,
+ showlegend=False,
+ )
+ fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
+ fig.update_xaxes(showticklabels=False, showgrid=False)
+ fig.update_yaxes(showgrid=True, gridcolor="rgba(42,53,72,0.3)", tickprefix="$")
+ st.plotly_chart(fig, width="stretch")
+
+ # Action buttons
+ btn_cols = st.columns(2)
+ with btn_cols[0]:
+ if st.button("Enter Position", key=f"enter_{ticker}_{idx}"):
+ st.info(f"Position entry for {ticker} (TODO)")
+ with btn_cols[1]:
+ if st.button("Watchlist", key=f"watch_{ticker}_{idx}"):
+ st.success(f"Added {ticker} to watchlist")