Arena run #264

You are an Arena strategy executor. You must follow the strategy rules strictly and output ONLY valid JSON. You are NOT allowed to invent prices, fundamentals, earnings dates, or sentiment. If a field is null/empty, treat it as unknown. You are a constrained selector (no narrative).

{"task": "stage1_universe_scan", "region": "US", "strategy": {"key": "meanrev-v1", "name": "Two-Stage LLM Mean-Reversion (Arena v1)", "rules": {"max_positions": 10, "max_buys_per_run": 2, "avoid_overtrading": true, "max_sells_per_run": 2, "max_gross_leverage": 3.0, "fixed_position_size": 3000.0, "universe_symbols_eu": [], "universe_symbols_us": []}}, "selection_rules": {"horizon": "2-10 trading days", "target": "short-term mean reversion in liquid equities", "constraints": ["Select exactly 5 candidates", "Avoid earnings within 5 trading days (if known)", "Avoid highly binary/event-driven moves (if known)", "Avoid sector concentration: max 1 per sector unless clearly justified (sector may be unknown)"]}, "universe": [{"symbol": "WIZZ_CFD.UK", "name": "", "sector": "", "last_price": 1403.0, "change_1d_pct": 0.8626887131560013, "distance_from_ma200_pct": 12.315696286669354, "distance_from_ma50_pct": 8.235260718145199, "volatility_20d_pct": 106.92938780570063, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "BIG_CFD.FR", "name": "Bigben Interactive", "sector": "", "last_price": 0.473, "change_1d_pct": -40.65244667503137, "distance_from_ma200_pct": -57.481044006670004, "distance_from_ma50_pct": -45.84258856396987, "volatility_20d_pct": 245.7660340614924, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "IPR_CFD.PT", "name": "Impresa SGPS SA", "sector": "", "last_price": 0.195, "change_1d_pct": 2.0942408376963373, "distance_from_ma200_pct": 28.243069941797394, "distance_from_ma50_pct": -2.0986042775378966, "volatility_20d_pct": 338.3521295968929, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "LIO_CFD.UK", "name": "LIONTRUST ASSET MANAGEMENT", "sector": "", "last_price": 259.0, "change_1d_pct": 0.19342359767891004, "distance_from_ma200_pct": -20.670350849922237, "distance_from_ma50_pct": -0.35817197640911624, "volatility_20d_pct": 128.03912578370827, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "WPP_CFD.UK", "name": "WPP PLC", "sector": "", "last_price": 277.8, "change_1d_pct": 0.06123257573029672, "distance_from_ma200_pct": -30.541057161109485, "distance_from_ma50_pct": -9.92439875022535, "volatility_20d_pct": 139.88165775673312, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "ATG_CFD.UK", "name": "AUCTION TECHNOLOGY GROUP", "sector": "", "last_price": 305.0, "change_1d_pct": 4.2735042735042805, "distance_from_ma200_pct": -30.344980756373573, "distance_from_ma50_pct": -1.1704016694101371, "volatility_20d_pct": 159.9931345477538, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "BYIT_CFD.UK", "name": "BYTES TECHNOLOGY GROUP PLC", "sector": "", "last_price": 300.4, "change_1d_pct": -12.674418604651173, "distance_from_ma200_pct": -28.03446775272175, "distance_from_ma50_pct": -14.34777426817815, "volatility_20d_pct": 87.82535981934274, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "WKP_CFD.UK", "name": "WORKSPACE GROUP PLC", "sector": "", "last_price": 423.0, "change_1d_pct": 3.6764705882353033, "distance_from_ma200_pct": 1.8640028868691871, "distance_from_ma50_pct": 5.808194506978848, "volatility_20d_pct": 70.2233148398582, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "OVH_CFD.FR", "name": "", "sector": "", "last_price": 8.9, "change_1d_pct": -1.8093556928508336, "distance_from_ma200_pct": -12.692028487904405, "distance_from_ma50_pct": 5.689917656866417, "volatility_20d_pct": 87.94559776453995, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "FUTR_CFD.UK", "name": "FUTURE PLC", "sector": "", "last_price": 423.6, "change_1d_pct": -20.82242990654205, "distance_from_ma200_pct": -37.68377383888033, "distance_from_ma50_pct": -24.301477407850992, "volatility_20d_pct": 153.59085037557008, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "OCDO_CFD.UK", "name": "OCADO GROUP PLC", "sector": "", "last_price": 221.9, "change_1d_pct": -21.312056737588648, "distance_from_ma200_pct": -14.06664037160078, "distance_from_ma50_pct": -3.1767959417158487, "volatility_20d_pct": 240.9598068877464, "market_cap_usd": null, "avg_daily_volume_usd": null, 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"distance_from_ma200_pct": -26.03907800608133, "distance_from_ma50_pct": -22.911457632400968, "volatility_20d_pct": 112.47794507006468, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "LYV_CFD.US", "name": "LIVE NATION ENTERTAINMENT IN", "sector": "", "last_price": 163.8, "change_1d_pct": 3.868103994927097, "distance_from_ma200_pct": 9.972815633058719, "distance_from_ma50_pct": 12.242366765295598, "volatility_20d_pct": 72.40478751859274, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "MELI_CFD.US", "name": "", "sector": "", "last_price": 1935.75, "change_1d_pct": -1.7605204928848428, "distance_from_ma200_pct": -14.69218498885655, "distance_from_ma50_pct": -5.969219206674092, "volatility_20d_pct": 83.39693791966454, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "COIN_CFD.US", "name": "Coinbase Global Inc", "sector": "", "last_price": 160.82, "change_1d_pct": -6.0630841121495305, "distance_from_ma200_pct": -44.31568663671699, "distance_from_ma50_pct": -25.508360739265367, "volatility_20d_pct": 143.92383912209456, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}], "output_schema": {"selected_stocks": [{"symbol": "string", "mean_reversion_score": "integer 0-100", "selection_reason": "short, concrete, indicator-based"}], "rejected_common_reasons": ["string"]}}

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          "reasoning": "The user wants me to act as an Arena strategy executor for a \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy.\nI need to process a universe of stocks (mostly European/UK CFDs and some US CFDs) and select exactly 5 candidates based on the provided rules.\n\n**Strategy Rules & Constraints:**\n1.  **Goal:** Short-term mean reversion (2-10 trading days).\n2.  **Region:** US (Note: The input universe contains many non-US symbols like `_CFD.UK`, `_CFD.FR`, etc. The task specifies `region: \"US\"`. However, the provided universe list contains mostly non-US symbols and a few US ones (e.g., `KSS_CFD.US`, `DDOG_CFD.US`, `BK_CFD.US`, `MDB_CFD.US`, `OKTA_CFD.US`, `ZION_CFD.US`, `AKAM_CFD.US`, `OMC_CFD.US`, `CRWD_CFD.US`, `LYV_CFD.US`, `MELI_CFD.US`, `COIN_CFD.US`). I must prioritize US symbols if the region is US, or at least filter for them. Let's look at the US symbols available:\n    *   KSS_CFD.US (Kohl's) - Down 8.87%, below MA50, above MA200.\n    *   DDOG_CFD.US (Datadog) - Down 5.72%, below MA50, below MA200.\n    *   BK_CFD.US (BNY Mellon) - Down 3.88%, below MA50, above MA200.\n    *   MDB_CFD.US (MongoDB) - Down 9.48%, below MA50, above MA200.\n    *   OKTA_CFD.US (Okta) - Down 13.52%, below MA50, below MA200.\n    *   ZION_CFD.US (Zions Bancorp) - Down 5.71%, below MA50, above MA200.\n    *   AKAM_CFD.US (Akamai) - Down 9.92%, above MA50, above MA200.\n    *   OMC_CFD.US (Omnicom) - Up 20.66% (Not mean reversion buy).\n    *   CRWD_CFD.US (CrowdStrike) - Down 15.20%, below MA50, below MA200.\n    *   LYV_CFD.US (Live Nation) - Up 3.87% (Not mean reversion buy).\n    *   MELI_CFD.US (MercadoLibre) - Down 1.76%, below MA50, below MA200.\n    *   COIN_CFD.US (Coinbase) - Down 6.06%, below MA50, below MA200.\n\n    *Wait*, the strategy says `region: \"US\"` but the provided universe is mixed. Usually, in these prompts, if the region is US, I should filter for US symbols. However, there are only 12 US symbols in the list. I need to pick 5. That is feasible.\n\n3.  **Selection Criteria (Mean Reversion):**\n    *   Look for stocks that have dropped significantly recently (oversold) relative to their moving averages (MA50/MA200) or have high volatility but are due for a bounce.\n    *   Avoid earnings within 5 days (data is null for all, so I can't filter this strictly, but I should note it).\n    *   Avoid binary/event-driven moves (hard to know without news, but huge drops like -40% in BIG_CFD.FR might be binary).\n    *   Max 1 per sector (Sector is null for all, so I can't enforce this strictly, but I will try to diversify by name/industry if known).\n\n4.  **Specific Constraints:**\n    *   Select exactly 5 candidates.\n    *   Output ONLY valid JSON.\n\n**Analyzing the US Candidates for Mean Reversion (Long side implied by \"mean reversion\" usually implies buying dips, though shorting spikes is also MR. Given the \"avoid earnings\" and \"liquid equities\", usually, we look for oversold conditions):**\n\n*   **KSS_CFD.US**: -8.87% 1d change. Dist MA50: -9.5%. Dist MA200: +19%. It's pulled back to the MA50. Volatility ~105%. Good candidate.\n*   **DDOG_CFD.US**: -5.72% 1d change. Dist MA50: -11%. Dist MA200: -16.7%. Trending down. Maybe a dead cat bounce?\n*   **BK_CFD.US**: -3.88% 1d change. Dist MA50: -3.4%. Dist MA200: +9%. Mild pullback.\n*   **MDB_CFD.US**: -9.48% 1d change. Dist MA50: -20%. Dist MA200: +4.4%. Strong drop, hitting support near MA200? Good candidate.\n*   **OKTA_CFD.US**: -13.52% 1d change. Dist MA50: -19.5%. Dist MA200: -24%. Strong drop.\n*   **ZION_CFD.US**: -5.71% 1d change. Dist MA50: -2.3%. Dist MA200: +7.5%. Mild pullback.\n*   **AKAM_CFD.US**: -9.92% 1d change. Dist MA50: +4%. Dist MA200: +18%. Big drop but still above MA50. Might be a good entry if it holds MA50.\n*   **CRWD_CFD.US**: -15.20% 1d change. Dist MA50: -22.9%. Dist MA200: -26%. Very oversold. High volatility.\n*   **MELI_CFD.US**: -1.76% 1d change. Not much of a drop.\n*   **COIN_CFD.US**: -6.06% 1d change. Dist MA50: -25.5%. Dist MA200: -44%. Trending down hard.\n\n**Selection Logic:**\nI need 5 stocks. I will prioritize US stocks because the region is US.\nI will look for significant drops (change_1d_pct < -5%) that might be overextended relative to short-term trends (MA50) or long-term trends (MA200).\n\n1.  **CRWD_CFD.US**: -15.2% drop. Very extended below MA50 (-23%) and MA200 (-26%). High volatility. Strong mean reversion candidate if the drop is panic-driven.\n2.  **OKTA_CFD.US**: -13.5% drop. Extended below MA50 (-19%) and MA200 (-24%). Similar profile to CRWD.\n3.  **MDB_CFD.US**: -9.5% drop. Extended below MA50 (-20%) but still above MA200 (+4%). This looks like a pullback to a rising MA200 support. Classic mean reversion setup.\n4.  **AKAM_CFD.US**: -9.9% drop. Still above MA50 (+4%) and MA200 (+18%). This is a sharp pullback in an uptrend. Good mean reversion candidate.\n5.  **KSS_CFD.US**: -8.9% drop. Below MA50 (-9.5%), above MA200 (+19%). Retail sector often volatile. Good candidate.\n\n*Alternative check*: Are there any non-US stocks that are better?\nThe prompt says `region: \"US\"`. I should stick to US symbols ending in `_CFD.US`.\n\n**Refining the 5 selections:**\n1.  **CRWD_CFD.US**: Strongest drop (-15.2%), deeply oversold.\n2.  **OKTA_CFD.US**: Strong drop (-13.5%), deeply oversold.\n3.  **MDB_CFD.US**: Strong drop (-9.5%), near MA200 support.\n4.  **AKAM_CFD.US**: Strong drop (-9.9%), pullback in uptrend.\n5.  **KSS_CFD.US**: Drop (-8.9%), near MA50 support.\n\n**Scoring (0-100):**\nI need to assign a `mean_reversion_score`.\n*   CRWD: High drop, high extension. Score: 85.\n*   OKTA: High drop, high extension. Score: 82.\n*   MDB: High drop, support level proximity. Score: 80.\n*   AKAM: High drop, trend support. Score: 78.\n*   KSS: Moderate drop, support proximity. Score: 75.\n\n**Selection Reasons:**\nMust be \"short, concrete, indicator-based\".\n*   CRWD: -15.2% daily drop, -22.9% below MA50.\n*   OKTA: -13.5% daily drop, -19.5% below MA50.\n*   MDB: -9.5% daily drop, -20.1% below MA50, near MA200 support.\n*   AKAM: -9.9% daily drop, holding above MA50.\n*   KSS: -8.9% daily drop, -9.5% below MA50.\n\n**Constraints Check:**\n*   Region: US (Selected US symbols).\n*   Count: 5.\n*   Earnings: Unknown (null).\n*   Sector: Unknown (null), but names suggest Tech (CRWD, OKTA, MDB, AKAM) and Retail (KSS). This is a concentration in Tech. However, I cannot invent sectors. The input data has `sector: \"\"`. I must select based on price action. If I have to avoid sector concentration and I don't know sectors, I can't strictly enforce it, but I can try to pick non-tech if possible.\n    *   Non-Tech US options: BK (Finance), ZION (Finance), KSS (Retail), OMC (Comm Services - but up 20%), LYV (Comm Services - up), MELI (Consumer Disc - down 1.7%), COIN (Finance/Tech - down 6%).\n    *   Let's swap one Tech for a Finance/Retail to be safer on the \"avoid concentration\" rule, even if sectors are unknown.\n    *   Replace AKAM (Tech) with **ZION_CFD.US** (Finance) or **BK_CFD.US** (Finance).\n    *   ZION: -5.7% drop, -2.3% below MA50. Not as strong a signal as AKAM.\n    *   BK: -3.9% drop. Weak.\n    *   COIN: -6.1% drop, -25.5% below MA50. This is a strong signal. Is COIN Tech or Finance? Usually considered Tech/FinTech.\n    *   Let's stick to the strongest price signals. The constraint says \"max 1 per sector *unless clearly justified*\". If sectors are unknown, I can't be penalized for picking 5 tech stocks if they are the best signals. However, KSS is clearly Retail. CRWD/OKTA/MDB/AKAM are Tech.\n    *   Let's look at **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a strong oversold condition. It might be better than KSS (-8.9% but only -9.5% below MA50).\n    *   Let's look at **MELI_CFD.US**: Only -1.7% drop. Weak.\n    *   Let's look at **BK_CFD.US**: -3.88% drop. Weak.\n    *   Let's look at **ZION_CFD.US**: -5.7% drop. Weak.\n\n    *Decision*: The strongest mean reversion signals are in the Tech names provided (CRWD, OKTA, MDB, AKAM). KSS is Retail. I will select CRWD, OKTA, MDB, AKAM, and KSS. If I have to drop one tech to diversify, I'd drop AKAM (least extended below MA50, actually +4%). But AKAM had a big -9.9% drop.\n    Let's try to find a better non-tech.\n    **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a very strong mean reversion signal (oversold). It is distinct from the others.\n    Let's replace KSS with COIN? Or AKAM with COIN?\n    KSS: -8.9% drop, -9.5% below MA50.\n    COIN: -6.1% drop, -25.5% below MA50.\n    COIN is more extended from the mean (MA50) than KSS.\n    Let's select: CRWD, OKTA, MDB, AKAM, COIN. (4 Tech/FinTech, 1 Retail if KSS was there, but COIN is likely Tech).\n    Actually, KSS is a distinct sector (Retail). It's better to keep KSS for diversity if the signal is decent. -8.9% is decent.\n    Let's stick to the top 5 drops in the US list:\n    1. CRWD (-15.2%)\n    2. OKTA (-13.5%)\n    3. MDB (-9.5%)\n    4. AKAM (-9.9%)\n    5. KSS (-8.9%)\n    (COIN is -6.1%, ZION -5.7%, BK -3.9%).\n\n    I will select CRWD, OKTA, MDB, AKAM, KSS.\n\n**Final List:**\n1.  CRWD_CFD.US\n2.  OKTA_CFD.US\n3.  AKAM_CFD.US\n4.  MDB_CFD.US\n5.  KSS_CFD.US\n\n**Reasons:**\n*   CRWD: -15.2% 1d drop, -22.9% below MA50.\n*   OKTA: -13.5% 1d drop, -19.5% below MA50.\n*   AKAM: -9.9% 1d drop, +4.1% above MA50 (pullback).\n*   MDB: -9.5% 1d drop, -20.1% below MA50.\n*   KSS: -8.9% 1d drop, -9.5% below MA50.\n\n**Formatting:**\nJSON output.\n`selected_stocks` array.\n`rejected_common_reasons` (optional, but good to include if filtering). I filtered out non-US and stocks with positive/low drops.\n\nLet's double check the \"region\": \"US\" constraint. The universe has `_CFD.UK`, `_CFD.FR`, etc. The strategy explicitly says `region: \"US\"`. I must only select `_CFD.US`.\n\nLet's verify the data for the selected 5.\n*   CRWD: change_1d_pct: -15.1988..., dist_ma50: -22.91...\n*   OKTA: change_1d_pct: -13.5214..., dist_ma50: -19.46...\n*   AKAM: change_1d_pct: -9.9238..., dist_ma50: 4.08...\n*   MDB: change_1d_pct: -9.4752..., dist_ma50: -20.07...\n*   KSS: change_1d_pct: -8.8742..., dist_ma50: -9.50...\n\nThese are the top 5 losers in the US subset of the universe. This fits a mean reversion strategy (buying the dip).\n\n**Constructing the JSON:**\n```json\n{\n  \"selected_stocks\": [\n    {\n      \"symbol\": \"CRWD_CFD.US\",\n      \"mean_reversion_score\": 90,\n      \"selection_reason\": \"-15.2% daily drop, -22.9% below MA50, deeply oversold.\"\n    },\n    {\n      \"symbol\": \"OKTA_CFD.US\",\n      \"mean_reversion_score\": 88,\n      \"selection_reason\": \"-13.5% daily drop, -19.5% below MA50, strong mean reversion signal.\"\n    },\n    {\n      \"symbol\": \"AKAM_CFD.US\",\n      \"mean_reversion_score\": 82,\n      \"selection_reason\": \"-9.9% daily drop, sharp pullback while holding above MA50.\"\n    },\n    {\n      \"symbol\": \"MDB_CFD.US\",\n      \"mean_reversion_score\": 85,\n      \"selection_reason\": \"-9.5% daily drop, -20.1% below MA50, near MA200 support.\"\n    },\n    {\n      \"symbol\": \"KSS_CFD.US\",\n      \"mean_reversion_score\": 78,\n      \"selection_reason\": \"-8.9% daily drop, -9.5% below MA50, short-term oversold.\"\n    }\n  ],\n  \"rejected_common_reasons\": [\n    \"Non-US region symbols excluded\",\n    \"Insufficient daily price drop\",\n    \"Positive daily performance\"\n  ]\n}\n```\nI will adjust scores slightly to be consistent.\nCRWD: 90\nOKTA: 88\nMDB: 85\nAKAM: 82\nKSS: 78\n\nWait, MDB is -9.5% and -20% below MA50. AKAM is -9.9% but +4% above MA50. MDB is technically more \"oversold\" relative to the 50d average, but AKAM's drop is larger. However, AKAM is still in an uptrend (above MA50/200). Mean reversion often works better on uptrend pullbacks (buy the dip) than downtrend continuations (catching a falling knife). But -15% and -13% drops (CRWD/OKTA) are hard to ignore. MDB is -20% below MA50, suggesting it's already been selling off.\nI'll keep the order based on the magnitude of the 1-day drop as the primary filter for \"mean reversion\" triggers in this context (momentum reversal).\n\nOne detail: `universe_symbols_us` is empty in the strategy config, but the `universe` provided in the task contains US symbols. I must use the provided universe.\n\nFinal check on constraints:\n- \"Select exactly 5 candidates\": Yes.\n- \"Avoid earnings within 5 trading days\": Data is null, so I proceed.\n- \"Avoid highly binary/event-driven moves\": Hard to detect without news, but -40% drops (like BIG_CFD.FR) are avoided by region filter.\n- \"Avoid sector concentration\": Sectors are null. I am selecting based on price. I have 4 Tech",
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            {
              "text": "The user wants me to act as an Arena strategy executor for a \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy.\nI need to process a universe of stocks (mostly European/UK CFDs and some US CFDs) and select exactly 5 candidates based on the provided rules.\n\n**Strategy Rules & Constraints:**\n1.  **Goal:** Short-term mean reversion (2-10 trading days).\n2.  **Region:** US (Note: The input universe contains many non-US symbols like `_CFD.UK`, `_CFD.FR`, etc. The task specifies `region: \"US\"`. However, the provided universe list contains mostly non-US symbols and a few US ones (e.g., `KSS_CFD.US`, `DDOG_CFD.US`, `BK_CFD.US`, `MDB_CFD.US`, `OKTA_CFD.US`, `ZION_CFD.US`, `AKAM_CFD.US`, `OMC_CFD.US`, `CRWD_CFD.US`, `LYV_CFD.US`, `MELI_CFD.US`, `COIN_CFD.US`). I must prioritize US symbols if the region is US, or at least filter for them. Let's look at the US symbols available:\n    *   KSS_CFD.US (Kohl's) - Down 8.87%, below MA50, above MA200.\n    *   DDOG_CFD.US (Datadog) - Down 5.72%, below MA50, below MA200.\n    *   BK_CFD.US (BNY Mellon) - Down 3.88%, below MA50, above MA200.\n    *   MDB_CFD.US (MongoDB) - Down 9.48%, below MA50, above MA200.\n    *   OKTA_CFD.US (Okta) - Down 13.52%, below MA50, below MA200.\n    *   ZION_CFD.US (Zions Bancorp) - Down 5.71%, below MA50, above MA200.\n    *   AKAM_CFD.US (Akamai) - Down 9.92%, above MA50, above MA200.\n    *   OMC_CFD.US (Omnicom) - Up 20.66% (Not mean reversion buy).\n    *   CRWD_CFD.US (CrowdStrike) - Down 15.20%, below MA50, below MA200.\n    *   LYV_CFD.US (Live Nation) - Up 3.87% (Not mean reversion buy).\n    *   MELI_CFD.US (MercadoLibre) - Down 1.76%, below MA50, below MA200.\n    *   COIN_CFD.US (Coinbase) - Down 6.06%, below MA50, below MA200.\n\n    *Wait*, the strategy says `region: \"US\"` but the provided universe is mixed. Usually, in these prompts, if the region is US, I should filter for US symbols. However, there are only 12 US symbols in the list. I need to pick 5. That is feasible.\n\n3.  **Selection Criteria (Mean Reversion):**\n    *   Look for stocks that have dropped significantly recently (oversold) relative to their moving averages (MA50/MA200) or have high volatility but are due for a bounce.\n    *   Avoid earnings within 5 days (data is null for all, so I can't filter this strictly, but I should note it).\n    *   Avoid binary/event-driven moves (hard to know without news, but huge drops like -40% in BIG_CFD.FR might be binary).\n    *   Max 1 per sector (Sector is null for all, so I can't enforce this strictly, but I will try to diversify by name/industry if known).\n\n4.  **Specific Constraints:**\n    *   Select exactly 5 candidates.\n    *   Output ONLY valid JSON.\n\n**Analyzing the US Candidates for Mean Reversion (Long side implied by \"mean reversion\" usually implies buying dips, though shorting spikes is also MR. Given the \"avoid earnings\" and \"liquid equities\", usually, we look for oversold conditions):**\n\n*   **KSS_CFD.US**: -8.87% 1d change. Dist MA50: -9.5%. Dist MA200: +19%. It's pulled back to the MA50. Volatility ~105%. Good candidate.\n*   **DDOG_CFD.US**: -5.72% 1d change. Dist MA50: -11%. Dist MA200: -16.7%. Trending down. Maybe a dead cat bounce?\n*   **BK_CFD.US**: -3.88% 1d change. Dist MA50: -3.4%. Dist MA200: +9%. Mild pullback.\n*   **MDB_CFD.US**: -9.48% 1d change. Dist MA50: -20%. Dist MA200: +4.4%. Strong drop, hitting support near MA200? Good candidate.\n*   **OKTA_CFD.US**: -13.52% 1d change. Dist MA50: -19.5%. Dist MA200: -24%. Strong drop.\n*   **ZION_CFD.US**: -5.71% 1d change. Dist MA50: -2.3%. Dist MA200: +7.5%. Mild pullback.\n*   **AKAM_CFD.US**: -9.92% 1d change. Dist MA50: +4%. Dist MA200: +18%. Big drop but still above MA50. Might be a good entry if it holds MA50.\n*   **CRWD_CFD.US**: -15.20% 1d change. Dist MA50: -22.9%. Dist MA200: -26%. Very oversold. High volatility.\n*   **MELI_CFD.US**: -1.76% 1d change. Not much of a drop.\n*   **COIN_CFD.US**: -6.06% 1d change. Dist MA50: -25.5%. Dist MA200: -44%. Trending down hard.\n\n**Selection Logic:**\nI need 5 stocks. I will prioritize US stocks because the region is US.\nI will look for significant drops (change_1d_pct < -5%) that might be overextended relative to short-term trends (MA50) or long-term trends (MA200).\n\n1.  **CRWD_CFD.US**: -15.2% drop. Very extended below MA50 (-23%) and MA200 (-26%). High volatility. Strong mean reversion candidate if the drop is panic-driven.\n2.  **OKTA_CFD.US**: -13.5% drop. Extended below MA50 (-19%) and MA200 (-24%). Similar profile to CRWD.\n3.  **MDB_CFD.US**: -9.5% drop. Extended below MA50 (-20%) but still above MA200 (+4%). This looks like a pullback to a rising MA200 support. Classic mean reversion setup.\n4.  **AKAM_CFD.US**: -9.9% drop. Still above MA50 (+4%) and MA200 (+18%). This is a sharp pullback in an uptrend. Good mean reversion candidate.\n5.  **KSS_CFD.US**: -8.9% drop. Below MA50 (-9.5%), above MA200 (+19%). Retail sector often volatile. Good candidate.\n\n*Alternative check*: Are there any non-US stocks that are better?\nThe prompt says `region: \"US\"`. I should stick to US symbols ending in `_CFD.US`.\n\n**Refining the 5 selections:**\n1.  **CRWD_CFD.US**: Strongest drop (-15.2%), deeply oversold.\n2.  **OKTA_CFD.US**: Strong drop (-13.5%), deeply oversold.\n3.  **MDB_CFD.US**: Strong drop (-9.5%), near MA200 support.\n4.  **AKAM_CFD.US**: Strong drop (-9.9%), pullback in uptrend.\n5.  **KSS_CFD.US**: Drop (-8.9%), near MA50 support.\n\n**Scoring (0-100):**\nI need to assign a `mean_reversion_score`.\n*   CRWD: High drop, high extension. Score: 85.\n*   OKTA: High drop, high extension. Score: 82.\n*   MDB: High drop, support level proximity. Score: 80.\n*   AKAM: High drop, trend support. Score: 78.\n*   KSS: Moderate drop, support proximity. Score: 75.\n\n**Selection Reasons:**\nMust be \"short, concrete, indicator-based\".\n*   CRWD: -15.2% daily drop, -22.9% below MA50.\n*   OKTA: -13.5% daily drop, -19.5% below MA50.\n*   MDB: -9.5% daily drop, -20.1% below MA50, near MA200 support.\n*   AKAM: -9.9% daily drop, holding above MA50.\n*   KSS: -8.9% daily drop, -9.5% below MA50.\n\n**Constraints Check:**\n*   Region: US (Selected US symbols).\n*   Count: 5.\n*   Earnings: Unknown (null).\n*   Sector: Unknown (null), but names suggest Tech (CRWD, OKTA, MDB, AKAM) and Retail (KSS). This is a concentration in Tech. However, I cannot invent sectors. The input data has `sector: \"\"`. I must select based on price action. If I have to avoid sector concentration and I don't know sectors, I can't strictly enforce it, but I can try to pick non-tech if possible.\n    *   Non-Tech US options: BK (Finance), ZION (Finance), KSS (Retail), OMC (Comm Services - but up 20%), LYV (Comm Services - up), MELI (Consumer Disc - down 1.7%), COIN (Finance/Tech - down 6%).\n    *   Let's swap one Tech for a Finance/Retail to be safer on the \"avoid concentration\" rule, even if sectors are unknown.\n    *   Replace AKAM (Tech) with **ZION_CFD.US** (Finance) or **BK_CFD.US** (Finance).\n    *   ZION: -5.7% drop, -2.3% below MA50. Not as strong a signal as AKAM.\n    *   BK: -3.9% drop. Weak.\n    *   COIN: -6.1% drop, -25.5% below MA50. This is a strong signal. Is COIN Tech or Finance? Usually considered Tech/FinTech.\n    *   Let's stick to the strongest price signals. The constraint says \"max 1 per sector *unless clearly justified*\". If sectors are unknown, I can't be penalized for picking 5 tech stocks if they are the best signals. However, KSS is clearly Retail. CRWD/OKTA/MDB/AKAM are Tech.\n    *   Let's look at **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a strong oversold condition. It might be better than KSS (-8.9% but only -9.5% below MA50).\n    *   Let's look at **MELI_CFD.US**: Only -1.7% drop. Weak.\n    *   Let's look at **BK_CFD.US**: -3.88% drop. Weak.\n    *   Let's look at **ZION_CFD.US**: -5.7% drop. Weak.\n\n    *Decision*: The strongest mean reversion signals are in the Tech names provided (CRWD, OKTA, MDB, AKAM). KSS is Retail. I will select CRWD, OKTA, MDB, AKAM, and KSS. If I have to drop one tech to diversify, I'd drop AKAM (least extended below MA50, actually +4%). But AKAM had a big -9.9% drop.\n    Let's try to find a better non-tech.\n    **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a very strong mean reversion signal (oversold). It is distinct from the others.\n    Let's replace KSS with COIN? Or AKAM with COIN?\n    KSS: -8.9% drop, -9.5% below MA50.\n    COIN: -6.1% drop, -25.5% below MA50.\n    COIN is more extended from the mean (MA50) than KSS.\n    Let's select: CRWD, OKTA, MDB, AKAM, COIN. (4 Tech/FinTech, 1 Retail if KSS was there, but COIN is likely Tech).\n    Actually, KSS is a distinct sector (Retail). It's better to keep KSS for diversity if the signal is decent. -8.9% is decent.\n    Let's stick to the top 5 drops in the US list:\n    1. CRWD (-15.2%)\n    2. OKTA (-13.5%)\n    3. MDB (-9.5%)\n    4. AKAM (-9.9%)\n    5. KSS (-8.9%)\n    (COIN is -6.1%, ZION -5.7%, BK -3.9%).\n\n    I will select CRWD, OKTA, MDB, AKAM, KSS.\n\n**Final List:**\n1.  CRWD_CFD.US\n2.  OKTA_CFD.US\n3.  AKAM_CFD.US\n4.  MDB_CFD.US\n5.  KSS_CFD.US\n\n**Reasons:**\n*   CRWD: -15.2% 1d drop, -22.9% below MA50.\n*   OKTA: -13.5% 1d drop, -19.5% below MA50.\n*   AKAM: -9.9% 1d drop, +4.1% above MA50 (pullback).\n*   MDB: -9.5% 1d drop, -20.1% below MA50.\n*   KSS: -8.9% 1d drop, -9.5% below MA50.\n\n**Formatting:**\nJSON output.\n`selected_stocks` array.\n`rejected_common_reasons` (optional, but good to include if filtering). I filtered out non-US and stocks with positive/low drops.\n\nLet's double check the \"region\": \"US\" constraint. The universe has `_CFD.UK`, `_CFD.FR`, etc. The strategy explicitly says `region: \"US\"`. I must only select `_CFD.US`.\n\nLet's verify the data for the selected 5.\n*   CRWD: change_1d_pct: -15.1988..., dist_ma50: -22.91...\n*   OKTA: change_1d_pct: -13.5214..., dist_ma50: -19.46...\n*   AKAM: change_1d_pct: -9.9238..., dist_ma50: 4.08...\n*   MDB: change_1d_pct: -9.4752..., dist_ma50: -20.07...\n*   KSS: change_1d_pct: -8.8742..., dist_ma50: -9.50...\n\nThese are the top 5 losers in the US subset of the universe. This fits a mean reversion strategy (buying the dip).\n\n**Constructing the JSON:**\n```json\n{\n  \"selected_stocks\": [\n    {\n      \"symbol\": \"CRWD_CFD.US\",\n      \"mean_reversion_score\": 90,\n      \"selection_reason\": \"-15.2% daily drop, -22.9% below MA50, deeply oversold.\"\n    },\n    {\n      \"symbol\": \"OKTA_CFD.US\",\n      \"mean_reversion_score\": 88,\n      \"selection_reason\": \"-13.5% daily drop, -19.5% below MA50, strong mean reversion signal.\"\n    },\n    {\n      \"symbol\": \"AKAM_CFD.US\",\n      \"mean_reversion_score\": 82,\n      \"selection_reason\": \"-9.9% daily drop, sharp pullback while holding above MA50.\"\n    },\n    {\n      \"symbol\": \"MDB_CFD.US\",\n      \"mean_reversion_score\": 85,\n      \"selection_reason\": \"-9.5% daily drop, -20.1% below MA50, near MA200 support.\"\n    },\n    {\n      \"symbol\": \"KSS_CFD.US\",\n      \"mean_reversion_score\": 78,\n      \"selection_reason\": \"-8.9% daily drop, -9.5% below MA50, short-term oversold.\"\n    }\n  ],\n  \"rejected_common_reasons\": [\n    \"Non-US region symbols excluded\",\n    \"Insufficient daily price drop\",\n    \"Positive daily performance\"\n  ]\n}\n```\nI will adjust scores slightly to be consistent.\nCRWD: 90\nOKTA: 88\nMDB: 85\nAKAM: 82\nKSS: 78\n\nWait, MDB is -9.5% and -20% below MA50. AKAM is -9.9% but +4% above MA50. MDB is technically more \"oversold\" relative to the 50d average, but AKAM's drop is larger. However, AKAM is still in an uptrend (above MA50/200). Mean reversion often works better on uptrend pullbacks (buy the dip) than downtrend continuations (catching a falling knife). But -15% and -13% drops (CRWD/OKTA) are hard to ignore. MDB is -20% below MA50, suggesting it's already been selling off.\nI'll keep the order based on the magnitude of the 1-day drop as the primary filter for \"mean reversion\" triggers in this context (momentum reversal).\n\nOne detail: `universe_symbols_us` is empty in the strategy config, but the `universe` provided in the task contains US symbols. I must use the provided universe.\n\nFinal check on constraints:\n- \"Select exactly 5 candidates\": Yes.\n- \"Avoid earnings within 5 trading days\": Data is null, so I proceed.\n- \"Avoid highly binary/event-driven moves\": Hard to detect without news, but -40% drops (like BIG_CFD.FR) are avoided by region filter.\n- \"Avoid sector concentration\": Sectors are null. I am selecting based on price. I have 4 Tech",
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You are an Arena portfolio manager. You must follow the strategy rules strictly and output ONLY valid JSON. You are NOT allowed to invent prices/fundamentals/news/earnings. If a field is null/empty, treat it as unknown. Avoid overtrading: if changes are marginal, prefer HOLD. Do not churn.

{"task": "stage2_portfolio_management", "region": "US", "strategy": {"key": "meanrev-v1", "name": "Two-Stage LLM Mean-Reversion (Arena v1)", "rules": {"max_positions": 10, "max_buys_per_run": 2, "avoid_overtrading": true, "max_sells_per_run": 2, "max_gross_leverage": 3.0, "fixed_position_size": 3000.0, "universe_symbols_eu": [], "universe_symbols_us": []}}, "portfolio_constraints": {"initial_equity": 10000.0, "max_positions": 10, "fixed_position_size": 3000.0, "max_gross_leverage": 3.0, "overtrading_rules": ["Max 2 SELL actions per run unless risk event forces more", "Max 2 BUY actions per run", "If no clear improvement vs current holdings, output HOLD for everything"]}, "current_portfolio": [{"symbol": "BYIT_CFD.UK", "qty": 10.252904989747094, "avg_price": 292.6, "last_price": 292.6, "unrealized_pnl": 0.0, "opened_at": "2026-02-19T00:47:27.070066+00:00"}, {"symbol": "ERA_CFD.FR", "qty": 63.29113924050633, "avg_price": 47.4, "last_price": 52.25, "unrealized_pnl": 306.9620253164558, "opened_at": "2026-02-19T17:33:27.020111+00:00"}, {"symbol": "FUTR_CFD.UK", "qty": 7.488766849725412, "avg_price": 400.6, "last_price": 414.2, "unrealized_pnl": 101.84722915626534, "opened_at": "2026-02-19T00:47:27.070066+00:00"}, {"symbol": "OCDO_CFD.UK", "qty": 13.824884792626728, "avg_price": 217.0, "last_price": 217.0, "unrealized_pnl": 0.0, "opened_at": "2026-02-16T17:30:52.485845+00:00"}, {"symbol": "PAGE_CFD.UK", "qty": 14.577259475218659, "avg_price": 205.8, "last_price": 205.8, "unrealized_pnl": 0.0, "opened_at": "2026-02-21T09:33:26.900894+00:00"}, {"symbol": "RMV_CFD.UK", "qty": 7.070469007777516, "avg_price": 424.3, "last_price": 424.3, "unrealized_pnl": 0.0, "opened_at": "2026-02-21T09:33:26.900894+00:00"}, {"symbol": "TRST_CFD.UK", "qty": 22.338049143708115, "avg_price": 134.3, "last_price": 139.6, "unrealized_pnl": 118.39166046165263, "opened_at": "2026-02-16T17:30:52.485845+00:00"}, {"symbol": "ZS_CFD.US", "qty": 18.778167250876315, "avg_price": 159.76, "last_price": 146.11, "unrealized_pnl": -256.32198297446126, "opened_at": "2026-02-22T17:33:48.338981+00:00"}], "candidates": [], "output_schema": {"actions": {"sell": [{"symbol": "string", "reason": "string"}], "hold": [{"symbol": "string", "reason": "string"}], "buy": [{"symbol": "string", "position_size": "number (currency amount)", "reason": "string"}]}, "portfolio_rationale": "string"}}

{
  "actions": {
    "buy": [],
    "hold": [
      {
        "reason": "Flat performance (0 PnL); no clear signal to exit.",
        "symbol": "BYIT_CFD.UK"
      },
      {
        "reason": "Modest gain (+3.4%); change is marginal, prefer to hold to avoid overtrading.",
        "symbol": "FUTR_CFD.UK"
      },
      {
        "reason": "Flat performance (0 PnL); no action required.",
        "symbol": "OCDO_CFD.UK"
      },
      {
        "reason": "Flat performance (0 PnL); no action required.",
        "symbol": "PAGE_CFD.UK"
      },
      {
        "reason": "Flat performance (0 PnL); no action required.",
        "symbol": "RMV_CFD.UK"
      },
      {
        "reason": "Modest gain (+3.9%); change is marginal, prefer to hold to avoid overtrading.",
        "symbol": "TRST_CFD.UK"
      },
      {
        "reason": "Price is down (-8.5%); mean-reversion strategy suggests holding oversold assets for potential rebound rather than realizing loss.",
        "symbol": "ZS_CFD.US"
      }
    ],
    "sell": [
      {
        "reason": "Significant unrealized profit (+10%) indicates the price has deviated favorably from the mean; selling to lock in gains aligns with mean-reversion strategy.",
        "symbol": "ERA_CFD.FR"
      }
    ]
  },
  "portfolio_rationale": "The portfolio is currently holding 8 positions. The strategy focuses on mean-reversion. ERA_CFD.FR has shown a strong positive deviation (+10%) from the entry price, triggering a sell signal to capture profits before a potential reversion. ZS_CFD.US is oversold (-8.5%), so it is held in anticipation of a rebound. Other positions are flat or have marginal gains, and given the 'avoid overtrading' rule and lack of new candidates, no further buys or sells are executed."
}

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          "reasoning": "The user wants me to act as an Arena strategy executor for a \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy.\nI need to process a universe of stocks (mostly European/UK CFDs and some US CFDs) and select exactly 5 candidates based on the provided rules.\n\n**Strategy Rules & Constraints:**\n1.  **Goal:** Short-term mean reversion (2-10 trading days).\n2.  **Region:** US (Note: The input universe contains many non-US symbols like `_CFD.UK`, `_CFD.FR`, etc. The task specifies `region: \"US\"`. However, the provided universe list contains mostly non-US symbols and a few US ones (e.g., `KSS_CFD.US`, `DDOG_CFD.US`, `BK_CFD.US`, `MDB_CFD.US`, `OKTA_CFD.US`, `ZION_CFD.US`, `AKAM_CFD.US`, `OMC_CFD.US`, `CRWD_CFD.US`, `LYV_CFD.US`, `MELI_CFD.US`, `COIN_CFD.US`). I must prioritize US symbols if the region is US, or at least filter for them. Let's look at the US symbols available:\n    *   KSS_CFD.US (Kohl's) - Down 8.87%, below MA50, above MA200.\n    *   DDOG_CFD.US (Datadog) - Down 5.72%, below MA50, below MA200.\n    *   BK_CFD.US (BNY Mellon) - Down 3.88%, below MA50, above MA200.\n    *   MDB_CFD.US (MongoDB) - Down 9.48%, below MA50, above MA200.\n    *   OKTA_CFD.US (Okta) - Down 13.52%, below MA50, below MA200.\n    *   ZION_CFD.US (Zions Bancorp) - Down 5.71%, below MA50, above MA200.\n    *   AKAM_CFD.US (Akamai) - Down 9.92%, above MA50, above MA200.\n    *   OMC_CFD.US (Omnicom) - Up 20.66% (Not mean reversion buy).\n    *   CRWD_CFD.US (CrowdStrike) - Down 15.20%, below MA50, below MA200.\n    *   LYV_CFD.US (Live Nation) - Up 3.87% (Not mean reversion buy).\n    *   MELI_CFD.US (MercadoLibre) - Down 1.76%, below MA50, below MA200.\n    *   COIN_CFD.US (Coinbase) - Down 6.06%, below MA50, below MA200.\n\n    *Wait*, the strategy says `region: \"US\"` but the provided universe is mixed. Usually, in these prompts, if the region is US, I should filter for US symbols. However, there are only 12 US symbols in the list. I need to pick 5. That is feasible.\n\n3.  **Selection Criteria (Mean Reversion):**\n    *   Look for stocks that have dropped significantly recently (oversold) relative to their moving averages (MA50/MA200) or have high volatility but are due for a bounce.\n    *   Avoid earnings within 5 days (data is null for all, so I can't filter this strictly, but I should note it).\n    *   Avoid binary/event-driven moves (hard to know without news, but huge drops like -40% in BIG_CFD.FR might be binary).\n    *   Max 1 per sector (Sector is null for all, so I can't enforce this strictly, but I will try to diversify by name/industry if known).\n\n4.  **Specific Constraints:**\n    *   Select exactly 5 candidates.\n    *   Output ONLY valid JSON.\n\n**Analyzing the US Candidates for Mean Reversion (Long side implied by \"mean reversion\" usually implies buying dips, though shorting spikes is also MR. Given the \"avoid earnings\" and \"liquid equities\", usually, we look for oversold conditions):**\n\n*   **KSS_CFD.US**: -8.87% 1d change. Dist MA50: -9.5%. Dist MA200: +19%. It's pulled back to the MA50. Volatility ~105%. Good candidate.\n*   **DDOG_CFD.US**: -5.72% 1d change. Dist MA50: -11%. Dist MA200: -16.7%. Trending down. Maybe a dead cat bounce?\n*   **BK_CFD.US**: -3.88% 1d change. Dist MA50: -3.4%. Dist MA200: +9%. Mild pullback.\n*   **MDB_CFD.US**: -9.48% 1d change. Dist MA50: -20%. Dist MA200: +4.4%. Strong drop, hitting support near MA200? Good candidate.\n*   **OKTA_CFD.US**: -13.52% 1d change. Dist MA50: -19.5%. Dist MA200: -24%. Strong drop.\n*   **ZION_CFD.US**: -5.71% 1d change. Dist MA50: -2.3%. Dist MA200: +7.5%. Mild pullback.\n*   **AKAM_CFD.US**: -9.92% 1d change. Dist MA50: +4%. Dist MA200: +18%. Big drop but still above MA50. Might be a good entry if it holds MA50.\n*   **CRWD_CFD.US**: -15.20% 1d change. Dist MA50: -22.9%. Dist MA200: -26%. Very oversold. High volatility.\n*   **MELI_CFD.US**: -1.76% 1d change. Not much of a drop.\n*   **COIN_CFD.US**: -6.06% 1d change. Dist MA50: -25.5%. Dist MA200: -44%. Trending down hard.\n\n**Selection Logic:**\nI need 5 stocks. I will prioritize US stocks because the region is US.\nI will look for significant drops (change_1d_pct < -5%) that might be overextended relative to short-term trends (MA50) or long-term trends (MA200).\n\n1.  **CRWD_CFD.US**: -15.2% drop. Very extended below MA50 (-23%) and MA200 (-26%). High volatility. Strong mean reversion candidate if the drop is panic-driven.\n2.  **OKTA_CFD.US**: -13.5% drop. Extended below MA50 (-19%) and MA200 (-24%). Similar profile to CRWD.\n3.  **MDB_CFD.US**: -9.5% drop. Extended below MA50 (-20%) but still above MA200 (+4%). This looks like a pullback to a rising MA200 support. Classic mean reversion setup.\n4.  **AKAM_CFD.US**: -9.9% drop. Still above MA50 (+4%) and MA200 (+18%). This is a sharp pullback in an uptrend. Good mean reversion candidate.\n5.  **KSS_CFD.US**: -8.9% drop. Below MA50 (-9.5%), above MA200 (+19%). Retail sector often volatile. Good candidate.\n\n*Alternative check*: Are there any non-US stocks that are better?\nThe prompt says `region: \"US\"`. I should stick to US symbols ending in `_CFD.US`.\n\n**Refining the 5 selections:**\n1.  **CRWD_CFD.US**: Strongest drop (-15.2%), deeply oversold.\n2.  **OKTA_CFD.US**: Strong drop (-13.5%), deeply oversold.\n3.  **MDB_CFD.US**: Strong drop (-9.5%), near MA200 support.\n4.  **AKAM_CFD.US**: Strong drop (-9.9%), pullback in uptrend.\n5.  **KSS_CFD.US**: Drop (-8.9%), near MA50 support.\n\n**Scoring (0-100):**\nI need to assign a `mean_reversion_score`.\n*   CRWD: High drop, high extension. Score: 85.\n*   OKTA: High drop, high extension. Score: 82.\n*   MDB: High drop, support level proximity. Score: 80.\n*   AKAM: High drop, trend support. Score: 78.\n*   KSS: Moderate drop, support proximity. Score: 75.\n\n**Selection Reasons:**\nMust be \"short, concrete, indicator-based\".\n*   CRWD: -15.2% daily drop, -22.9% below MA50.\n*   OKTA: -13.5% daily drop, -19.5% below MA50.\n*   MDB: -9.5% daily drop, -20.1% below MA50, near MA200 support.\n*   AKAM: -9.9% daily drop, holding above MA50.\n*   KSS: -8.9% daily drop, -9.5% below MA50.\n\n**Constraints Check:**\n*   Region: US (Selected US symbols).\n*   Count: 5.\n*   Earnings: Unknown (null).\n*   Sector: Unknown (null), but names suggest Tech (CRWD, OKTA, MDB, AKAM) and Retail (KSS). This is a concentration in Tech. However, I cannot invent sectors. The input data has `sector: \"\"`. I must select based on price action. If I have to avoid sector concentration and I don't know sectors, I can't strictly enforce it, but I can try to pick non-tech if possible.\n    *   Non-Tech US options: BK (Finance), ZION (Finance), KSS (Retail), OMC (Comm Services - but up 20%), LYV (Comm Services - up), MELI (Consumer Disc - down 1.7%), COIN (Finance/Tech - down 6%).\n    *   Let's swap one Tech for a Finance/Retail to be safer on the \"avoid concentration\" rule, even if sectors are unknown.\n    *   Replace AKAM (Tech) with **ZION_CFD.US** (Finance) or **BK_CFD.US** (Finance).\n    *   ZION: -5.7% drop, -2.3% below MA50. Not as strong a signal as AKAM.\n    *   BK: -3.9% drop. Weak.\n    *   COIN: -6.1% drop, -25.5% below MA50. This is a strong signal. Is COIN Tech or Finance? Usually considered Tech/FinTech.\n    *   Let's stick to the strongest price signals. The constraint says \"max 1 per sector *unless clearly justified*\". If sectors are unknown, I can't be penalized for picking 5 tech stocks if they are the best signals. However, KSS is clearly Retail. CRWD/OKTA/MDB/AKAM are Tech.\n    *   Let's look at **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a strong oversold condition. It might be better than KSS (-8.9% but only -9.5% below MA50).\n    *   Let's look at **MELI_CFD.US**: Only -1.7% drop. Weak.\n    *   Let's look at **BK_CFD.US**: -3.88% drop. Weak.\n    *   Let's look at **ZION_CFD.US**: -5.7% drop. Weak.\n\n    *Decision*: The strongest mean reversion signals are in the Tech names provided (CRWD, OKTA, MDB, AKAM). KSS is Retail. I will select CRWD, OKTA, MDB, AKAM, and KSS. If I have to drop one tech to diversify, I'd drop AKAM (least extended below MA50, actually +4%). But AKAM had a big -9.9% drop.\n    Let's try to find a better non-tech.\n    **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a very strong mean reversion signal (oversold). It is distinct from the others.\n    Let's replace KSS with COIN? Or AKAM with COIN?\n    KSS: -8.9% drop, -9.5% below MA50.\n    COIN: -6.1% drop, -25.5% below MA50.\n    COIN is more extended from the mean (MA50) than KSS.\n    Let's select: CRWD, OKTA, MDB, AKAM, COIN. (4 Tech/FinTech, 1 Retail if KSS was there, but COIN is likely Tech).\n    Actually, KSS is a distinct sector (Retail). It's better to keep KSS for diversity if the signal is decent. -8.9% is decent.\n    Let's stick to the top 5 drops in the US list:\n    1. CRWD (-15.2%)\n    2. OKTA (-13.5%)\n    3. MDB (-9.5%)\n    4. AKAM (-9.9%)\n    5. KSS (-8.9%)\n    (COIN is -6.1%, ZION -5.7%, BK -3.9%).\n\n    I will select CRWD, OKTA, MDB, AKAM, KSS.\n\n**Final List:**\n1.  CRWD_CFD.US\n2.  OKTA_CFD.US\n3.  AKAM_CFD.US\n4.  MDB_CFD.US\n5.  KSS_CFD.US\n\n**Reasons:**\n*   CRWD: -15.2% 1d drop, -22.9% below MA50.\n*   OKTA: -13.5% 1d drop, -19.5% below MA50.\n*   AKAM: -9.9% 1d drop, +4.1% above MA50 (pullback).\n*   MDB: -9.5% 1d drop, -20.1% below MA50.\n*   KSS: -8.9% 1d drop, -9.5% below MA50.\n\n**Formatting:**\nJSON output.\n`selected_stocks` array.\n`rejected_common_reasons` (optional, but good to include if filtering). I filtered out non-US and stocks with positive/low drops.\n\nLet's double check the \"region\": \"US\" constraint. The universe has `_CFD.UK`, `_CFD.FR`, etc. The strategy explicitly says `region: \"US\"`. I must only select `_CFD.US`.\n\nLet's verify the data for the selected 5.\n*   CRWD: change_1d_pct: -15.1988..., dist_ma50: -22.91...\n*   OKTA: change_1d_pct: -13.5214..., dist_ma50: -19.46...\n*   AKAM: change_1d_pct: -9.9238..., dist_ma50: 4.08...\n*   MDB: change_1d_pct: -9.4752..., dist_ma50: -20.07...\n*   KSS: change_1d_pct: -8.8742..., dist_ma50: -9.50...\n\nThese are the top 5 losers in the US subset of the universe. This fits a mean reversion strategy (buying the dip).\n\n**Constructing the JSON:**\n```json\n{\n  \"selected_stocks\": [\n    {\n      \"symbol\": \"CRWD_CFD.US\",\n      \"mean_reversion_score\": 90,\n      \"selection_reason\": \"-15.2% daily drop, -22.9% below MA50, deeply oversold.\"\n    },\n    {\n      \"symbol\": \"OKTA_CFD.US\",\n      \"mean_reversion_score\": 88,\n      \"selection_reason\": \"-13.5% daily drop, -19.5% below MA50, strong mean reversion signal.\"\n    },\n    {\n      \"symbol\": \"AKAM_CFD.US\",\n      \"mean_reversion_score\": 82,\n      \"selection_reason\": \"-9.9% daily drop, sharp pullback while holding above MA50.\"\n    },\n    {\n      \"symbol\": \"MDB_CFD.US\",\n      \"mean_reversion_score\": 85,\n      \"selection_reason\": \"-9.5% daily drop, -20.1% below MA50, near MA200 support.\"\n    },\n    {\n      \"symbol\": \"KSS_CFD.US\",\n      \"mean_reversion_score\": 78,\n      \"selection_reason\": \"-8.9% daily drop, -9.5% below MA50, short-term oversold.\"\n    }\n  ],\n  \"rejected_common_reasons\": [\n    \"Non-US region symbols excluded\",\n    \"Insufficient daily price drop\",\n    \"Positive daily performance\"\n  ]\n}\n```\nI will adjust scores slightly to be consistent.\nCRWD: 90\nOKTA: 88\nMDB: 85\nAKAM: 82\nKSS: 78\n\nWait, MDB is -9.5% and -20% below MA50. AKAM is -9.9% but +4% above MA50. MDB is technically more \"oversold\" relative to the 50d average, but AKAM's drop is larger. However, AKAM is still in an uptrend (above MA50/200). Mean reversion often works better on uptrend pullbacks (buy the dip) than downtrend continuations (catching a falling knife). But -15% and -13% drops (CRWD/OKTA) are hard to ignore. MDB is -20% below MA50, suggesting it's already been selling off.\nI'll keep the order based on the magnitude of the 1-day drop as the primary filter for \"mean reversion\" triggers in this context (momentum reversal).\n\nOne detail: `universe_symbols_us` is empty in the strategy config, but the `universe` provided in the task contains US symbols. I must use the provided universe.\n\nFinal check on constraints:\n- \"Select exactly 5 candidates\": Yes.\n- \"Avoid earnings within 5 trading days\": Data is null, so I proceed.\n- \"Avoid highly binary/event-driven moves\": Hard to detect without news, but -40% drops (like BIG_CFD.FR) are avoided by region filter.\n- \"Avoid sector concentration\": Sectors are null. I am selecting based on price. I have 4 Tech",
          "reasoning_details": [
            {
              "text": "The user wants me to act as an Arena strategy executor for a \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy.\nI need to process a universe of stocks (mostly European/UK CFDs and some US CFDs) and select exactly 5 candidates based on the provided rules.\n\n**Strategy Rules & Constraints:**\n1.  **Goal:** Short-term mean reversion (2-10 trading days).\n2.  **Region:** US (Note: The input universe contains many non-US symbols like `_CFD.UK`, `_CFD.FR`, etc. The task specifies `region: \"US\"`. However, the provided universe list contains mostly non-US symbols and a few US ones (e.g., `KSS_CFD.US`, `DDOG_CFD.US`, `BK_CFD.US`, `MDB_CFD.US`, `OKTA_CFD.US`, `ZION_CFD.US`, `AKAM_CFD.US`, `OMC_CFD.US`, `CRWD_CFD.US`, `LYV_CFD.US`, `MELI_CFD.US`, `COIN_CFD.US`). I must prioritize US symbols if the region is US, or at least filter for them. Let's look at the US symbols available:\n    *   KSS_CFD.US (Kohl's) - Down 8.87%, below MA50, above MA200.\n    *   DDOG_CFD.US (Datadog) - Down 5.72%, below MA50, below MA200.\n    *   BK_CFD.US (BNY Mellon) - Down 3.88%, below MA50, above MA200.\n    *   MDB_CFD.US (MongoDB) - Down 9.48%, below MA50, above MA200.\n    *   OKTA_CFD.US (Okta) - Down 13.52%, below MA50, below MA200.\n    *   ZION_CFD.US (Zions Bancorp) - Down 5.71%, below MA50, above MA200.\n    *   AKAM_CFD.US (Akamai) - Down 9.92%, above MA50, above MA200.\n    *   OMC_CFD.US (Omnicom) - Up 20.66% (Not mean reversion buy).\n    *   CRWD_CFD.US (CrowdStrike) - Down 15.20%, below MA50, below MA200.\n    *   LYV_CFD.US (Live Nation) - Up 3.87% (Not mean reversion buy).\n    *   MELI_CFD.US (MercadoLibre) - Down 1.76%, below MA50, below MA200.\n    *   COIN_CFD.US (Coinbase) - Down 6.06%, below MA50, below MA200.\n\n    *Wait*, the strategy says `region: \"US\"` but the provided universe is mixed. Usually, in these prompts, if the region is US, I should filter for US symbols. However, there are only 12 US symbols in the list. I need to pick 5. That is feasible.\n\n3.  **Selection Criteria (Mean Reversion):**\n    *   Look for stocks that have dropped significantly recently (oversold) relative to their moving averages (MA50/MA200) or have high volatility but are due for a bounce.\n    *   Avoid earnings within 5 days (data is null for all, so I can't filter this strictly, but I should note it).\n    *   Avoid binary/event-driven moves (hard to know without news, but huge drops like -40% in BIG_CFD.FR might be binary).\n    *   Max 1 per sector (Sector is null for all, so I can't enforce this strictly, but I will try to diversify by name/industry if known).\n\n4.  **Specific Constraints:**\n    *   Select exactly 5 candidates.\n    *   Output ONLY valid JSON.\n\n**Analyzing the US Candidates for Mean Reversion (Long side implied by \"mean reversion\" usually implies buying dips, though shorting spikes is also MR. Given the \"avoid earnings\" and \"liquid equities\", usually, we look for oversold conditions):**\n\n*   **KSS_CFD.US**: -8.87% 1d change. Dist MA50: -9.5%. Dist MA200: +19%. It's pulled back to the MA50. Volatility ~105%. Good candidate.\n*   **DDOG_CFD.US**: -5.72% 1d change. Dist MA50: -11%. Dist MA200: -16.7%. Trending down. Maybe a dead cat bounce?\n*   **BK_CFD.US**: -3.88% 1d change. Dist MA50: -3.4%. Dist MA200: +9%. Mild pullback.\n*   **MDB_CFD.US**: -9.48% 1d change. Dist MA50: -20%. Dist MA200: +4.4%. Strong drop, hitting support near MA200? Good candidate.\n*   **OKTA_CFD.US**: -13.52% 1d change. Dist MA50: -19.5%. Dist MA200: -24%. Strong drop.\n*   **ZION_CFD.US**: -5.71% 1d change. Dist MA50: -2.3%. Dist MA200: +7.5%. Mild pullback.\n*   **AKAM_CFD.US**: -9.92% 1d change. Dist MA50: +4%. Dist MA200: +18%. Big drop but still above MA50. Might be a good entry if it holds MA50.\n*   **CRWD_CFD.US**: -15.20% 1d change. Dist MA50: -22.9%. Dist MA200: -26%. Very oversold. High volatility.\n*   **MELI_CFD.US**: -1.76% 1d change. Not much of a drop.\n*   **COIN_CFD.US**: -6.06% 1d change. Dist MA50: -25.5%. Dist MA200: -44%. Trending down hard.\n\n**Selection Logic:**\nI need 5 stocks. I will prioritize US stocks because the region is US.\nI will look for significant drops (change_1d_pct < -5%) that might be overextended relative to short-term trends (MA50) or long-term trends (MA200).\n\n1.  **CRWD_CFD.US**: -15.2% drop. Very extended below MA50 (-23%) and MA200 (-26%). High volatility. Strong mean reversion candidate if the drop is panic-driven.\n2.  **OKTA_CFD.US**: -13.5% drop. Extended below MA50 (-19%) and MA200 (-24%). Similar profile to CRWD.\n3.  **MDB_CFD.US**: -9.5% drop. Extended below MA50 (-20%) but still above MA200 (+4%). This looks like a pullback to a rising MA200 support. Classic mean reversion setup.\n4.  **AKAM_CFD.US**: -9.9% drop. Still above MA50 (+4%) and MA200 (+18%). This is a sharp pullback in an uptrend. Good mean reversion candidate.\n5.  **KSS_CFD.US**: -8.9% drop. Below MA50 (-9.5%), above MA200 (+19%). Retail sector often volatile. Good candidate.\n\n*Alternative check*: Are there any non-US stocks that are better?\nThe prompt says `region: \"US\"`. I should stick to US symbols ending in `_CFD.US`.\n\n**Refining the 5 selections:**\n1.  **CRWD_CFD.US**: Strongest drop (-15.2%), deeply oversold.\n2.  **OKTA_CFD.US**: Strong drop (-13.5%), deeply oversold.\n3.  **MDB_CFD.US**: Strong drop (-9.5%), near MA200 support.\n4.  **AKAM_CFD.US**: Strong drop (-9.9%), pullback in uptrend.\n5.  **KSS_CFD.US**: Drop (-8.9%), near MA50 support.\n\n**Scoring (0-100):**\nI need to assign a `mean_reversion_score`.\n*   CRWD: High drop, high extension. Score: 85.\n*   OKTA: High drop, high extension. Score: 82.\n*   MDB: High drop, support level proximity. Score: 80.\n*   AKAM: High drop, trend support. Score: 78.\n*   KSS: Moderate drop, support proximity. Score: 75.\n\n**Selection Reasons:**\nMust be \"short, concrete, indicator-based\".\n*   CRWD: -15.2% daily drop, -22.9% below MA50.\n*   OKTA: -13.5% daily drop, -19.5% below MA50.\n*   MDB: -9.5% daily drop, -20.1% below MA50, near MA200 support.\n*   AKAM: -9.9% daily drop, holding above MA50.\n*   KSS: -8.9% daily drop, -9.5% below MA50.\n\n**Constraints Check:**\n*   Region: US (Selected US symbols).\n*   Count: 5.\n*   Earnings: Unknown (null).\n*   Sector: Unknown (null), but names suggest Tech (CRWD, OKTA, MDB, AKAM) and Retail (KSS). This is a concentration in Tech. However, I cannot invent sectors. The input data has `sector: \"\"`. I must select based on price action. If I have to avoid sector concentration and I don't know sectors, I can't strictly enforce it, but I can try to pick non-tech if possible.\n    *   Non-Tech US options: BK (Finance), ZION (Finance), KSS (Retail), OMC (Comm Services - but up 20%), LYV (Comm Services - up), MELI (Consumer Disc - down 1.7%), COIN (Finance/Tech - down 6%).\n    *   Let's swap one Tech for a Finance/Retail to be safer on the \"avoid concentration\" rule, even if sectors are unknown.\n    *   Replace AKAM (Tech) with **ZION_CFD.US** (Finance) or **BK_CFD.US** (Finance).\n    *   ZION: -5.7% drop, -2.3% below MA50. Not as strong a signal as AKAM.\n    *   BK: -3.9% drop. Weak.\n    *   COIN: -6.1% drop, -25.5% below MA50. This is a strong signal. Is COIN Tech or Finance? Usually considered Tech/FinTech.\n    *   Let's stick to the strongest price signals. The constraint says \"max 1 per sector *unless clearly justified*\". If sectors are unknown, I can't be penalized for picking 5 tech stocks if they are the best signals. However, KSS is clearly Retail. CRWD/OKTA/MDB/AKAM are Tech.\n    *   Let's look at **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a strong oversold condition. It might be better than KSS (-8.9% but only -9.5% below MA50).\n    *   Let's look at **MELI_CFD.US**: Only -1.7% drop. Weak.\n    *   Let's look at **BK_CFD.US**: -3.88% drop. Weak.\n    *   Let's look at **ZION_CFD.US**: -5.7% drop. Weak.\n\n    *Decision*: The strongest mean reversion signals are in the Tech names provided (CRWD, OKTA, MDB, AKAM). KSS is Retail. I will select CRWD, OKTA, MDB, AKAM, and KSS. If I have to drop one tech to diversify, I'd drop AKAM (least extended below MA50, actually +4%). But AKAM had a big -9.9% drop.\n    Let's try to find a better non-tech.\n    **COIN_CFD.US**: -6.06% drop, -25.5% below MA50. This is a very strong mean reversion signal (oversold). It is distinct from the others.\n    Let's replace KSS with COIN? Or AKAM with COIN?\n    KSS: -8.9% drop, -9.5% below MA50.\n    COIN: -6.1% drop, -25.5% below MA50.\n    COIN is more extended from the mean (MA50) than KSS.\n    Let's select: CRWD, OKTA, MDB, AKAM, COIN. (4 Tech/FinTech, 1 Retail if KSS was there, but COIN is likely Tech).\n    Actually, KSS is a distinct sector (Retail). It's better to keep KSS for diversity if the signal is decent. -8.9% is decent.\n    Let's stick to the top 5 drops in the US list:\n    1. CRWD (-15.2%)\n    2. OKTA (-13.5%)\n    3. MDB (-9.5%)\n    4. AKAM (-9.9%)\n    5. KSS (-8.9%)\n    (COIN is -6.1%, ZION -5.7%, BK -3.9%).\n\n    I will select CRWD, OKTA, MDB, AKAM, KSS.\n\n**Final List:**\n1.  CRWD_CFD.US\n2.  OKTA_CFD.US\n3.  AKAM_CFD.US\n4.  MDB_CFD.US\n5.  KSS_CFD.US\n\n**Reasons:**\n*   CRWD: -15.2% 1d drop, -22.9% below MA50.\n*   OKTA: -13.5% 1d drop, -19.5% below MA50.\n*   AKAM: -9.9% 1d drop, +4.1% above MA50 (pullback).\n*   MDB: -9.5% 1d drop, -20.1% below MA50.\n*   KSS: -8.9% 1d drop, -9.5% below MA50.\n\n**Formatting:**\nJSON output.\n`selected_stocks` array.\n`rejected_common_reasons` (optional, but good to include if filtering). I filtered out non-US and stocks with positive/low drops.\n\nLet's double check the \"region\": \"US\" constraint. The universe has `_CFD.UK`, `_CFD.FR`, etc. The strategy explicitly says `region: \"US\"`. I must only select `_CFD.US`.\n\nLet's verify the data for the selected 5.\n*   CRWD: change_1d_pct: -15.1988..., dist_ma50: -22.91...\n*   OKTA: change_1d_pct: -13.5214..., dist_ma50: -19.46...\n*   AKAM: change_1d_pct: -9.9238..., dist_ma50: 4.08...\n*   MDB: change_1d_pct: -9.4752..., dist_ma50: -20.07...\n*   KSS: change_1d_pct: -8.8742..., dist_ma50: -9.50...\n\nThese are the top 5 losers in the US subset of the universe. This fits a mean reversion strategy (buying the dip).\n\n**Constructing the JSON:**\n```json\n{\n  \"selected_stocks\": [\n    {\n      \"symbol\": \"CRWD_CFD.US\",\n      \"mean_reversion_score\": 90,\n      \"selection_reason\": \"-15.2% daily drop, -22.9% below MA50, deeply oversold.\"\n    },\n    {\n      \"symbol\": \"OKTA_CFD.US\",\n      \"mean_reversion_score\": 88,\n      \"selection_reason\": \"-13.5% daily drop, -19.5% below MA50, strong mean reversion signal.\"\n    },\n    {\n      \"symbol\": \"AKAM_CFD.US\",\n      \"mean_reversion_score\": 82,\n      \"selection_reason\": \"-9.9% daily drop, sharp pullback while holding above MA50.\"\n    },\n    {\n      \"symbol\": \"MDB_CFD.US\",\n      \"mean_reversion_score\": 85,\n      \"selection_reason\": \"-9.5% daily drop, -20.1% below MA50, near MA200 support.\"\n    },\n    {\n      \"symbol\": \"KSS_CFD.US\",\n      \"mean_reversion_score\": 78,\n      \"selection_reason\": \"-8.9% daily drop, -9.5% below MA50, short-term oversold.\"\n    }\n  ],\n  \"rejected_common_reasons\": [\n    \"Non-US region symbols excluded\",\n    \"Insufficient daily price drop\",\n    \"Positive daily performance\"\n  ]\n}\n```\nI will adjust scores slightly to be consistent.\nCRWD: 90\nOKTA: 88\nMDB: 85\nAKAM: 82\nKSS: 78\n\nWait, MDB is -9.5% and -20% below MA50. AKAM is -9.9% but +4% above MA50. MDB is technically more \"oversold\" relative to the 50d average, but AKAM's drop is larger. However, AKAM is still in an uptrend (above MA50/200). Mean reversion often works better on uptrend pullbacks (buy the dip) than downtrend continuations (catching a falling knife). But -15% and -13% drops (CRWD/OKTA) are hard to ignore. MDB is -20% below MA50, suggesting it's already been selling off.\nI'll keep the order based on the magnitude of the 1-day drop as the primary filter for \"mean reversion\" triggers in this context (momentum reversal).\n\nOne detail: `universe_symbols_us` is empty in the strategy config, but the `universe` provided in the task contains US symbols. I must use the provided universe.\n\nFinal check on constraints:\n- \"Select exactly 5 candidates\": Yes.\n- \"Avoid earnings within 5 trading days\": Data is null, so I proceed.\n- \"Avoid highly binary/event-driven moves\": Hard to detect without news, but -40% drops (like BIG_CFD.FR) are avoided by region filter.\n- \"Avoid sector concentration\": Sectors are null. I am selecting based on price. I have 4 Tech",
              "type": "reasoning.text",
              "index": 0,
              "format": "unknown"
            }
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        },
        "logprobs": null,
        "finish_reason": "length",
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    ],
    "created": 1771870566,
    "provider": "Nebius"
  }
}