Arena run #435

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": "PAGE_CFD.UK", "name": "PAGEGROUP PLC", "sector": "", "last_price": 205.8, "change_1d_pct": -13.091216216216218, "distance_from_ma200_pct": -20.507377691735872, "distance_from_ma50_pct": -9.456474873951793, "volatility_20d_pct": 113.15149478988747, "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": "MCG_CFD.UK", "name": "MOBICO GROUP PLC", "sector": "", "last_price": 30.54, "change_1d_pct": 21.867517956903427, "distance_from_ma200_pct": 10.17176911546931, "distance_from_ma50_pct": 28.667486813057195, "volatility_20d_pct": 211.42235702559637, "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": "UTG_CFD.UK", "name": "UNITE GROUP PLC/THE", "sector": "", "last_price": 567.5, "change_1d_pct": 3.4639927073837784, "distance_from_ma200_pct": -23.929923575376698, "distance_from_ma50_pct": -3.6875042216270137, "volatility_20d_pct": 73.84223442053181, "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": "TRN_CFD.UK", "name": "TRAINLINE PLC", "sector": "", "last_price": 206.0, "change_1d_pct": -12.93322062552832, "distance_from_ma200_pct": -22.557701091522887, "distance_from_ma50_pct": -12.560104316644482, "volatility_20d_pct": 131.09279103846345, "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": "DLN_CFD.UK", "name": "DERWENT LONDON PLC", "sector": "", "last_price": 1953.0, "change_1d_pct": 10.401356698699837, "distance_from_ma200_pct": 5.069478604951705, "distance_from_ma50_pct": 12.150447212111025, "volatility_20d_pct": 115.39088732649292, "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": "STAN_CFD.UK", "name": "", "sector": "", "last_price": 1842.0, "change_1d_pct": 0.3541269408880332, "distance_from_ma200_pct": 31.43986196388351, "distance_from_ma50_pct": 4.292113036344269, "volatility_20d_pct": 75.84142956557304, "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": "SGRO_CFD.UK", "name": "", "sector": "", "last_price": 838.6, "change_1d_pct": 14.312977099236646, "distance_from_ma200_pct": 23.337223471135403, "distance_from_ma50_pct": 14.881685289390623, "volatility_20d_pct": 61.79400737887707, "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": "RHIM_CFD.UK", "name": "RHI MAGNESITA NV", "sector": "", "last_price": 2960.0, "change_1d_pct": 9.023941068139973, "distance_from_ma200_pct": 12.939755835694466, "distance_from_ma50_pct": 17.95826987098712, "volatility_20d_pct": 137.0123103901981, "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": "BBY_CFD.UK", "name": "BALFOUR BEATTY PLC", "sector": "", "last_price": 759.18, "change_1d_pct": 5.222453222453205, "distance_from_ma200_pct": 33.313724414201815, "distance_from_ma50_pct": 8.832962232591, "volatility_20d_pct": 54.25956465831767, "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": "JCQ_CFD.FR", "name": "", "sector": "", "last_price": 23.15, "change_1d_pct": -2.7310924369748024, "distance_from_ma200_pct": 12.41703173875155, "distance_from_ma50_pct": 2.726261802426566, "volatility_20d_pct": 60.277859704178006, "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": "SHC_CFD.UK", "name": "SHAFTESBURY CAPITAL PLC", "sector": "", "last_price": 147.2, "change_1d_pct": 2.364394993045882, "distance_from_ma200_pct": 5.210906053879838, "distance_from_ma50_pct": 3.8182841489510233, "volatility_20d_pct": 60.09537427211038, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": 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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": "GO_CFD.US", "name": "Grocery Outlet Holding Corp", "sector": "", "last_price": 6.95, "change_1d_pct": -29.726996966632967, "distance_from_ma200_pct": -47.21211614853522, "distance_from_ma50_pct": -28.605181517473753, "volatility_20d_pct": 144.92553547550253, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, 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{"symbol": "GXI_CFD.DE", "name": "GERRESHEIMER AG", "sector": "", "last_price": 17.966, "change_1d_pct": 14.725415070242654, "distance_from_ma200_pct": -49.1749846210904, "distance_from_ma50_pct": -24.764359931724258, "volatility_20d_pct": 234.08732547419734, "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": "KBX_CFD.DE", "name": "", "sector": "", "last_price": 107.0, "change_1d_pct": 1.904761904761898, "distance_from_ma200_pct": 19.320630704320706, "distance_from_ma50_pct": 4.476638437795, "volatility_20d_pct": 49.68442531513264, "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": "BESI_CFD.NL", "name": "BE SEMICONDUCTOR INDUSTRIES", "sector": "", "last_price": 193.44, "change_1d_pct": 8.4913067863152, "distance_from_ma200_pct": 41.10400314770468, "distance_from_ma50_pct": 15.007928198524256, "volatility_20d_pct": 85.67853186666386, "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": "ASM_CFD.NL", "name": "ASM International NV", "sector": "", "last_price": 725.982, "change_1d_pct": 3.7677488701041373, "distance_from_ma200_pct": 35.30853907513016, "distance_from_ma50_pct": 9.053716836595882, "volatility_20d_pct": 69.29437779862882, "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": "FBIN_CFD.US", "name": "Fortune Brands Innovations Inc.", "sector": "", "last_price": 49.95, "change_1d_pct": -8.432630614115478, "distance_from_ma200_pct": -6.939222649074228, "distance_from_ma50_pct": -10.202570058175075, "volatility_20d_pct": 139.31617121917984, "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": "KGX_CFD.DE", "name": "KION GROUP AG", "sector": "", "last_price": 52.95, "change_1d_pct": -0.656660412757959, "distance_from_ma200_pct": -7.3274334424576875, "distance_from_ma50_pct": -16.48971001820666, "volatility_20d_pct": 88.527731407205, "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": "NXT_CFD.UK", "name": "NEXT PLC", "sector": "", "last_price": 13225.0, "change_1d_pct": -5.603140613847257, "distance_from_ma200_pct": 4.116518358426768, "distance_from_ma50_pct": -3.780207539944458, "volatility_20d_pct": 72.99366321435181, "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": "DELL_CFD.US", "name": "DELL TECHNOLOGIES -C", "sector": "", "last_price": 148.16, "change_1d_pct": 0.6521739130434856, "distance_from_ma200_pct": 14.351604943607587, "distance_from_ma50_pct": 20.369557940848626, "volatility_20d_pct": 141.23660705754838, "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": "SGL_CFD.DE", "name": "SGL Carbon SE", "sector": "", "last_price": 3.99, "change_1d_pct": 3.475103734439844, "distance_from_ma200_pct": 15.838186537222088, "distance_from_ma50_pct": 2.856789321454545, "volatility_20d_pct": 83.02913938763913, "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": "GFT_CFD.DE", "name": "", "sector": "", "last_price": 18.054, "change_1d_pct": 21.005361930294896, "distance_from_ma200_pct": -5.472950703154222, "distance_from_ma50_pct": -2.2658472891683767, "volatility_20d_pct": 91.79751049921435, "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": "DCC_CFD.UK", "name": "DCC PLC", "sector": "", "last_price": 4880.0, "change_1d_pct": 12.235510579576815, "distance_from_ma200_pct": 1.3673868483688123, "distance_from_ma50_pct": 1.2134077235287588, "volatility_20d_pct": 97.80708435693202, "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": "LGEN_CFD.UK", "name": "", "sector": "", "last_price": 262.78, "change_1d_pct": -4.3392792136876635, "distance_from_ma200_pct": 4.419531435333424, "distance_from_ma50_pct": -0.9270883995189272, "volatility_20d_pct": 51.67078455235858, "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": "AML_CFD.UK", "name": "ASTON MARTIN LAGONDA GLOBAL", "sector": "", "last_price": 45.17, "change_1d_pct": -14.853911404335529, "distance_from_ma200_pct": -34.955064706534586, "distance_from_ma50_pct": -24.17986007600478, "volatility_20d_pct": 124.9610443191448, "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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  "raw": {
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          "reasoning": "The user wants me to execute a \"stage1_universe_scan\" based on a specific mean-reversion strategy (\"meanrev-v1\").\n\n**Strategy Rules:**\n*   **Goal:** Short-term mean reversion in liquid equities.\n*   **Horizon:** 2-10 trading days.\n*   **Max Positions:** 10.\n*   **Max Buys per Run:** 2.\n*   **Max Sells per Run:** 2.\n*   **Region:** US (Note: The universe provided contains a mix of US, UK, DE, FR, NL symbols. The strategy says `region: \"US\"`, but the `universe_symbols_us` is empty and the input universe contains many non-US symbols. However, the prompt says \"You are a constrained selector\". Usually, if the region is specified as US, I should prioritize US symbols. But looking at the universe, there are only 3 US symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`. Wait, let me check the list again.\n    *   US Symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`.\n    *   The rest are UK (.UK), DE (.DE), FR (.FR), NL (.NL).\n    *   The prompt says `region: \"US\"`. This is a strict constraint. I should ideally filter for US symbols. However, often in these Arena tasks, if the universe provided is mixed and the region is specified, I should stick to the region. Let's look at the US candidates.\n        *   `GO_CFD.US`: -29.7% 1d change, -47% vs MA200, -28% vs MA50. High volatility (145%). Strong mean reversion candidate (oversold).\n        *   `DKNG_CFD.US`: +7.4% 1d change, -30% vs MA200, -13% vs MA50. Not a pure mean reversion buy (it's up today), but might be a short if overextended? It's below MA200, so maybe a buy on pullback? But 1d change is positive.\n        *   `GE_CFD.US`: -2% 1d change, +15% vs MA200. Not mean reversion.\n        *   `JBLU_CFD.US`: -14.6% 1d change, -2% vs MA200, -10% vs MA50. Strong oversold candidate.\n        *   `BWXT_CFD.US`: -1.9% 1d change, +15% vs MA200. Not mean reversion.\n        *   `DELL_CFD.US`: +0.6% 1d change, +14% vs MA200. Not mean reversion.\n        *   `FBIN_CFD.US`: -8.4% 1d change, -6.9% vs MA200, -10% vs MA50. Oversold candidate.\n\n    *   If I strictly follow `region: \"US\"`, I have 3 candidates: `GO`, `JBLU`, `FBIN`. The task asks to \"Select exactly 5 candidates\". This is a conflict. \"Region: US\" vs \"Select exactly 5\".\n    *   Usually, in these prompts, if the universe is provided explicitly, I should select from the provided universe. If the region constraint makes it impossible to select 5, I might need to relax the region constraint or interpret \"region: US\" as a preference or a mistake in the prompt context vs data provided. Given the instruction \"You must follow the strategy rules strictly\", I should try to respect the region. But \"Select exactly 5 candidates\" is a hard constraint in `selection_rules`.\n    *   Let's look at the non-US symbols. There are many UK/EU symbols. Maybe the user wants me to trade the provided universe regardless of the region tag, or the region tag is a legacy field. However, `universe_symbols_us` is empty.\n    *   Let's assume I should prioritize US, but if I can't find 5, I might need to look at the others or just pick the best 5 from the whole list if the region constraint seems secondary to the \"Select exactly 5\" rule in the context of a specific scan task.\n    *   Actually, looking at the `universe` list, it's heavily UK/EU. If I only pick US, I fail the \"Select exactly 5\" rule. If I pick 5 from the whole list, I fail the \"Region: US\" rule.\n    *   Let's re-read the prompt: `region: \"US\"`. `universe_symbols_us: []`.\n    *   Hypothesis: The user provided a mixed universe but set the region to US. I should probably prioritize US symbols. If there aren't enough US symbols to meet the \"Select exactly 5\" requirement, I will select the available US symbols and then the best fitting non-US symbols to reach 5, or just select the best 5 from the whole list if the region constraint is considered a filter that resulted in a smaller list.\n    *   However, usually \"Region\" is a hard filter. Let's check the US candidates again.\n        1.  `GO_CFD.US`: -29.7% (1d), -47% (MA200), -28% (MA50). Very oversold. High vol.\n        2.  `JBLU_CFD.US`: -14.6% (1d), -2% (MA200), -10% (MA50). Oversold.\n        3.  `FBIN_CFD.US`: -8.4% (1d), -6.9% (MA200), -10% (MA50). Oversold.\n    *   That's only 3.\n    *   Let's look at the UK/EU list for strong mean reversion signals (large negative 1d change, significantly below MA50/MA200).\n        *   `PAGE_CFD.UK`: -13.1% (1d), -20.5% (MA200), -9.4% (MA50). Strong candidate.\n        *   `TRN_CFD.UK`: -12.9% (1d), -22.5% (MA200), -12.5% (MA50). Strong candidate.\n        *   `FUTR_CFD.UK`: -20.8% (1d), -37.6% (MA200), -24.3% (MA50). Very strong candidate.\n        *   `OCDO_CFD.UK`: -21.3% (1d), -14% (MA200), -3% (MA50). Strong candidate.\n        *   `PZC_CFD.UK`: -10.3% (1d), +8% (MA200), +8% (MA50). This is a pullback from highs, not a deep oversold relative to MA200, but decent 1d drop.\n        *   `AML_CFD.UK`: -14.8% (1d), -34% (MA200), -24% (MA50). Strong candidate.\n        *   `NXT_CFD.UK`: -5.6% (1d), +4% (MA200), -3.7% (MA50).\n        *   `PSN_CFD.UK`: -5.8% (1d), +7% (MA200), -3.4% (MA50).\n        *   `LGEN_CFD.UK`: -4.3% (1d), +4% (MA200), -0.9% (MA50).\n    *   Decision: The `region: \"US\"` constraint is likely a strict rule. However, the `selection_rules` say \"Select exactly 5 candidates\". If I can only find 3 US candidates, I cannot satisfy both. In Arena strategy execution, usually, the \"Select exactly N\" rule is the output format requirement, while \"Region\" is a filter. If the filter yields < N, I should probably output the filtered ones or fill the rest with the next best fits from the broader universe if the system implies a \"scan of provided universe\". Given the universe is explicitly provided in the JSON, I will assume the user wants me to pick 5 from *this* list, but the strategy prefers US. Since there are only 3 US, I will pick those 3 and the top 2 non-US that fit the mean reversion criteria best.\n    *   Wait, looking at the prompt again: `universe_symbols_us: []`. This suggests the strategy *should* be scanning US symbols, but the input data is a mixed bag. This is a common scenario in testing. I will prioritize the US symbols for the \"buys\" (mean reversion usually implies buying oversold).\n    *   Let's refine the \"Mean Reversion\" logic.\n        *   Target: Short-term mean reversion (buying the dip).\n        *   Indicators: Large negative 1-day change (`change_1d_pct`), Price significantly below MA50 (`distance_from_ma50_pct`), Price significantly below MA200 (`distance_from_ma200_pct`).\n        *   Constraints: Avoid earnings (unknown here), avoid binary events (unknown here), max 1 per sector (sectors unknown, so I can't enforce strictly, but I'll try to diversify by name/symbol if possible).\n\n    *   **Candidate Analysis:**\n\n        **US Candidates (Priority):**\n        1.  **GO_CFD.US (Grocery Outlet)**:\n            *   1d: -29.7% (Huge drop)\n            *   MA50: -28.6%\n            *   MA200: -47.2%\n            *   Vol: 144%\n            *   Score: Very high. Deeply oversold.\n        2.  **JBLU_CFD.US (JetBlue)**:\n            *   1d: -14.6%\n            *   MA50: -10.8%\n            *   MA200: -2.4%\n            *   Vol: 135%\n            *   Score: High. Good drop, below averages.\n        3.  **FBIN_CFD.US (Fortune Brands)**:\n            *   1d: -8.4%\n            *   MA50: -10.2%\n            *   MA200: -6.9%\n            *   Vol: 139%\n            *   Score: Moderate/High. Below averages.\n\n        **Non-US Candidates (To fill to 5):**\n        4.  **FUTR_CFD.UK (Future PLC)**:\n            *   1d: -20.8%\n            *   MA50: -24.3%\n            *   MA200: -37.7%\n            *   Vol: 153%\n            *   Score: Very high. Deeply oversold.\n        5.  **AML_CFD.UK (Aston Martin)**:\n            *   1d: -14.9%\n            *   MA50: -24.2%\n            *   MA200: -35.0%\n            *   Vol: 125%\n            *   Score: Very high. Deeply oversold.\n        6.  **OCDO_CFD.UK (Ocado)**:\n            *   1d: -21.3%\n            *   MA50: -3.2%\n            *   MA200: -14.1%\n            *   Score: High 1d drop, but not as far below MA50/MA200 as FUTR/AML. Still strong.\n        7.  **TRN_CFD.UK (Trainline)**:\n            *   1d: -12.9%\n            *   MA50: -12.6%\n            *   MA200: -22.6%\n            *   Score: High.\n        8.  **PAGE_CFD.UK (Pagegroup)**:\n            *   1d: -13.1%\n            *   MA50: -9.5%\n            *   MA200: -20.5%\n            *   Score: High.\n\n    *   **Selection:**\n        1.  `GO_CFD.US` (US, huge drop)\n        2.  `JBLU_CFD.US` (US, strong drop)\n        3.  `FBIN_CFD.US` (US, decent drop)\n        4.  `FUTR_CFD.UK` (UK, huge drop, deeply oversold)\n        5.  `AML_CFD.UK` (UK, strong drop, deeply oversold)\n\n    *   **Sector Check:**\n        *   GO: Retail/Grocery\n        *   JBLU: Airlines\n        *   FBIN: Home/Security/Consumer?\n        *   FUTR: Media/Tech\n        *   AML: Auto/Luxury\n        *   Sectors seem distinct enough (based on names).\n\n    *   **Scoring (0-100):**\n        *   GO: 95 (Extreme drop)\n        *   FUTR: 90 (Extreme drop)\n        *   AML: 88 (Strong drop)\n        *   JBLU: 85 (Strong drop)\n        *   FBIN: 75 (Moderate drop)\n\n    *   **Reasoning Construction:**\n        *   GO: -29.7% 1d drop, -47% vs MA200, extreme oversold.\n        *   FUTR: -20.8% 1d drop, -37% vs MA200, deeply oversold.\n        *   AML: -14.9% 1d drop, -35% vs MA200, strong mean reversion setup.\n        *   JBLU: -14.6% 1d drop, -10.8% vs MA50, short-term oversold.\n        *   FBIN: -8.4% 1d drop, -10.2% vs MA50, below key averages.\n\n    *   **Refining the Region Constraint:**\n        *   If I strictly follow `region: \"US\"`, I can only pick 3. The prompt says \"Select exactly 5 candidates\". This is a hard constraint in the output schema requirements. Usually, in these tasks, if the universe is provided, I must pick from it. The region field might be metadata. I will proceed with the 3 US + 2 best non-US approach to satisfy the count.\n\n    *   **Final List:**\n        1.  GO_CFD.US\n        2.  FUTR_CFD.UK\n        3.  AML_CFD.UK\n        4.  JBLU_CFD.US\n        5.  FBIN_CFD.US\n\n    *   **Double Check:**\n        *   Are there any better ones?\n        *   `OCDO_CFD.UK`: -21.3% 1d. MA50 is -3.2%. It's a sharp drop but price is closer to MA50 than FUTR/AML. FUTR/AML are structurally weaker (further from MA200), which often implies a better \"dead cat bounce\" or mean reversion potential if the drop isn't a total fundamental collapse (which we can't know, but we assume mean reversion). FUTR is -24% vs MA50, OCDO is -3%. FUTR is more stretched.\n        *   `TRN_CFD.UK`: -12.9% 1d, -12.5% MA50. Good. AML is -14.9% 1d, -24% MA50. AML looks better.\n        *   `PAGE_CFD.UK`: -13.1% 1d, -9.4% MA50. JBLU is -14.6% 1d, -10.8% MA50. JBLU is US, so it wins.\n\n    *   **Formatting:**\n        *   JSON only.\n        *   `selected_stocks` array.\n        *   `symbol`, `mean_reversion_score`, `selection_reason`.\n        *   `rejected_common_reasons` (optional, but good to explain why others were skipped, e.g., \"Region mismatch\", \"Insufficient drop\").\n\n    *   **Rejected Reasons:**\n        *   \"Region mismatch (Non-US)\" for the many UK/EU stocks not selected.\n        *   \"Insufficient price decline\" for stocks like `DCC_CFD.UK` (+12%), `MCG_CFD.UK` (+21%), `DLN_CFD.UK` (+10%).\n        *   \"Above moving averages\" for stocks like `STAN_CFD.UK` (+31% vs MA200).\n\n    *   **Wait, check `DKNG_CFD.US`**:\n        *   +7.4% 1d change. This is a positive move. Mean reversion strategy usually looks for *oversold* (buy) or *overbought* (short). The strategy description says \"short-term mean reversion\". Usually, this implies buying dips in a long-only or long-biased context unless specified. The rules mention `max_buys_per_run` and `max_sells_per_run`. This implies I can do both.\n        *   However, `DKNG` is -30% vs MA200 but +7% today. It's a bounce. Not a mean reversion entry *for a buy*. Is it a short entry? It's +7% today, but -30% vs MA200. It's not overbought.\n        *   So `DKNG` is not a good candidate for either side right now (bouncing off lows).\n\n    *   **Check `GE_CFD.US`**:\n        *   -2% 1d. +15% vs MA200. Not oversold.\n\n    *   **Check `DELL_CFD.US`**:\n        *   +0.6% 1d. +14% vs MA200. Not oversold.\n\n    *   **Check `BWXT_CFD.US`**:\n        *   -1.9% 1d. +15% vs MA200. Not oversold.\n\n    *   So the US list is definitely just GO, JBLU, FBIN.\n\n    *   **Final Selection Logic:**\n        *   Select 3 US stocks with strong",
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            {
              "text": "The user wants me to execute a \"stage1_universe_scan\" based on a specific mean-reversion strategy (\"meanrev-v1\").\n\n**Strategy Rules:**\n*   **Goal:** Short-term mean reversion in liquid equities.\n*   **Horizon:** 2-10 trading days.\n*   **Max Positions:** 10.\n*   **Max Buys per Run:** 2.\n*   **Max Sells per Run:** 2.\n*   **Region:** US (Note: The universe provided contains a mix of US, UK, DE, FR, NL symbols. The strategy says `region: \"US\"`, but the `universe_symbols_us` is empty and the input universe contains many non-US symbols. However, the prompt says \"You are a constrained selector\". Usually, if the region is specified as US, I should prioritize US symbols. But looking at the universe, there are only 3 US symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`. Wait, let me check the list again.\n    *   US Symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`.\n    *   The rest are UK (.UK), DE (.DE), FR (.FR), NL (.NL).\n    *   The prompt says `region: \"US\"`. This is a strict constraint. I should ideally filter for US symbols. However, often in these Arena tasks, if the universe provided is mixed and the region is specified, I should stick to the region. Let's look at the US candidates.\n        *   `GO_CFD.US`: -29.7% 1d change, -47% vs MA200, -28% vs MA50. High volatility (145%). Strong mean reversion candidate (oversold).\n        *   `DKNG_CFD.US`: +7.4% 1d change, -30% vs MA200, -13% vs MA50. Not a pure mean reversion buy (it's up today), but might be a short if overextended? It's below MA200, so maybe a buy on pullback? But 1d change is positive.\n        *   `GE_CFD.US`: -2% 1d change, +15% vs MA200. Not mean reversion.\n        *   `JBLU_CFD.US`: -14.6% 1d change, -2% vs MA200, -10% vs MA50. Strong oversold candidate.\n        *   `BWXT_CFD.US`: -1.9% 1d change, +15% vs MA200. Not mean reversion.\n        *   `DELL_CFD.US`: +0.6% 1d change, +14% vs MA200. Not mean reversion.\n        *   `FBIN_CFD.US`: -8.4% 1d change, -6.9% vs MA200, -10% vs MA50. Oversold candidate.\n\n    *   If I strictly follow `region: \"US\"`, I have 3 candidates: `GO`, `JBLU`, `FBIN`. The task asks to \"Select exactly 5 candidates\". This is a conflict. \"Region: US\" vs \"Select exactly 5\".\n    *   Usually, in these prompts, if the universe is provided explicitly, I should select from the provided universe. If the region constraint makes it impossible to select 5, I might need to relax the region constraint or interpret \"region: US\" as a preference or a mistake in the prompt context vs data provided. Given the instruction \"You must follow the strategy rules strictly\", I should try to respect the region. But \"Select exactly 5 candidates\" is a hard constraint in `selection_rules`.\n    *   Let's look at the non-US symbols. There are many UK/EU symbols. Maybe the user wants me to trade the provided universe regardless of the region tag, or the region tag is a legacy field. However, `universe_symbols_us` is empty.\n    *   Let's assume I should prioritize US, but if I can't find 5, I might need to look at the others or just pick the best 5 from the whole list if the region constraint seems secondary to the \"Select exactly 5\" rule in the context of a specific scan task.\n    *   Actually, looking at the `universe` list, it's heavily UK/EU. If I only pick US, I fail the \"Select exactly 5\" rule. If I pick 5 from the whole list, I fail the \"Region: US\" rule.\n    *   Let's re-read the prompt: `region: \"US\"`. `universe_symbols_us: []`.\n    *   Hypothesis: The user provided a mixed universe but set the region to US. I should probably prioritize US symbols. If there aren't enough US symbols to meet the \"Select exactly 5\" requirement, I will select the available US symbols and then the best fitting non-US symbols to reach 5, or just select the best 5 from the whole list if the region constraint is considered a filter that resulted in a smaller list.\n    *   However, usually \"Region\" is a hard filter. Let's check the US candidates again.\n        1.  `GO_CFD.US`: -29.7% (1d), -47% (MA200), -28% (MA50). Very oversold. High vol.\n        2.  `JBLU_CFD.US`: -14.6% (1d), -2% (MA200), -10% (MA50). Oversold.\n        3.  `FBIN_CFD.US`: -8.4% (1d), -6.9% (MA200), -10% (MA50). Oversold.\n    *   That's only 3.\n    *   Let's look at the UK/EU list for strong mean reversion signals (large negative 1d change, significantly below MA50/MA200).\n        *   `PAGE_CFD.UK`: -13.1% (1d), -20.5% (MA200), -9.4% (MA50). Strong candidate.\n        *   `TRN_CFD.UK`: -12.9% (1d), -22.5% (MA200), -12.5% (MA50). Strong candidate.\n        *   `FUTR_CFD.UK`: -20.8% (1d), -37.6% (MA200), -24.3% (MA50). Very strong candidate.\n        *   `OCDO_CFD.UK`: -21.3% (1d), -14% (MA200), -3% (MA50). Strong candidate.\n        *   `PZC_CFD.UK`: -10.3% (1d), +8% (MA200), +8% (MA50). This is a pullback from highs, not a deep oversold relative to MA200, but decent 1d drop.\n        *   `AML_CFD.UK`: -14.8% (1d), -34% (MA200), -24% (MA50). Strong candidate.\n        *   `NXT_CFD.UK`: -5.6% (1d), +4% (MA200), -3.7% (MA50).\n        *   `PSN_CFD.UK`: -5.8% (1d), +7% (MA200), -3.4% (MA50).\n        *   `LGEN_CFD.UK`: -4.3% (1d), +4% (MA200), -0.9% (MA50).\n    *   Decision: The `region: \"US\"` constraint is likely a strict rule. However, the `selection_rules` say \"Select exactly 5 candidates\". If I can only find 3 US candidates, I cannot satisfy both. In Arena strategy execution, usually, the \"Select exactly N\" rule is the output format requirement, while \"Region\" is a filter. If the filter yields < N, I should probably output the filtered ones or fill the rest with the next best fits from the broader universe if the system implies a \"scan of provided universe\". Given the universe is explicitly provided in the JSON, I will assume the user wants me to pick 5 from *this* list, but the strategy prefers US. Since there are only 3 US, I will pick those 3 and the top 2 non-US that fit the mean reversion criteria best.\n    *   Wait, looking at the prompt again: `universe_symbols_us: []`. This suggests the strategy *should* be scanning US symbols, but the input data is a mixed bag. This is a common scenario in testing. I will prioritize the US symbols for the \"buys\" (mean reversion usually implies buying oversold).\n    *   Let's refine the \"Mean Reversion\" logic.\n        *   Target: Short-term mean reversion (buying the dip).\n        *   Indicators: Large negative 1-day change (`change_1d_pct`), Price significantly below MA50 (`distance_from_ma50_pct`), Price significantly below MA200 (`distance_from_ma200_pct`).\n        *   Constraints: Avoid earnings (unknown here), avoid binary events (unknown here), max 1 per sector (sectors unknown, so I can't enforce strictly, but I'll try to diversify by name/symbol if possible).\n\n    *   **Candidate Analysis:**\n\n        **US Candidates (Priority):**\n        1.  **GO_CFD.US (Grocery Outlet)**:\n            *   1d: -29.7% (Huge drop)\n            *   MA50: -28.6%\n            *   MA200: -47.2%\n            *   Vol: 144%\n            *   Score: Very high. Deeply oversold.\n        2.  **JBLU_CFD.US (JetBlue)**:\n            *   1d: -14.6%\n            *   MA50: -10.8%\n            *   MA200: -2.4%\n            *   Vol: 135%\n            *   Score: High. Good drop, below averages.\n        3.  **FBIN_CFD.US (Fortune Brands)**:\n            *   1d: -8.4%\n            *   MA50: -10.2%\n            *   MA200: -6.9%\n            *   Vol: 139%\n            *   Score: Moderate/High. Below averages.\n\n        **Non-US Candidates (To fill to 5):**\n        4.  **FUTR_CFD.UK (Future PLC)**:\n            *   1d: -20.8%\n            *   MA50: -24.3%\n            *   MA200: -37.7%\n            *   Vol: 153%\n            *   Score: Very high. Deeply oversold.\n        5.  **AML_CFD.UK (Aston Martin)**:\n            *   1d: -14.9%\n            *   MA50: -24.2%\n            *   MA200: -35.0%\n            *   Vol: 125%\n            *   Score: Very high. Deeply oversold.\n        6.  **OCDO_CFD.UK (Ocado)**:\n            *   1d: -21.3%\n            *   MA50: -3.2%\n            *   MA200: -14.1%\n            *   Score: High 1d drop, but not as far below MA50/MA200 as FUTR/AML. Still strong.\n        7.  **TRN_CFD.UK (Trainline)**:\n            *   1d: -12.9%\n            *   MA50: -12.6%\n            *   MA200: -22.6%\n            *   Score: High.\n        8.  **PAGE_CFD.UK (Pagegroup)**:\n            *   1d: -13.1%\n            *   MA50: -9.5%\n            *   MA200: -20.5%\n            *   Score: High.\n\n    *   **Selection:**\n        1.  `GO_CFD.US` (US, huge drop)\n        2.  `JBLU_CFD.US` (US, strong drop)\n        3.  `FBIN_CFD.US` (US, decent drop)\n        4.  `FUTR_CFD.UK` (UK, huge drop, deeply oversold)\n        5.  `AML_CFD.UK` (UK, strong drop, deeply oversold)\n\n    *   **Sector Check:**\n        *   GO: Retail/Grocery\n        *   JBLU: Airlines\n        *   FBIN: Home/Security/Consumer?\n        *   FUTR: Media/Tech\n        *   AML: Auto/Luxury\n        *   Sectors seem distinct enough (based on names).\n\n    *   **Scoring (0-100):**\n        *   GO: 95 (Extreme drop)\n        *   FUTR: 90 (Extreme drop)\n        *   AML: 88 (Strong drop)\n        *   JBLU: 85 (Strong drop)\n        *   FBIN: 75 (Moderate drop)\n\n    *   **Reasoning Construction:**\n        *   GO: -29.7% 1d drop, -47% vs MA200, extreme oversold.\n        *   FUTR: -20.8% 1d drop, -37% vs MA200, deeply oversold.\n        *   AML: -14.9% 1d drop, -35% vs MA200, strong mean reversion setup.\n        *   JBLU: -14.6% 1d drop, -10.8% vs MA50, short-term oversold.\n        *   FBIN: -8.4% 1d drop, -10.2% vs MA50, below key averages.\n\n    *   **Refining the Region Constraint:**\n        *   If I strictly follow `region: \"US\"`, I can only pick 3. The prompt says \"Select exactly 5 candidates\". This is a hard constraint in the output schema requirements. Usually, in these tasks, if the universe is provided, I must pick from it. The region field might be metadata. I will proceed with the 3 US + 2 best non-US approach to satisfy the count.\n\n    *   **Final List:**\n        1.  GO_CFD.US\n        2.  FUTR_CFD.UK\n        3.  AML_CFD.UK\n        4.  JBLU_CFD.US\n        5.  FBIN_CFD.US\n\n    *   **Double Check:**\n        *   Are there any better ones?\n        *   `OCDO_CFD.UK`: -21.3% 1d. MA50 is -3.2%. It's a sharp drop but price is closer to MA50 than FUTR/AML. FUTR/AML are structurally weaker (further from MA200), which often implies a better \"dead cat bounce\" or mean reversion potential if the drop isn't a total fundamental collapse (which we can't know, but we assume mean reversion). FUTR is -24% vs MA50, OCDO is -3%. FUTR is more stretched.\n        *   `TRN_CFD.UK`: -12.9% 1d, -12.5% MA50. Good. AML is -14.9% 1d, -24% MA50. AML looks better.\n        *   `PAGE_CFD.UK`: -13.1% 1d, -9.4% MA50. JBLU is -14.6% 1d, -10.8% MA50. JBLU is US, so it wins.\n\n    *   **Formatting:**\n        *   JSON only.\n        *   `selected_stocks` array.\n        *   `symbol`, `mean_reversion_score`, `selection_reason`.\n        *   `rejected_common_reasons` (optional, but good to explain why others were skipped, e.g., \"Region mismatch\", \"Insufficient drop\").\n\n    *   **Rejected Reasons:**\n        *   \"Region mismatch (Non-US)\" for the many UK/EU stocks not selected.\n        *   \"Insufficient price decline\" for stocks like `DCC_CFD.UK` (+12%), `MCG_CFD.UK` (+21%), `DLN_CFD.UK` (+10%).\n        *   \"Above moving averages\" for stocks like `STAN_CFD.UK` (+31% vs MA200).\n\n    *   **Wait, check `DKNG_CFD.US`**:\n        *   +7.4% 1d change. This is a positive move. Mean reversion strategy usually looks for *oversold* (buy) or *overbought* (short). The strategy description says \"short-term mean reversion\". Usually, this implies buying dips in a long-only or long-biased context unless specified. The rules mention `max_buys_per_run` and `max_sells_per_run`. This implies I can do both.\n        *   However, `DKNG` is -30% vs MA200 but +7% today. It's a bounce. Not a mean reversion entry *for a buy*. Is it a short entry? It's +7% today, but -30% vs MA200. It's not overbought.\n        *   So `DKNG` is not a good candidate for either side right now (bouncing off lows).\n\n    *   **Check `GE_CFD.US`**:\n        *   -2% 1d. +15% vs MA200. Not oversold.\n\n    *   **Check `DELL_CFD.US`**:\n        *   +0.6% 1d. +14% vs MA200. Not oversold.\n\n    *   **Check `BWXT_CFD.US`**:\n        *   -1.9% 1d. +15% vs MA200. Not oversold.\n\n    *   So the US list is definitely just GO, JBLU, FBIN.\n\n    *   **Final Selection Logic:**\n        *   Select 3 US stocks with strong",
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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": "BIG_CFD.FR", "qty": 6622.516556291391, "avg_price": 0.453, "last_price": 0.317, "unrealized_pnl": -900.6622516556292, "opened_at": "2026-02-26T18:07:22.382508+00:00"}, {"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": "CRWD_CFD.US", "qty": 0.0, "avg_price": 349.425, "last_price": 374.67, "unrealized_pnl": 0.0, "opened_at": "2026-02-24T18:14:37.115518+00:00"}, {"symbol": "ERA_CFD.FR", "qty": 0.0, "avg_price": 47.4, "last_price": 52.1, "unrealized_pnl": 0.0, "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": "HIK_CFD.UK", "qty": 2.3529411764705883, "avg_price": 1275.0, "last_price": 1234.0, "unrealized_pnl": -96.47058823529412, "opened_at": "2026-03-02T10:31:26.559356+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": 151.91, "unrealized_pnl": -785.5685131195337, "opened_at": "2026-02-21T09:33:26.900894+00:00"}, {"symbol": "RMV_CFD.UK", "qty": 0.0, "avg_price": 424.3, "last_price": 442.6, "unrealized_pnl": 0.0, "opened_at": "2026-02-21T09:33:26.900894+00:00"}, {"symbol": "THG_CFD.UK", "qty": 85.76329331046313, "avg_price": 34.98, "last_price": 34.98, "unrealized_pnl": 0.0, "opened_at": "2026-02-24T18:14:37.115518+00:00"}, {"symbol": "TRST_CFD.UK", "qty": 0.0, "avg_price": 134.3, "last_price": 139.6, "unrealized_pnl": 0.0, "opened_at": "2026-02-16T17:30:52.485845+00:00"}, {"symbol": "TUI1_CFD.DE", "qty": 405.51500405515003, "avg_price": 7.398000000000001, "last_price": 7.324, "unrealized_pnl": -30.0081103000814, "opened_at": "2026-03-02T10:31:26.559356+00:00"}, {"symbol": "XRX_CFD.US", "qty": 1734.1040462427745, "avg_price": 1.73, "last_price": 1.8199999999999998, "unrealized_pnl": 156.06936416184945, "opened_at": "2026-02-26T18:07:22.382508+00:00"}, {"symbol": "ZS_CFD.US", "qty": 18.778167250876315, "avg_price": 159.76, "last_price": 160.47, "unrealized_pnl": 13.332498748122333, "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": "Position is down ~30%. Mean-reversion strategy dictates holding oversold assets to capture potential bounce.",
        "symbol": "BIG_CFD.FR"
      },
      {
        "reason": "Position is flat (0% PnL). No action required.",
        "symbol": "BYIT_CFD.UK"
      },
      {
        "reason": "Gain of ~3.4% is marginal. Prefering to hold to avoid overtrading unless momentum reverses sharply.",
        "symbol": "FUTR_CFD.UK"
      },
      {
        "reason": "Loss of ~3.2% is within expected volatility for mean reversion. Holding for rebound.",
        "symbol": "HIK_CFD.UK"
      },
      {
        "reason": "Position is flat (0% PnL). No action required.",
        "symbol": "OCDO_CFD.UK"
      },
      {
        "reason": "Position is down ~26%. Deeply oversold; holding for mean reversion.",
        "symbol": "PAGE_CFD.UK"
      },
      {
        "reason": "Position is flat (0% PnL). No action required.",
        "symbol": "THG_CFD.UK"
      },
      {
        "reason": "Loss of ~1% is marginal. Holding for reversion.",
        "symbol": "TUI1_CFD.DE"
      },
      {
        "reason": "Gain of ~0.4% is marginal. Holding.",
        "symbol": "ZS_CFD.US"
      }
    ],
    "sell": [
      {
        "reason": "Position has gained ~5.2%, representing a significant deviation from entry price. Selling to lock in gains aligns with mean-reversion profit-taking rules.",
        "symbol": "XRX_CFD.US"
      }
    ]
  },
  "portfolio_rationale": "The mean-reversion strategy identifies XRX_CFD.US as the primary candidate for exit due to its ~5.2% unrealized gain, which is the strongest positive deviation in the portfolio. No buy actions are executed as the candidates list is empty. Significant losers (BIG_CFD.FR, PAGE_CFD.UK) are retained in anticipation of a price correction back to the mean, consistent with the strategy's logic. Marginal movers are held to minimize churn and transaction costs."
}

{
  "raw": {
    "id": "gen-1772734927-GP63TiExaI7FgMPjN5o4",
    "model": "z-ai/glm-4.7-20251222",
    "usage": {
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      "is_byok": false,
      "cost_details": {
        "upstream_inference_cost": 0.0125877,
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    "object": "chat.completion",
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        "message": {
          "role": "assistant",
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          "refusal": null,
          "reasoning": "The user wants me to execute a \"stage1_universe_scan\" based on a specific mean-reversion strategy (\"meanrev-v1\").\n\n**Strategy Rules:**\n*   **Goal:** Short-term mean reversion in liquid equities.\n*   **Horizon:** 2-10 trading days.\n*   **Max Positions:** 10.\n*   **Max Buys per Run:** 2.\n*   **Max Sells per Run:** 2.\n*   **Region:** US (Note: The universe provided contains a mix of US, UK, DE, FR, NL symbols. The strategy says `region: \"US\"`, but the `universe_symbols_us` is empty and the input universe contains many non-US symbols. However, the prompt says \"You are a constrained selector\". Usually, if the region is specified as US, I should prioritize US symbols. But looking at the universe, there are only 3 US symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`. Wait, let me check the list again.\n    *   US Symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`.\n    *   The rest are UK (.UK), DE (.DE), FR (.FR), NL (.NL).\n    *   The prompt says `region: \"US\"`. This is a strict constraint. I should ideally filter for US symbols. However, often in these Arena tasks, if the universe provided is mixed and the region is specified, I should stick to the region. Let's look at the US candidates.\n        *   `GO_CFD.US`: -29.7% 1d change, -47% vs MA200, -28% vs MA50. High volatility (145%). Strong mean reversion candidate (oversold).\n        *   `DKNG_CFD.US`: +7.4% 1d change, -30% vs MA200, -13% vs MA50. Not a pure mean reversion buy (it's up today), but might be a short if overextended? It's below MA200, so maybe a buy on pullback? But 1d change is positive.\n        *   `GE_CFD.US`: -2% 1d change, +15% vs MA200. Not mean reversion.\n        *   `JBLU_CFD.US`: -14.6% 1d change, -2% vs MA200, -10% vs MA50. Strong oversold candidate.\n        *   `BWXT_CFD.US`: -1.9% 1d change, +15% vs MA200. Not mean reversion.\n        *   `DELL_CFD.US`: +0.6% 1d change, +14% vs MA200. Not mean reversion.\n        *   `FBIN_CFD.US`: -8.4% 1d change, -6.9% vs MA200, -10% vs MA50. Oversold candidate.\n\n    *   If I strictly follow `region: \"US\"`, I have 3 candidates: `GO`, `JBLU`, `FBIN`. The task asks to \"Select exactly 5 candidates\". This is a conflict. \"Region: US\" vs \"Select exactly 5\".\n    *   Usually, in these prompts, if the universe is provided explicitly, I should select from the provided universe. If the region constraint makes it impossible to select 5, I might need to relax the region constraint or interpret \"region: US\" as a preference or a mistake in the prompt context vs data provided. Given the instruction \"You must follow the strategy rules strictly\", I should try to respect the region. But \"Select exactly 5 candidates\" is a hard constraint in `selection_rules`.\n    *   Let's look at the non-US symbols. There are many UK/EU symbols. Maybe the user wants me to trade the provided universe regardless of the region tag, or the region tag is a legacy field. However, `universe_symbols_us` is empty.\n    *   Let's assume I should prioritize US, but if I can't find 5, I might need to look at the others or just pick the best 5 from the whole list if the region constraint seems secondary to the \"Select exactly 5\" rule in the context of a specific scan task.\n    *   Actually, looking at the `universe` list, it's heavily UK/EU. If I only pick US, I fail the \"Select exactly 5\" rule. If I pick 5 from the whole list, I fail the \"Region: US\" rule.\n    *   Let's re-read the prompt: `region: \"US\"`. `universe_symbols_us: []`.\n    *   Hypothesis: The user provided a mixed universe but set the region to US. I should probably prioritize US symbols. If there aren't enough US symbols to meet the \"Select exactly 5\" requirement, I will select the available US symbols and then the best fitting non-US symbols to reach 5, or just select the best 5 from the whole list if the region constraint is considered a filter that resulted in a smaller list.\n    *   However, usually \"Region\" is a hard filter. Let's check the US candidates again.\n        1.  `GO_CFD.US`: -29.7% (1d), -47% (MA200), -28% (MA50). Very oversold. High vol.\n        2.  `JBLU_CFD.US`: -14.6% (1d), -2% (MA200), -10% (MA50). Oversold.\n        3.  `FBIN_CFD.US`: -8.4% (1d), -6.9% (MA200), -10% (MA50). Oversold.\n    *   That's only 3.\n    *   Let's look at the UK/EU list for strong mean reversion signals (large negative 1d change, significantly below MA50/MA200).\n        *   `PAGE_CFD.UK`: -13.1% (1d), -20.5% (MA200), -9.4% (MA50). Strong candidate.\n        *   `TRN_CFD.UK`: -12.9% (1d), -22.5% (MA200), -12.5% (MA50). Strong candidate.\n        *   `FUTR_CFD.UK`: -20.8% (1d), -37.6% (MA200), -24.3% (MA50). Very strong candidate.\n        *   `OCDO_CFD.UK`: -21.3% (1d), -14% (MA200), -3% (MA50). Strong candidate.\n        *   `PZC_CFD.UK`: -10.3% (1d), +8% (MA200), +8% (MA50). This is a pullback from highs, not a deep oversold relative to MA200, but decent 1d drop.\n        *   `AML_CFD.UK`: -14.8% (1d), -34% (MA200), -24% (MA50). Strong candidate.\n        *   `NXT_CFD.UK`: -5.6% (1d), +4% (MA200), -3.7% (MA50).\n        *   `PSN_CFD.UK`: -5.8% (1d), +7% (MA200), -3.4% (MA50).\n        *   `LGEN_CFD.UK`: -4.3% (1d), +4% (MA200), -0.9% (MA50).\n    *   Decision: The `region: \"US\"` constraint is likely a strict rule. However, the `selection_rules` say \"Select exactly 5 candidates\". If I can only find 3 US candidates, I cannot satisfy both. In Arena strategy execution, usually, the \"Select exactly N\" rule is the output format requirement, while \"Region\" is a filter. If the filter yields < N, I should probably output the filtered ones or fill the rest with the next best fits from the broader universe if the system implies a \"scan of provided universe\". Given the universe is explicitly provided in the JSON, I will assume the user wants me to pick 5 from *this* list, but the strategy prefers US. Since there are only 3 US, I will pick those 3 and the top 2 non-US that fit the mean reversion criteria best.\n    *   Wait, looking at the prompt again: `universe_symbols_us: []`. This suggests the strategy *should* be scanning US symbols, but the input data is a mixed bag. This is a common scenario in testing. I will prioritize the US symbols for the \"buys\" (mean reversion usually implies buying oversold).\n    *   Let's refine the \"Mean Reversion\" logic.\n        *   Target: Short-term mean reversion (buying the dip).\n        *   Indicators: Large negative 1-day change (`change_1d_pct`), Price significantly below MA50 (`distance_from_ma50_pct`), Price significantly below MA200 (`distance_from_ma200_pct`).\n        *   Constraints: Avoid earnings (unknown here), avoid binary events (unknown here), max 1 per sector (sectors unknown, so I can't enforce strictly, but I'll try to diversify by name/symbol if possible).\n\n    *   **Candidate Analysis:**\n\n        **US Candidates (Priority):**\n        1.  **GO_CFD.US (Grocery Outlet)**:\n            *   1d: -29.7% (Huge drop)\n            *   MA50: -28.6%\n            *   MA200: -47.2%\n            *   Vol: 144%\n            *   Score: Very high. Deeply oversold.\n        2.  **JBLU_CFD.US (JetBlue)**:\n            *   1d: -14.6%\n            *   MA50: -10.8%\n            *   MA200: -2.4%\n            *   Vol: 135%\n            *   Score: High. Good drop, below averages.\n        3.  **FBIN_CFD.US (Fortune Brands)**:\n            *   1d: -8.4%\n            *   MA50: -10.2%\n            *   MA200: -6.9%\n            *   Vol: 139%\n            *   Score: Moderate/High. Below averages.\n\n        **Non-US Candidates (To fill to 5):**\n        4.  **FUTR_CFD.UK (Future PLC)**:\n            *   1d: -20.8%\n            *   MA50: -24.3%\n            *   MA200: -37.7%\n            *   Vol: 153%\n            *   Score: Very high. Deeply oversold.\n        5.  **AML_CFD.UK (Aston Martin)**:\n            *   1d: -14.9%\n            *   MA50: -24.2%\n            *   MA200: -35.0%\n            *   Vol: 125%\n            *   Score: Very high. Deeply oversold.\n        6.  **OCDO_CFD.UK (Ocado)**:\n            *   1d: -21.3%\n            *   MA50: -3.2%\n            *   MA200: -14.1%\n            *   Score: High 1d drop, but not as far below MA50/MA200 as FUTR/AML. Still strong.\n        7.  **TRN_CFD.UK (Trainline)**:\n            *   1d: -12.9%\n            *   MA50: -12.6%\n            *   MA200: -22.6%\n            *   Score: High.\n        8.  **PAGE_CFD.UK (Pagegroup)**:\n            *   1d: -13.1%\n            *   MA50: -9.5%\n            *   MA200: -20.5%\n            *   Score: High.\n\n    *   **Selection:**\n        1.  `GO_CFD.US` (US, huge drop)\n        2.  `JBLU_CFD.US` (US, strong drop)\n        3.  `FBIN_CFD.US` (US, decent drop)\n        4.  `FUTR_CFD.UK` (UK, huge drop, deeply oversold)\n        5.  `AML_CFD.UK` (UK, strong drop, deeply oversold)\n\n    *   **Sector Check:**\n        *   GO: Retail/Grocery\n        *   JBLU: Airlines\n        *   FBIN: Home/Security/Consumer?\n        *   FUTR: Media/Tech\n        *   AML: Auto/Luxury\n        *   Sectors seem distinct enough (based on names).\n\n    *   **Scoring (0-100):**\n        *   GO: 95 (Extreme drop)\n        *   FUTR: 90 (Extreme drop)\n        *   AML: 88 (Strong drop)\n        *   JBLU: 85 (Strong drop)\n        *   FBIN: 75 (Moderate drop)\n\n    *   **Reasoning Construction:**\n        *   GO: -29.7% 1d drop, -47% vs MA200, extreme oversold.\n        *   FUTR: -20.8% 1d drop, -37% vs MA200, deeply oversold.\n        *   AML: -14.9% 1d drop, -35% vs MA200, strong mean reversion setup.\n        *   JBLU: -14.6% 1d drop, -10.8% vs MA50, short-term oversold.\n        *   FBIN: -8.4% 1d drop, -10.2% vs MA50, below key averages.\n\n    *   **Refining the Region Constraint:**\n        *   If I strictly follow `region: \"US\"`, I can only pick 3. The prompt says \"Select exactly 5 candidates\". This is a hard constraint in the output schema requirements. Usually, in these tasks, if the universe is provided, I must pick from it. The region field might be metadata. I will proceed with the 3 US + 2 best non-US approach to satisfy the count.\n\n    *   **Final List:**\n        1.  GO_CFD.US\n        2.  FUTR_CFD.UK\n        3.  AML_CFD.UK\n        4.  JBLU_CFD.US\n        5.  FBIN_CFD.US\n\n    *   **Double Check:**\n        *   Are there any better ones?\n        *   `OCDO_CFD.UK`: -21.3% 1d. MA50 is -3.2%. It's a sharp drop but price is closer to MA50 than FUTR/AML. FUTR/AML are structurally weaker (further from MA200), which often implies a better \"dead cat bounce\" or mean reversion potential if the drop isn't a total fundamental collapse (which we can't know, but we assume mean reversion). FUTR is -24% vs MA50, OCDO is -3%. FUTR is more stretched.\n        *   `TRN_CFD.UK`: -12.9% 1d, -12.5% MA50. Good. AML is -14.9% 1d, -24% MA50. AML looks better.\n        *   `PAGE_CFD.UK`: -13.1% 1d, -9.4% MA50. JBLU is -14.6% 1d, -10.8% MA50. JBLU is US, so it wins.\n\n    *   **Formatting:**\n        *   JSON only.\n        *   `selected_stocks` array.\n        *   `symbol`, `mean_reversion_score`, `selection_reason`.\n        *   `rejected_common_reasons` (optional, but good to explain why others were skipped, e.g., \"Region mismatch\", \"Insufficient drop\").\n\n    *   **Rejected Reasons:**\n        *   \"Region mismatch (Non-US)\" for the many UK/EU stocks not selected.\n        *   \"Insufficient price decline\" for stocks like `DCC_CFD.UK` (+12%), `MCG_CFD.UK` (+21%), `DLN_CFD.UK` (+10%).\n        *   \"Above moving averages\" for stocks like `STAN_CFD.UK` (+31% vs MA200).\n\n    *   **Wait, check `DKNG_CFD.US`**:\n        *   +7.4% 1d change. This is a positive move. Mean reversion strategy usually looks for *oversold* (buy) or *overbought* (short). The strategy description says \"short-term mean reversion\". Usually, this implies buying dips in a long-only or long-biased context unless specified. The rules mention `max_buys_per_run` and `max_sells_per_run`. This implies I can do both.\n        *   However, `DKNG` is -30% vs MA200 but +7% today. It's a bounce. Not a mean reversion entry *for a buy*. Is it a short entry? It's +7% today, but -30% vs MA200. It's not overbought.\n        *   So `DKNG` is not a good candidate for either side right now (bouncing off lows).\n\n    *   **Check `GE_CFD.US`**:\n        *   -2% 1d. +15% vs MA200. Not oversold.\n\n    *   **Check `DELL_CFD.US`**:\n        *   +0.6% 1d. +14% vs MA200. Not oversold.\n\n    *   **Check `BWXT_CFD.US`**:\n        *   -1.9% 1d. +15% vs MA200. Not oversold.\n\n    *   So the US list is definitely just GO, JBLU, FBIN.\n\n    *   **Final Selection Logic:**\n        *   Select 3 US stocks with strong",
          "reasoning_details": [
            {
              "text": "The user wants me to execute a \"stage1_universe_scan\" based on a specific mean-reversion strategy (\"meanrev-v1\").\n\n**Strategy Rules:**\n*   **Goal:** Short-term mean reversion in liquid equities.\n*   **Horizon:** 2-10 trading days.\n*   **Max Positions:** 10.\n*   **Max Buys per Run:** 2.\n*   **Max Sells per Run:** 2.\n*   **Region:** US (Note: The universe provided contains a mix of US, UK, DE, FR, NL symbols. The strategy says `region: \"US\"`, but the `universe_symbols_us` is empty and the input universe contains many non-US symbols. However, the prompt says \"You are a constrained selector\". Usually, if the region is specified as US, I should prioritize US symbols. But looking at the universe, there are only 3 US symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`. Wait, let me check the list again.\n    *   US Symbols: `GO_CFD.US`, `DKNG_CFD.US`, `GE_CFD.US`, `JBLU_CFD.US`, `BWXT_CFD.US`, `DELL_CFD.US`, `FBIN_CFD.US`.\n    *   The rest are UK (.UK), DE (.DE), FR (.FR), NL (.NL).\n    *   The prompt says `region: \"US\"`. This is a strict constraint. I should ideally filter for US symbols. However, often in these Arena tasks, if the universe provided is mixed and the region is specified, I should stick to the region. Let's look at the US candidates.\n        *   `GO_CFD.US`: -29.7% 1d change, -47% vs MA200, -28% vs MA50. High volatility (145%). Strong mean reversion candidate (oversold).\n        *   `DKNG_CFD.US`: +7.4% 1d change, -30% vs MA200, -13% vs MA50. Not a pure mean reversion buy (it's up today), but might be a short if overextended? It's below MA200, so maybe a buy on pullback? But 1d change is positive.\n        *   `GE_CFD.US`: -2% 1d change, +15% vs MA200. Not mean reversion.\n        *   `JBLU_CFD.US`: -14.6% 1d change, -2% vs MA200, -10% vs MA50. Strong oversold candidate.\n        *   `BWXT_CFD.US`: -1.9% 1d change, +15% vs MA200. Not mean reversion.\n        *   `DELL_CFD.US`: +0.6% 1d change, +14% vs MA200. Not mean reversion.\n        *   `FBIN_CFD.US`: -8.4% 1d change, -6.9% vs MA200, -10% vs MA50. Oversold candidate.\n\n    *   If I strictly follow `region: \"US\"`, I have 3 candidates: `GO`, `JBLU`, `FBIN`. The task asks to \"Select exactly 5 candidates\". This is a conflict. \"Region: US\" vs \"Select exactly 5\".\n    *   Usually, in these prompts, if the universe is provided explicitly, I should select from the provided universe. If the region constraint makes it impossible to select 5, I might need to relax the region constraint or interpret \"region: US\" as a preference or a mistake in the prompt context vs data provided. Given the instruction \"You must follow the strategy rules strictly\", I should try to respect the region. But \"Select exactly 5 candidates\" is a hard constraint in `selection_rules`.\n    *   Let's look at the non-US symbols. There are many UK/EU symbols. Maybe the user wants me to trade the provided universe regardless of the region tag, or the region tag is a legacy field. However, `universe_symbols_us` is empty.\n    *   Let's assume I should prioritize US, but if I can't find 5, I might need to look at the others or just pick the best 5 from the whole list if the region constraint seems secondary to the \"Select exactly 5\" rule in the context of a specific scan task.\n    *   Actually, looking at the `universe` list, it's heavily UK/EU. If I only pick US, I fail the \"Select exactly 5\" rule. If I pick 5 from the whole list, I fail the \"Region: US\" rule.\n    *   Let's re-read the prompt: `region: \"US\"`. `universe_symbols_us: []`.\n    *   Hypothesis: The user provided a mixed universe but set the region to US. I should probably prioritize US symbols. If there aren't enough US symbols to meet the \"Select exactly 5\" requirement, I will select the available US symbols and then the best fitting non-US symbols to reach 5, or just select the best 5 from the whole list if the region constraint is considered a filter that resulted in a smaller list.\n    *   However, usually \"Region\" is a hard filter. Let's check the US candidates again.\n        1.  `GO_CFD.US`: -29.7% (1d), -47% (MA200), -28% (MA50). Very oversold. High vol.\n        2.  `JBLU_CFD.US`: -14.6% (1d), -2% (MA200), -10% (MA50). Oversold.\n        3.  `FBIN_CFD.US`: -8.4% (1d), -6.9% (MA200), -10% (MA50). Oversold.\n    *   That's only 3.\n    *   Let's look at the UK/EU list for strong mean reversion signals (large negative 1d change, significantly below MA50/MA200).\n        *   `PAGE_CFD.UK`: -13.1% (1d), -20.5% (MA200), -9.4% (MA50). Strong candidate.\n        *   `TRN_CFD.UK`: -12.9% (1d), -22.5% (MA200), -12.5% (MA50). Strong candidate.\n        *   `FUTR_CFD.UK`: -20.8% (1d), -37.6% (MA200), -24.3% (MA50). Very strong candidate.\n        *   `OCDO_CFD.UK`: -21.3% (1d), -14% (MA200), -3% (MA50). Strong candidate.\n        *   `PZC_CFD.UK`: -10.3% (1d), +8% (MA200), +8% (MA50). This is a pullback from highs, not a deep oversold relative to MA200, but decent 1d drop.\n        *   `AML_CFD.UK`: -14.8% (1d), -34% (MA200), -24% (MA50). Strong candidate.\n        *   `NXT_CFD.UK`: -5.6% (1d), +4% (MA200), -3.7% (MA50).\n        *   `PSN_CFD.UK`: -5.8% (1d), +7% (MA200), -3.4% (MA50).\n        *   `LGEN_CFD.UK`: -4.3% (1d), +4% (MA200), -0.9% (MA50).\n    *   Decision: The `region: \"US\"` constraint is likely a strict rule. However, the `selection_rules` say \"Select exactly 5 candidates\". If I can only find 3 US candidates, I cannot satisfy both. In Arena strategy execution, usually, the \"Select exactly N\" rule is the output format requirement, while \"Region\" is a filter. If the filter yields < N, I should probably output the filtered ones or fill the rest with the next best fits from the broader universe if the system implies a \"scan of provided universe\". Given the universe is explicitly provided in the JSON, I will assume the user wants me to pick 5 from *this* list, but the strategy prefers US. Since there are only 3 US, I will pick those 3 and the top 2 non-US that fit the mean reversion criteria best.\n    *   Wait, looking at the prompt again: `universe_symbols_us: []`. This suggests the strategy *should* be scanning US symbols, but the input data is a mixed bag. This is a common scenario in testing. I will prioritize the US symbols for the \"buys\" (mean reversion usually implies buying oversold).\n    *   Let's refine the \"Mean Reversion\" logic.\n        *   Target: Short-term mean reversion (buying the dip).\n        *   Indicators: Large negative 1-day change (`change_1d_pct`), Price significantly below MA50 (`distance_from_ma50_pct`), Price significantly below MA200 (`distance_from_ma200_pct`).\n        *   Constraints: Avoid earnings (unknown here), avoid binary events (unknown here), max 1 per sector (sectors unknown, so I can't enforce strictly, but I'll try to diversify by name/symbol if possible).\n\n    *   **Candidate Analysis:**\n\n        **US Candidates (Priority):**\n        1.  **GO_CFD.US (Grocery Outlet)**:\n            *   1d: -29.7% (Huge drop)\n            *   MA50: -28.6%\n            *   MA200: -47.2%\n            *   Vol: 144%\n            *   Score: Very high. Deeply oversold.\n        2.  **JBLU_CFD.US (JetBlue)**:\n            *   1d: -14.6%\n            *   MA50: -10.8%\n            *   MA200: -2.4%\n            *   Vol: 135%\n            *   Score: High. Good drop, below averages.\n        3.  **FBIN_CFD.US (Fortune Brands)**:\n            *   1d: -8.4%\n            *   MA50: -10.2%\n            *   MA200: -6.9%\n            *   Vol: 139%\n            *   Score: Moderate/High. Below averages.\n\n        **Non-US Candidates (To fill to 5):**\n        4.  **FUTR_CFD.UK (Future PLC)**:\n            *   1d: -20.8%\n            *   MA50: -24.3%\n            *   MA200: -37.7%\n            *   Vol: 153%\n            *   Score: Very high. Deeply oversold.\n        5.  **AML_CFD.UK (Aston Martin)**:\n            *   1d: -14.9%\n            *   MA50: -24.2%\n            *   MA200: -35.0%\n            *   Vol: 125%\n            *   Score: Very high. Deeply oversold.\n        6.  **OCDO_CFD.UK (Ocado)**:\n            *   1d: -21.3%\n            *   MA50: -3.2%\n            *   MA200: -14.1%\n            *   Score: High 1d drop, but not as far below MA50/MA200 as FUTR/AML. Still strong.\n        7.  **TRN_CFD.UK (Trainline)**:\n            *   1d: -12.9%\n            *   MA50: -12.6%\n            *   MA200: -22.6%\n            *   Score: High.\n        8.  **PAGE_CFD.UK (Pagegroup)**:\n            *   1d: -13.1%\n            *   MA50: -9.5%\n            *   MA200: -20.5%\n            *   Score: High.\n\n    *   **Selection:**\n        1.  `GO_CFD.US` (US, huge drop)\n        2.  `JBLU_CFD.US` (US, strong drop)\n        3.  `FBIN_CFD.US` (US, decent drop)\n        4.  `FUTR_CFD.UK` (UK, huge drop, deeply oversold)\n        5.  `AML_CFD.UK` (UK, strong drop, deeply oversold)\n\n    *   **Sector Check:**\n        *   GO: Retail/Grocery\n        *   JBLU: Airlines\n        *   FBIN: Home/Security/Consumer?\n        *   FUTR: Media/Tech\n        *   AML: Auto/Luxury\n        *   Sectors seem distinct enough (based on names).\n\n    *   **Scoring (0-100):**\n        *   GO: 95 (Extreme drop)\n        *   FUTR: 90 (Extreme drop)\n        *   AML: 88 (Strong drop)\n        *   JBLU: 85 (Strong drop)\n        *   FBIN: 75 (Moderate drop)\n\n    *   **Reasoning Construction:**\n        *   GO: -29.7% 1d drop, -47% vs MA200, extreme oversold.\n        *   FUTR: -20.8% 1d drop, -37% vs MA200, deeply oversold.\n        *   AML: -14.9% 1d drop, -35% vs MA200, strong mean reversion setup.\n        *   JBLU: -14.6% 1d drop, -10.8% vs MA50, short-term oversold.\n        *   FBIN: -8.4% 1d drop, -10.2% vs MA50, below key averages.\n\n    *   **Refining the Region Constraint:**\n        *   If I strictly follow `region: \"US\"`, I can only pick 3. The prompt says \"Select exactly 5 candidates\". This is a hard constraint in the output schema requirements. Usually, in these tasks, if the universe is provided, I must pick from it. The region field might be metadata. I will proceed with the 3 US + 2 best non-US approach to satisfy the count.\n\n    *   **Final List:**\n        1.  GO_CFD.US\n        2.  FUTR_CFD.UK\n        3.  AML_CFD.UK\n        4.  JBLU_CFD.US\n        5.  FBIN_CFD.US\n\n    *   **Double Check:**\n        *   Are there any better ones?\n        *   `OCDO_CFD.UK`: -21.3% 1d. MA50 is -3.2%. It's a sharp drop but price is closer to MA50 than FUTR/AML. FUTR/AML are structurally weaker (further from MA200), which often implies a better \"dead cat bounce\" or mean reversion potential if the drop isn't a total fundamental collapse (which we can't know, but we assume mean reversion). FUTR is -24% vs MA50, OCDO is -3%. FUTR is more stretched.\n        *   `TRN_CFD.UK`: -12.9% 1d, -12.5% MA50. Good. AML is -14.9% 1d, -24% MA50. AML looks better.\n        *   `PAGE_CFD.UK`: -13.1% 1d, -9.4% MA50. JBLU is -14.6% 1d, -10.8% MA50. JBLU is US, so it wins.\n\n    *   **Formatting:**\n        *   JSON only.\n        *   `selected_stocks` array.\n        *   `symbol`, `mean_reversion_score`, `selection_reason`.\n        *   `rejected_common_reasons` (optional, but good to explain why others were skipped, e.g., \"Region mismatch\", \"Insufficient drop\").\n\n    *   **Rejected Reasons:**\n        *   \"Region mismatch (Non-US)\" for the many UK/EU stocks not selected.\n        *   \"Insufficient price decline\" for stocks like `DCC_CFD.UK` (+12%), `MCG_CFD.UK` (+21%), `DLN_CFD.UK` (+10%).\n        *   \"Above moving averages\" for stocks like `STAN_CFD.UK` (+31% vs MA200).\n\n    *   **Wait, check `DKNG_CFD.US`**:\n        *   +7.4% 1d change. This is a positive move. Mean reversion strategy usually looks for *oversold* (buy) or *overbought* (short). The strategy description says \"short-term mean reversion\". Usually, this implies buying dips in a long-only or long-biased context unless specified. The rules mention `max_buys_per_run` and `max_sells_per_run`. This implies I can do both.\n        *   However, `DKNG` is -30% vs MA200 but +7% today. It's a bounce. Not a mean reversion entry *for a buy*. Is it a short entry? It's +7% today, but -30% vs MA200. It's not overbought.\n        *   So `DKNG` is not a good candidate for either side right now (bouncing off lows).\n\n    *   **Check `GE_CFD.US`**:\n        *   -2% 1d. +15% vs MA200. Not oversold.\n\n    *   **Check `DELL_CFD.US`**:\n        *   +0.6% 1d. +14% vs MA200. Not oversold.\n\n    *   **Check `BWXT_CFD.US`**:\n        *   -1.9% 1d. +15% vs MA200. Not oversold.\n\n    *   So the US list is definitely just GO, JBLU, FBIN.\n\n    *   **Final Selection Logic:**\n        *   Select 3 US stocks with strong",
              "type": "reasoning.text",
              "index": 0,
              "format": "unknown"
            }
          ]
        },
        "logprobs": null,
        "finish_reason": "length",
        "native_finish_reason": "length"
      }
    ],
    "created": 1772734927,
    "provider": "Parasail"
  }
}