Arena run #407

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": "EU", "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": "VTY_CFD.UK", "name": "VISTRY GROUP PLC", "sector": "", "last_price": 720.0, "change_1d_pct": 7.752170008979342, "distance_from_ma200_pct": 13.334264143447182, "distance_from_ma50_pct": 9.187026761436968, "volatility_20d_pct": 108.27100991710547, "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": "WEIR_CFD.UK", "name": "", "sector": "", "last_price": 3564.0, "change_1d_pct": 19.19732441471571, "distance_from_ma200_pct": 37.136107856394496, "distance_from_ma50_pct": 18.20579522816075, "volatility_20d_pct": 87.71696843792506, "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": "CCL_CFD.UK", "name": "CARNIVAL PLC", "sector": "", "last_price": 2373.0, "change_1d_pct": 7.912687585266021, "distance_from_ma200_pct": 26.41215845244669, "distance_from_ma50_pct": 14.616441127141865, "volatility_20d_pct": 141.1300212149729, "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": "CBG_CFD.UK", "name": "CLOSE BROTHERS GROUP PLC", "sector": "", "last_price": 497.8, "change_1d_pct": -5.89792060491493, "distance_from_ma200_pct": 15.793508864321094, "distance_from_ma50_pct": 3.592137485786795, "volatility_20d_pct": 116.76618625853061, "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": "KGF_CFD.UK", "name": "KINGFISHER PLC", "sector": "", "last_price": 367.3, "change_1d_pct": 9.282951502529002, "distance_from_ma200_pct": 25.836325072262547, "distance_from_ma50_pct": 13.171108060075287, "volatility_20d_pct": 79.7523939314479, "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": "ADS_CFD.DE", "name": "ADIDAS AG", "sector": "", "last_price": 146.92, "change_1d_pct": -8.170982480483524, "distance_from_ma200_pct": -16.771731120801313, "distance_from_ma50_pct": -6.281487748849712, "volatility_20d_pct": 43.26107369371941, "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": 41.0, "change_1d_pct": -29.31034482758621, "distance_from_ma200_pct": -41.07802778099134, "distance_from_ma50_pct": -31.55967161996588, "volatility_20d_pct": 109.21218170050702, "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": "COFB_CFD.BE", "name": "Cofinimmo SA", "sector": "", "last_price": 91.25, "change_1d_pct": 1.9553072625698276, "distance_from_ma200_pct": null, "distance_from_ma50_pct": 9.988790183575812, "volatility_20d_pct": 1542.002222512536, "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": "IMI_CFD.UK", "name": "IMI PLC", "sector": "", "last_price": 2828.0, "change_1d_pct": 8.26952526799387, "distance_from_ma200_pct": 29.012317870749314, "distance_from_ma50_pct": 11.107007677698544, "volatility_20d_pct": 78.40398881701634, "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": "AT1_CFD.DE", "name": "AROUNDTOWN SA", "sector": "", "last_price": 2.692, "change_1d_pct": -12.654120700843595, "distance_from_ma200_pct": -10.79535287529243, "distance_from_ma50_pct": -2.9063183028081974, "volatility_20d_pct": 98.53482010392509, "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": "CNA_CFD.UK", "name": "CENTRICA PLC", "sector": "", "last_price": 197.0, "change_1d_pct": 2.3376623376623273, "distance_from_ma200_pct": 16.907910392757188, "distance_from_ma50_pct": 8.119974753711467, "volatility_20d_pct": 93.26050913466813, "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": "STNE_CFD.US", "name": "", "sector": "", "last_price": 14.17, "change_1d_pct": -13.544844417327639, "distance_from_ma200_pct": -10.990785002229941, "distance_from_ma50_pct": -10.379983808945559, "volatility_20d_pct": 147.29515351881847, "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": "HLMA_CFD.UK", "name": "HALMA PLC", "sector": "", "last_price": 4104.0, "change_1d_pct": 15.345699831365934, "distance_from_ma200_pct": 23.591861367515033, "distance_from_ma50_pct": 12.09684468140897, "volatility_20d_pct": 82.81135582460098, "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": 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": 0.185, "change_1d_pct": -6.565656565656575, "distance_from_ma200_pct": 20.69809166530745, "distance_from_ma50_pct": -5.833248498422073, "volatility_20d_pct": 242.82012575943392, "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": "IPO_CFD.UK", "name": "IP GROUP PLC", "sector": "", "last_price": 57.6, "change_1d_pct": -1.873935264054516, "distance_from_ma200_pct": 10.233643652522883, "distance_from_ma50_pct": -2.027486732888828, "volatility_20d_pct": 89.82123635439407, "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": "MDB_CFD.US", "name": "", "sector": "", "last_price": 259.43, "change_1d_pct": -21.353866674750655, "distance_from_ma200_pct": -15.190615164707822, "distance_from_ma50_pct": -32.01683816847183, "volatility_20d_pct": 215.6796730862848, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, "news_sentiment_score": null, 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"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, 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"snapshot_ok_prices_technicals_only"}, {"symbol": "SNOW_CFD.US", "name": "", "sector": "", "last_price": 168.16, "change_1d_pct": -1.4706744008906036, "distance_from_ma200_pct": -22.961439946710716, "distance_from_ma50_pct": -15.014479276693182, "volatility_20d_pct": 138.69851569407896, "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": "GOOS_CFD.US", "name": "", "sector": "", "last_price": 11.94, "change_1d_pct": -3.6319612590799077, "distance_from_ma200_pct": -6.134289285630057, "distance_from_ma50_pct": -5.341768539218961, "volatility_20d_pct": 129.54896197262082, "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": "PSN_CFD.UK", "name": "PERSIMMON PLC", "sector": "", "last_price": 1398.0, "change_1d_pct": -2.7640602612433396, "distance_from_ma200_pct": 10.380997224407462, "distance_from_ma50_pct": -0.5773200509943011, "volatility_20d_pct": 95.523625160791, "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": "ARG_CFD.FR", "name": "ARGAN", "sector": "", "last_price": 66.0, "change_1d_pct": -6.6108218247679424, "distance_from_ma200_pct": 0.8922312752617456, "distance_from_ma50_pct": -1.0027521234909686, "volatility_20d_pct": 45.27217456851028, "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": "WLK_CFD.US", "name": "WESTLAKE CHEMICAL CORP", "sector": "", "last_price": 104.3, "change_1d_pct": 4.143784323514721, "distance_from_ma200_pct": 31.068351579857435, "distance_from_ma50_pct": 19.30553426688293, "volatility_20d_pct": 108.2661494522795, "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": "UHAL_CFD.US", "name": "", "sector": "", "last_price": 51.92, "change_1d_pct": 3.591380686352763, "distance_from_ma200_pct": -8.283540234149012, "distance_from_ma50_pct": -2.469446562949662, "volatility_20d_pct": 85.7198981925985, "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": "IVZ_CFD.US", "name": "", "sector": "", "last_price": 25.71, "change_1d_pct": -3.9237668161435035, "distance_from_ma200_pct": 14.96077427489979, "distance_from_ma50_pct": -5.63821743949614, "volatility_20d_pct": 82.56488833349962, "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": "JLL_CFD.US", "name": "", "sector": "", "last_price": 314.8, "change_1d_pct": -1.4031571034828216, "distance_from_ma200_pct": 6.188624459274572, "distance_from_ma50_pct": -6.114319832842929, "volatility_20d_pct": 109.52287995519126, "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 mean-reversion strategy. I need to follow strict rules and output ONLY valid JSON. I cannot invent any data. If a field is null/empty, I must treat it as unknown.\n\nLet me break down the task:\n- **Task**: stage1_universe_scan\n- **Region**: EU\n- **Strategy**: meanrev-v1 (Two-Stage LLM Mean-Reversion)\n- **Rules**:\n  - max_positions: 10\n  - max_buys_per_run: 2\n  - avoid_overtrading: true\n  - max_sells_per_run: 2\n  - max_gross_leverage: 3.0\n  - fixed_position_size: 3000.0\n  - universe_symbols_eu: [] (empty)\n  - universe_symbols_us: [] (empty)\n- **Selection Rules**:\n  - Horizon: 2-10 trading days\n  - Target: short-term mean reversion in liquid equities\n  - Constraints:\n    - Select exactly 5 candidates\n    - Avoid earnings within 5 trading days (if known)\n    - Avoid highly binary/event-driven moves (if known)\n    - Avoid sector concentration: max 1 per sector unless clearly justified (sector may be unknown)\n- **Universe**: A list of 40 stocks with various data points. Most have null/unknown values for fundamentals, earnings, sentiment, etc. The data is mostly technical: price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct.\n\n**Key points:**\n- I must select **exactly 5 candidates** for mean reversion.\n- The selection must be based on **short-term mean reversion** indicators.\n- I should avoid stocks with earnings within 5 days, but **all earnings data is null/unknown**, so this constraint is effectively not applicable based on the provided data.\n- I should avoid binary/event-driven moves, but **no news headlines or sentiment scores are provided**, so I cannot assess this. I must rely on technical indicators.\n- I should avoid sector concentration, but **all sector data is empty/unknown**. Since sectors are unknown, I cannot enforce this constraint strictly. I should select based on technicals and note this limitation.\n- I must output **only valid JSON**.\n- I must be a **constrained selector (no narrative)**.\n- I cannot invent prices, fundamentals, earnings dates, or sentiment.\n\n**Mean Reversion Logic:**\nFor mean reversion, I want stocks that have moved significantly away from their moving averages and are likely to revert. Typically:\n- Large **negative** 1-day change (oversold) -> potential BUY candidate for upward reversion.\n- Large **positive** 1-day change (overbought) -> potential SELL/SHORT candidate for downward reversion.\n- Large absolute distance from MA50 or MA200 suggests deviation from mean.\n- High volatility is common but should be considered for risk.\n\nSince this is a **scan** for candidates, I should identify both potential LONG candidates (oversold) and potential SHORT candidates (overbought). The strategy is \"Two-Stage\", so this scan is likely identifying candidates for both sides. The output schema asks for `selected_stocks` with a `mean_reversion_score` and `selection_reason`. It doesn't explicitly separate long/short, but the reason should indicate the direction.\n\nLet me analyze the universe for mean reversion signals:\n\n**Oversold (Potential LONG candidates):**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Strong oversold signal.\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Very strong oversold, but extreme move might be event-driven (unknown). High volatility (109%).\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Oversold.\n- **STNE_CFD.US**: -13.54% change, -10.99% from MA200, -10.38% from MA50. Oversold. (US symbol, but in EU scan? The symbol has .US, but the task is EU region. The data includes US symbols. I should consider all provided, but maybe prioritize .UK, .DE, .FR, .PT, .BE? The task says \"region\": \"EU\", but the universe mixes them. I'll treat the region as a general context but evaluate all symbols given. The strategy doesn't forbid US symbols in an EU scan explicitly. I'll select the best candidates regardless of suffix, but note that .US symbols might be less relevant. Let me check the instructions again. The strategy has `universe_symbols_eu` and `universe_symbols_us` which are empty. This suggests the universe is being provided dynamically. The region flag might be for context. I'll focus on the technicals and select the best 5 mean-reversion candidates from the *provided list*, interpreting \"EU\" as the primary focus but not strictly limiting to EU tickers if the data is mixed. Actually, looking at the symbols: VTY, WEIR, UTG, CCL, CBG, KGF, ADS, AML, COFB, IMI, AT1, CNA, STNE, HLMA, SHC, IPR, IPO, ATG, MDB, EXPN, CCC, BYIT, WKP, CCH, MCG, EZJ, LSEG, SBGI, MKS, BOSS, ALK, HMSO, BT, MSLH, PAGE, NBIS, COIN, NXT, SDG, SNOW, GOOS, PSN, ARG, WLK, UHAL, IVZ, JLL. Many are .UK, .DE, .FR, .PT, .BE. STNE, MDB, SBGI, ALK, NBIS, COIN, SNOW, GOOS, WLK, UHAL, IVZ, JLL are .US or .CFD.US. The task is EU region. I should probably prioritize EU symbols (.UK, .DE, .FR, .PT, .BE). Let me filter the list to EU symbols first.\n\n**EU Symbols:**\n- VTY_CFD.UK\n- WEIR_CFD.UK\n- UTG_CFD.UK\n- CCL_CFD.UK\n- CBG_CFD.UK\n- KGF_CFD.UK\n- ADS_CFD.DE\n- AML_CFD.UK\n- COFB_CFD.BE\n- IMI_CFD.UK\n- AT1_CFD.DE\n- CNA_CFD.UK\n- HLMA_CFD.UK\n- SHC_CFD.UK\n- IPR_CFD.PT\n- IPO_CFD.UK\n- ATG_CFD.UK\n- EXPN_CFD.UK\n- CCC_CFD.UK\n- BYIT_CFD.UK\n- WKP_CFD.UK\n- CCH_CFD.UK\n- MCG_CFD.UK\n- EZJ_CFD.UK\n- LSEG_CFD.UK\n- MKS_CFD.UK\n- BOSS_CFD.DE\n- HMSO_CFD.UK\n- BT_CFD.UK\n- MSLH_CFD.UK\n- PAGE_CFD.UK\n- NXT_CFD.UK\n- SDG_CFD.FR\n- PSN_CFD.UK\n- ARG_CFD.FR\n\n**US Symbols (to be ignored for EU scan):**\n- STNE_CFD.US\n- MDB_CFD.US\n- SBGI_CFD.US\n- ALK_CFD.US\n- NBIS_CFD.US\n- COIN_CFD.US\n- SNOW_CFD.US\n- GOOS_CFD.US\n- WLK_CFD.US\n- UHAL_CFD.US\n- IVZ_CFD.US\n- JLL_CFD.US\n\nOkay, I'll focus ONLY on EU symbols for the selection.\n\n**Re-evaluating EU Oversold candidates:**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Good candidate. Volatility 43% (moderate).\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Extreme oversold. High vol (109%). Risky but strong signal.\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Good candidate. Volatility 98%.\n- **EZJ_CFD.UK**: -8.18% change, -10.29% from MA200, -8.26% from MA50. Good candidate. Volatility 86%.\n- **PAGE_CFD.UK**: -10.96% change, -19.31% from MA200, -8.41% from MA50. Good candidate. Volatility 112%.\n- **BYIT_CFD.UK**: -12.67% change, -28.03% from MA200, -14.35% from MA50. Good candidate. Volatility 88%.\n- **UTG_CFD.UK**: +3.46% change, -23.93% from MA200, -3.69% from MA50. Not oversold on daily change, but far from MA200. Mixed signal.\n- **EXPN_CFD.UK**: +8.67% change, -22.50% from MA200, -7.08% from MA50. Overbought daily, but below MAs. Mixed.\n\n**EU Overbought (Potential SHORT candidates):**\n- **VTY_CFD.UK**: +7.75% change, +13.33% from MA200, +9.19% from MA50. Overbought.\n- **WEIR_CFD.UK**: +19.20% change, +37.14% from MA200, +18.21% from MA50. Very overbought. High vol (87%).\n- **CCL_CFD.UK**: +7.91% change, +26.41% from MA200, +14.62% from MA50. Overbought.\n- **CBG_CFD.UK**: -5.90% change, +15.79% from MA200, +3.59% from MA50. Mixed. Down day but above MAs.\n- **KGF_CFD.UK**: +9.28% change, +25.84% from MA200, +13.17% from MA50. Overbought.\n- **IMI_CFD.UK**: +8.27% change, +29.01% from MA200, +11.11% from MA50. Overbought.\n- **CNA_CFD.UK**: +2.34% change, +16.91% from MA200, +8.12% from MA50. Slightly overbought.\n- **HLMA_CFD.UK**: +15.35% change, +23.59% from MA200, +12.10% from MA50. Very overbought. Volatility 82%.\n- **MCG_CFD.UK**: +23.85% change, +12.69% from MA200, +32.02% from MA50. Extremely overbought. High vol (210%).\n- **LSEG_CFD.UK**: +9.55% change, -7.44% from MA200, +3.75% from MA50. Mixed. Up day, but below MA200.\n- **MKS_CFD.UK**: +9.41% change, +10.11% from MA200, +12.60% from MA50. Overbought.\n- **HMSO_CFD.UK**: +14.13% change, +22.52% from MA200, +10.01% from MA50. Very overbought. Volatility 87%.\n- **BT_CFD.UK**: +3.38% change, +10.17% from MA200, +9.33% from MA50. Mildly overbought.\n\n**Strategy**: Select exactly 5 candidates. For mean reversion, I want a mix of clear oversold and clear overbought stocks. The score should reflect the strength of the signal. I'll create a simple scoring system based on the magnitude of the deviation.\n\n**Scoring logic (0-100):**\n- **Oversold (LONG)**: Score based on negative change_1d_pct and negative distance_from_ma50_pct. The more negative, the higher the score (closer to 100).\n- **Overbought (SHORT)**: Score based on positive change_1d_pct and positive distance_from_ma50_pct. The more positive, the higher the score (closer to 100).\n\nLet me pick the top candidates:\n\n**LONG Candidates (Oversold):**\n1. **AML_CFD.UK**: -29.31% change, -31.56% from MA50. Extreme. Score: 95. (High vol, but strong signal)\n2. **BYIT_CFD.UK**: -12.67% change, -14.35% from MA50. Strong. Score: 85.\n3. **PAGE_CFD.UK**: -10.96% change, -8.41% from MA50. Good. Score: 80.\n4. **AT1_CFD.DE**: -12.65% change, -2.91% from MA50. Good daily drop, less stretched from MA50. Score: 78.\n5. **ADS_CFD.DE**: -8.17% change, -6.28% from MA50. Solid. Score: 75.\n6. **EZJ_CFD.UK**: -8.18% change, -8.26% from MA50. Solid. Score: 76.\n\n**SHORT Candidates (Overbought):**\n1. **MCG_CFD.UK**: +23.85% change, +32.02% from MA50. Extreme. Score: 95. (Very high vol)\n2. **WEIR_CFD.UK**: +19.20% change, +18.21% from MA50. Very strong. Score: 90.\n3. **HLMA_CFD.UK**: +15.35% change, +12.10% from MA50. Strong. Score: 85.\n4. **HMSO_CFD.UK**: +14.13% change, +10.01% from MA50. Strong. Score: 82.\n5. **KGF_CFD.UK**: +9.28% change, +13.17% from MA50. Good. Score: 80.\n6. **IMI_CFD.UK**: +8.27% change, +11.11% from MA50. Good. Score: 78.\n\n**Selection of 5:**\nI need to pick exactly 5. For a balanced scan, I should include both long and short candidates. The strategy doesn't specify the ratio, but a mix is logical for a mean-reversion universe scan. Let's pick the strongest signals overall.\n\n1. **AML_CFD.UK (LONG)**: Extremely oversold. Strongest signal. Score 95.\n2. **MCG_CFD.UK (SHORT)**: Extremely overbought. Strongest signal. Score 95.\n3. **WEIR_CFD.UK (SHORT)**: Very overbought. Score 90.\n4. **BYIT_CFD.UK (LONG)**: Strongly oversold. Score 85.\n5. **HLMA_CFD.UK (SHORT)**: Strongly overbought. Score 85.\n\nThis gives me 3 SHORT and 2 LONG candidates. This seems reasonable. The sector constraint is impossible to enforce because all sector fields are empty. I must state this in the rejected reasons.\n\n**Selection Reason Wording**: Must be \"short, concrete, indicator-based\". No narrative.\n- For LONG (oversold): \"Oversold: -X% vs MA50, -Y% 1d\"\n- For SHORT (overbought): \"Overbought: +X% vs MA50, +Y% 1d\"\n\nLet me refine the scores and reasons:\n- **AML_CFD.UK**: change_1d_pct = -29.31, distance_from_ma50_pct = -31.56. Score: 95. Reason: \"Oversold: -31.6% vs MA50, -29.3% 1d\"\n- **MCG_CFD.UK**: change_1d_pct = +23.85, distance_from_ma50_pct = +32.02. Score: 95. Reason: \"Overbought: +32.0% vs MA50, +23.9% 1d\"\n- **WEIR_CFD.UK**: change_1d_pct = +19.20, distance_from_ma50_pct = +18.21. Score: 90. Reason: \"Overbought: +18.2% vs MA50, +19.2% 1d\"\n- **BYIT_CFD.UK**: change_1d_pct = -12.67, distance_from_ma50_pct = -14.35. Score: 85. Reason: \"Oversold: -14.3% vs MA50, -12.7% 1d\"\n- **HLMA_CFD.UK**: change_1d_pct = +15.35, distance_from_ma50_pct = +12.10. Score: 85. Reason: \"Overbought: +12.1% vs MA50, +15.3% 1d\"\n\nThis looks good. The scores are integers 0-100. The reasons are short and indicator-based.\n\n**Rejected Common Reasons**:\n- \"Sector unknown: cannot enforce sector concentration limit\"\n- \"Earnings data unknown: cannot filter on earnings date\"\n- \"News/sentiment data unknown: cannot filter on event-driven moves\"\n- \"Insufficient mean reversion signal: deviation from MA50 and 1d change not significant enough\"\n\nI should list the most relevant reasons why many stocks were rejected. The first three are systemic data gaps. The last one is the actual strategy filter.\n\nLet me double-check the constraints:\n- Exactly 5 candidates: Yes, I have 5.\n- Avoid earnings: Not applicable (all null).\n- Avoid binary/event-driven: Not applicable (no news data).\n- Avoid sector concentration: Not enforceable (all sector fields empty). I must mention this.\n- No narrative: The reasons are concise and data-driven.\n- Only JSON output",
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            {
              "text": " The user wants me to act as an \"Arena strategy executor\" for a mean-reversion strategy. I need to follow strict rules and output ONLY valid JSON. I cannot invent any data. If a field is null/empty, I must treat it as unknown.\n\nLet me break down the task:\n- **Task**: stage1_universe_scan\n- **Region**: EU\n- **Strategy**: meanrev-v1 (Two-Stage LLM Mean-Reversion)\n- **Rules**:\n  - max_positions: 10\n  - max_buys_per_run: 2\n  - avoid_overtrading: true\n  - max_sells_per_run: 2\n  - max_gross_leverage: 3.0\n  - fixed_position_size: 3000.0\n  - universe_symbols_eu: [] (empty)\n  - universe_symbols_us: [] (empty)\n- **Selection Rules**:\n  - Horizon: 2-10 trading days\n  - Target: short-term mean reversion in liquid equities\n  - Constraints:\n    - Select exactly 5 candidates\n    - Avoid earnings within 5 trading days (if known)\n    - Avoid highly binary/event-driven moves (if known)\n    - Avoid sector concentration: max 1 per sector unless clearly justified (sector may be unknown)\n- **Universe**: A list of 40 stocks with various data points. Most have null/unknown values for fundamentals, earnings, sentiment, etc. The data is mostly technical: price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct.\n\n**Key points:**\n- I must select **exactly 5 candidates** for mean reversion.\n- The selection must be based on **short-term mean reversion** indicators.\n- I should avoid stocks with earnings within 5 days, but **all earnings data is null/unknown**, so this constraint is effectively not applicable based on the provided data.\n- I should avoid binary/event-driven moves, but **no news headlines or sentiment scores are provided**, so I cannot assess this. I must rely on technical indicators.\n- I should avoid sector concentration, but **all sector data is empty/unknown**. Since sectors are unknown, I cannot enforce this constraint strictly. I should select based on technicals and note this limitation.\n- I must output **only valid JSON**.\n- I must be a **constrained selector (no narrative)**.\n- I cannot invent prices, fundamentals, earnings dates, or sentiment.\n\n**Mean Reversion Logic:**\nFor mean reversion, I want stocks that have moved significantly away from their moving averages and are likely to revert. Typically:\n- Large **negative** 1-day change (oversold) -> potential BUY candidate for upward reversion.\n- Large **positive** 1-day change (overbought) -> potential SELL/SHORT candidate for downward reversion.\n- Large absolute distance from MA50 or MA200 suggests deviation from mean.\n- High volatility is common but should be considered for risk.\n\nSince this is a **scan** for candidates, I should identify both potential LONG candidates (oversold) and potential SHORT candidates (overbought). The strategy is \"Two-Stage\", so this scan is likely identifying candidates for both sides. The output schema asks for `selected_stocks` with a `mean_reversion_score` and `selection_reason`. It doesn't explicitly separate long/short, but the reason should indicate the direction.\n\nLet me analyze the universe for mean reversion signals:\n\n**Oversold (Potential LONG candidates):**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Strong oversold signal.\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Very strong oversold, but extreme move might be event-driven (unknown). High volatility (109%).\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Oversold.\n- **STNE_CFD.US**: -13.54% change, -10.99% from MA200, -10.38% from MA50. Oversold. (US symbol, but in EU scan? The symbol has .US, but the task is EU region. The data includes US symbols. I should consider all provided, but maybe prioritize .UK, .DE, .FR, .PT, .BE? The task says \"region\": \"EU\", but the universe mixes them. I'll treat the region as a general context but evaluate all symbols given. The strategy doesn't forbid US symbols in an EU scan explicitly. I'll select the best candidates regardless of suffix, but note that .US symbols might be less relevant. Let me check the instructions again. The strategy has `universe_symbols_eu` and `universe_symbols_us` which are empty. This suggests the universe is being provided dynamically. The region flag might be for context. I'll focus on the technicals and select the best 5 mean-reversion candidates from the *provided list*, interpreting \"EU\" as the primary focus but not strictly limiting to EU tickers if the data is mixed. Actually, looking at the symbols: VTY, WEIR, UTG, CCL, CBG, KGF, ADS, AML, COFB, IMI, AT1, CNA, STNE, HLMA, SHC, IPR, IPO, ATG, MDB, EXPN, CCC, BYIT, WKP, CCH, MCG, EZJ, LSEG, SBGI, MKS, BOSS, ALK, HMSO, BT, MSLH, PAGE, NBIS, COIN, NXT, SDG, SNOW, GOOS, PSN, ARG, WLK, UHAL, IVZ, JLL. Many are .UK, .DE, .FR, .PT, .BE. STNE, MDB, SBGI, ALK, NBIS, COIN, SNOW, GOOS, WLK, UHAL, IVZ, JLL are .US or .CFD.US. The task is EU region. I should probably prioritize EU symbols (.UK, .DE, .FR, .PT, .BE). Let me filter the list to EU symbols first.\n\n**EU Symbols:**\n- VTY_CFD.UK\n- WEIR_CFD.UK\n- UTG_CFD.UK\n- CCL_CFD.UK\n- CBG_CFD.UK\n- KGF_CFD.UK\n- ADS_CFD.DE\n- AML_CFD.UK\n- COFB_CFD.BE\n- IMI_CFD.UK\n- AT1_CFD.DE\n- CNA_CFD.UK\n- HLMA_CFD.UK\n- SHC_CFD.UK\n- IPR_CFD.PT\n- IPO_CFD.UK\n- ATG_CFD.UK\n- EXPN_CFD.UK\n- CCC_CFD.UK\n- BYIT_CFD.UK\n- WKP_CFD.UK\n- CCH_CFD.UK\n- MCG_CFD.UK\n- EZJ_CFD.UK\n- LSEG_CFD.UK\n- MKS_CFD.UK\n- BOSS_CFD.DE\n- HMSO_CFD.UK\n- BT_CFD.UK\n- MSLH_CFD.UK\n- PAGE_CFD.UK\n- NXT_CFD.UK\n- SDG_CFD.FR\n- PSN_CFD.UK\n- ARG_CFD.FR\n\n**US Symbols (to be ignored for EU scan):**\n- STNE_CFD.US\n- MDB_CFD.US\n- SBGI_CFD.US\n- ALK_CFD.US\n- NBIS_CFD.US\n- COIN_CFD.US\n- SNOW_CFD.US\n- GOOS_CFD.US\n- WLK_CFD.US\n- UHAL_CFD.US\n- IVZ_CFD.US\n- JLL_CFD.US\n\nOkay, I'll focus ONLY on EU symbols for the selection.\n\n**Re-evaluating EU Oversold candidates:**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Good candidate. Volatility 43% (moderate).\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Extreme oversold. High vol (109%). Risky but strong signal.\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Good candidate. Volatility 98%.\n- **EZJ_CFD.UK**: -8.18% change, -10.29% from MA200, -8.26% from MA50. Good candidate. Volatility 86%.\n- **PAGE_CFD.UK**: -10.96% change, -19.31% from MA200, -8.41% from MA50. Good candidate. Volatility 112%.\n- **BYIT_CFD.UK**: -12.67% change, -28.03% from MA200, -14.35% from MA50. Good candidate. Volatility 88%.\n- **UTG_CFD.UK**: +3.46% change, -23.93% from MA200, -3.69% from MA50. Not oversold on daily change, but far from MA200. Mixed signal.\n- **EXPN_CFD.UK**: +8.67% change, -22.50% from MA200, -7.08% from MA50. Overbought daily, but below MAs. Mixed.\n\n**EU Overbought (Potential SHORT candidates):**\n- **VTY_CFD.UK**: +7.75% change, +13.33% from MA200, +9.19% from MA50. Overbought.\n- **WEIR_CFD.UK**: +19.20% change, +37.14% from MA200, +18.21% from MA50. Very overbought. High vol (87%).\n- **CCL_CFD.UK**: +7.91% change, +26.41% from MA200, +14.62% from MA50. Overbought.\n- **CBG_CFD.UK**: -5.90% change, +15.79% from MA200, +3.59% from MA50. Mixed. Down day but above MAs.\n- **KGF_CFD.UK**: +9.28% change, +25.84% from MA200, +13.17% from MA50. Overbought.\n- **IMI_CFD.UK**: +8.27% change, +29.01% from MA200, +11.11% from MA50. Overbought.\n- **CNA_CFD.UK**: +2.34% change, +16.91% from MA200, +8.12% from MA50. Slightly overbought.\n- **HLMA_CFD.UK**: +15.35% change, +23.59% from MA200, +12.10% from MA50. Very overbought. Volatility 82%.\n- **MCG_CFD.UK**: +23.85% change, +12.69% from MA200, +32.02% from MA50. Extremely overbought. High vol (210%).\n- **LSEG_CFD.UK**: +9.55% change, -7.44% from MA200, +3.75% from MA50. Mixed. Up day, but below MA200.\n- **MKS_CFD.UK**: +9.41% change, +10.11% from MA200, +12.60% from MA50. Overbought.\n- **HMSO_CFD.UK**: +14.13% change, +22.52% from MA200, +10.01% from MA50. Very overbought. Volatility 87%.\n- **BT_CFD.UK**: +3.38% change, +10.17% from MA200, +9.33% from MA50. Mildly overbought.\n\n**Strategy**: Select exactly 5 candidates. For mean reversion, I want a mix of clear oversold and clear overbought stocks. The score should reflect the strength of the signal. I'll create a simple scoring system based on the magnitude of the deviation.\n\n**Scoring logic (0-100):**\n- **Oversold (LONG)**: Score based on negative change_1d_pct and negative distance_from_ma50_pct. The more negative, the higher the score (closer to 100).\n- **Overbought (SHORT)**: Score based on positive change_1d_pct and positive distance_from_ma50_pct. The more positive, the higher the score (closer to 100).\n\nLet me pick the top candidates:\n\n**LONG Candidates (Oversold):**\n1. **AML_CFD.UK**: -29.31% change, -31.56% from MA50. Extreme. Score: 95. (High vol, but strong signal)\n2. **BYIT_CFD.UK**: -12.67% change, -14.35% from MA50. Strong. Score: 85.\n3. **PAGE_CFD.UK**: -10.96% change, -8.41% from MA50. Good. Score: 80.\n4. **AT1_CFD.DE**: -12.65% change, -2.91% from MA50. Good daily drop, less stretched from MA50. Score: 78.\n5. **ADS_CFD.DE**: -8.17% change, -6.28% from MA50. Solid. Score: 75.\n6. **EZJ_CFD.UK**: -8.18% change, -8.26% from MA50. Solid. Score: 76.\n\n**SHORT Candidates (Overbought):**\n1. **MCG_CFD.UK**: +23.85% change, +32.02% from MA50. Extreme. Score: 95. (Very high vol)\n2. **WEIR_CFD.UK**: +19.20% change, +18.21% from MA50. Very strong. Score: 90.\n3. **HLMA_CFD.UK**: +15.35% change, +12.10% from MA50. Strong. Score: 85.\n4. **HMSO_CFD.UK**: +14.13% change, +10.01% from MA50. Strong. Score: 82.\n5. **KGF_CFD.UK**: +9.28% change, +13.17% from MA50. Good. Score: 80.\n6. **IMI_CFD.UK**: +8.27% change, +11.11% from MA50. Good. Score: 78.\n\n**Selection of 5:**\nI need to pick exactly 5. For a balanced scan, I should include both long and short candidates. The strategy doesn't specify the ratio, but a mix is logical for a mean-reversion universe scan. Let's pick the strongest signals overall.\n\n1. **AML_CFD.UK (LONG)**: Extremely oversold. Strongest signal. Score 95.\n2. **MCG_CFD.UK (SHORT)**: Extremely overbought. Strongest signal. Score 95.\n3. **WEIR_CFD.UK (SHORT)**: Very overbought. Score 90.\n4. **BYIT_CFD.UK (LONG)**: Strongly oversold. Score 85.\n5. **HLMA_CFD.UK (SHORT)**: Strongly overbought. Score 85.\n\nThis gives me 3 SHORT and 2 LONG candidates. This seems reasonable. The sector constraint is impossible to enforce because all sector fields are empty. I must state this in the rejected reasons.\n\n**Selection Reason Wording**: Must be \"short, concrete, indicator-based\". No narrative.\n- For LONG (oversold): \"Oversold: -X% vs MA50, -Y% 1d\"\n- For SHORT (overbought): \"Overbought: +X% vs MA50, +Y% 1d\"\n\nLet me refine the scores and reasons:\n- **AML_CFD.UK**: change_1d_pct = -29.31, distance_from_ma50_pct = -31.56. Score: 95. Reason: \"Oversold: -31.6% vs MA50, -29.3% 1d\"\n- **MCG_CFD.UK**: change_1d_pct = +23.85, distance_from_ma50_pct = +32.02. Score: 95. Reason: \"Overbought: +32.0% vs MA50, +23.9% 1d\"\n- **WEIR_CFD.UK**: change_1d_pct = +19.20, distance_from_ma50_pct = +18.21. Score: 90. Reason: \"Overbought: +18.2% vs MA50, +19.2% 1d\"\n- **BYIT_CFD.UK**: change_1d_pct = -12.67, distance_from_ma50_pct = -14.35. Score: 85. Reason: \"Oversold: -14.3% vs MA50, -12.7% 1d\"\n- **HLMA_CFD.UK**: change_1d_pct = +15.35, distance_from_ma50_pct = +12.10. Score: 85. Reason: \"Overbought: +12.1% vs MA50, +15.3% 1d\"\n\nThis looks good. The scores are integers 0-100. The reasons are short and indicator-based.\n\n**Rejected Common Reasons**:\n- \"Sector unknown: cannot enforce sector concentration limit\"\n- \"Earnings data unknown: cannot filter on earnings date\"\n- \"News/sentiment data unknown: cannot filter on event-driven moves\"\n- \"Insufficient mean reversion signal: deviation from MA50 and 1d change not significant enough\"\n\nI should list the most relevant reasons why many stocks were rejected. The first three are systemic data gaps. The last one is the actual strategy filter.\n\nLet me double-check the constraints:\n- Exactly 5 candidates: Yes, I have 5.\n- Avoid earnings: Not applicable (all null).\n- Avoid binary/event-driven: Not applicable (no news data).\n- Avoid sector concentration: Not enforceable (all sector fields empty). I must mention this.\n- No narrative: The reasons are concise and data-driven.\n- Only JSON output",
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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": "EU", "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": "2026-03-01T10:30:13.622938+00:00"}, {"symbol": "ICG_CFD.UK", "qty": 1.7647058823529411, "avg_price": 1700.0, "last_price": 1693.0, "unrealized_pnl": -12.352941176470587, "opened_at": "2026-02-16T09:30:55.514112+00:00"}, {"symbol": "LSS_CFD.FR", "qty": 0.0, "avg_price": 20.15, "last_price": 19.44, "unrealized_pnl": 0.0, "opened_at": "2026-02-16T09:30:55.514112+00:00"}, {"symbol": "PAGE_CFD.UK", "qty": 14.577259475218659, "avg_price": 205.8, "last_price": 205.8, "unrealized_pnl": 0.0, "opened_at": "2026-02-28T10:51:05.818250+00:00"}, {"symbol": "WPP_CFD.UK", "qty": 11.389521640091116, "avg_price": 263.4, "last_price": 245.2, "unrealized_pnl": -207.28929384965818, "opened_at": "2026-02-28T10:51:05.818250+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": [
      {
        "reason": "Significant drawdown (-6.9%) presents mean reversion opportunity, adding to position",
        "symbol": "WPP_CFD.UK",
        "position_size": 3000
      }
    ],
    "hold": [
      {
        "reason": "Price stable at cost basis (292.6), no mean reversion signal",
        "symbol": "BYIT_CFD.UK"
      },
      {
        "reason": "Minimal drawdown (-0.7%), insufficient for mean reversion trigger",
        "symbol": "ICG_CFD.UK"
      },
      {
        "reason": "Price stable at cost basis (205.8), no mean reversion signal",
        "symbol": "PAGE_CFD.UK"
      }
    ],
    "sell": []
  },
  "portfolio_rationale": "WPP shows clear mean reversion signal with -6.9% decline from entry, warranting addition. Other holdings show insufficient deviation from cost basis to justify trading. Avoiding overtrading by limiting to single high-conviction buy."
}

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          "reasoning": " The user wants me to act as an \"Arena strategy executor\" for a mean-reversion strategy. I need to follow strict rules and output ONLY valid JSON. I cannot invent any data. If a field is null/empty, I must treat it as unknown.\n\nLet me break down the task:\n- **Task**: stage1_universe_scan\n- **Region**: EU\n- **Strategy**: meanrev-v1 (Two-Stage LLM Mean-Reversion)\n- **Rules**:\n  - max_positions: 10\n  - max_buys_per_run: 2\n  - avoid_overtrading: true\n  - max_sells_per_run: 2\n  - max_gross_leverage: 3.0\n  - fixed_position_size: 3000.0\n  - universe_symbols_eu: [] (empty)\n  - universe_symbols_us: [] (empty)\n- **Selection Rules**:\n  - Horizon: 2-10 trading days\n  - Target: short-term mean reversion in liquid equities\n  - Constraints:\n    - Select exactly 5 candidates\n    - Avoid earnings within 5 trading days (if known)\n    - Avoid highly binary/event-driven moves (if known)\n    - Avoid sector concentration: max 1 per sector unless clearly justified (sector may be unknown)\n- **Universe**: A list of 40 stocks with various data points. Most have null/unknown values for fundamentals, earnings, sentiment, etc. The data is mostly technical: price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct.\n\n**Key points:**\n- I must select **exactly 5 candidates** for mean reversion.\n- The selection must be based on **short-term mean reversion** indicators.\n- I should avoid stocks with earnings within 5 days, but **all earnings data is null/unknown**, so this constraint is effectively not applicable based on the provided data.\n- I should avoid binary/event-driven moves, but **no news headlines or sentiment scores are provided**, so I cannot assess this. I must rely on technical indicators.\n- I should avoid sector concentration, but **all sector data is empty/unknown**. Since sectors are unknown, I cannot enforce this constraint strictly. I should select based on technicals and note this limitation.\n- I must output **only valid JSON**.\n- I must be a **constrained selector (no narrative)**.\n- I cannot invent prices, fundamentals, earnings dates, or sentiment.\n\n**Mean Reversion Logic:**\nFor mean reversion, I want stocks that have moved significantly away from their moving averages and are likely to revert. Typically:\n- Large **negative** 1-day change (oversold) -> potential BUY candidate for upward reversion.\n- Large **positive** 1-day change (overbought) -> potential SELL/SHORT candidate for downward reversion.\n- Large absolute distance from MA50 or MA200 suggests deviation from mean.\n- High volatility is common but should be considered for risk.\n\nSince this is a **scan** for candidates, I should identify both potential LONG candidates (oversold) and potential SHORT candidates (overbought). The strategy is \"Two-Stage\", so this scan is likely identifying candidates for both sides. The output schema asks for `selected_stocks` with a `mean_reversion_score` and `selection_reason`. It doesn't explicitly separate long/short, but the reason should indicate the direction.\n\nLet me analyze the universe for mean reversion signals:\n\n**Oversold (Potential LONG candidates):**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Strong oversold signal.\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Very strong oversold, but extreme move might be event-driven (unknown). High volatility (109%).\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Oversold.\n- **STNE_CFD.US**: -13.54% change, -10.99% from MA200, -10.38% from MA50. Oversold. (US symbol, but in EU scan? The symbol has .US, but the task is EU region. The data includes US symbols. I should consider all provided, but maybe prioritize .UK, .DE, .FR, .PT, .BE? The task says \"region\": \"EU\", but the universe mixes them. I'll treat the region as a general context but evaluate all symbols given. The strategy doesn't forbid US symbols in an EU scan explicitly. I'll select the best candidates regardless of suffix, but note that .US symbols might be less relevant. Let me check the instructions again. The strategy has `universe_symbols_eu` and `universe_symbols_us` which are empty. This suggests the universe is being provided dynamically. The region flag might be for context. I'll focus on the technicals and select the best 5 mean-reversion candidates from the *provided list*, interpreting \"EU\" as the primary focus but not strictly limiting to EU tickers if the data is mixed. Actually, looking at the symbols: VTY, WEIR, UTG, CCL, CBG, KGF, ADS, AML, COFB, IMI, AT1, CNA, STNE, HLMA, SHC, IPR, IPO, ATG, MDB, EXPN, CCC, BYIT, WKP, CCH, MCG, EZJ, LSEG, SBGI, MKS, BOSS, ALK, HMSO, BT, MSLH, PAGE, NBIS, COIN, NXT, SDG, SNOW, GOOS, PSN, ARG, WLK, UHAL, IVZ, JLL. Many are .UK, .DE, .FR, .PT, .BE. STNE, MDB, SBGI, ALK, NBIS, COIN, SNOW, GOOS, WLK, UHAL, IVZ, JLL are .US or .CFD.US. The task is EU region. I should probably prioritize EU symbols (.UK, .DE, .FR, .PT, .BE). Let me filter the list to EU symbols first.\n\n**EU Symbols:**\n- VTY_CFD.UK\n- WEIR_CFD.UK\n- UTG_CFD.UK\n- CCL_CFD.UK\n- CBG_CFD.UK\n- KGF_CFD.UK\n- ADS_CFD.DE\n- AML_CFD.UK\n- COFB_CFD.BE\n- IMI_CFD.UK\n- AT1_CFD.DE\n- CNA_CFD.UK\n- HLMA_CFD.UK\n- SHC_CFD.UK\n- IPR_CFD.PT\n- IPO_CFD.UK\n- ATG_CFD.UK\n- EXPN_CFD.UK\n- CCC_CFD.UK\n- BYIT_CFD.UK\n- WKP_CFD.UK\n- CCH_CFD.UK\n- MCG_CFD.UK\n- EZJ_CFD.UK\n- LSEG_CFD.UK\n- MKS_CFD.UK\n- BOSS_CFD.DE\n- HMSO_CFD.UK\n- BT_CFD.UK\n- MSLH_CFD.UK\n- PAGE_CFD.UK\n- NXT_CFD.UK\n- SDG_CFD.FR\n- PSN_CFD.UK\n- ARG_CFD.FR\n\n**US Symbols (to be ignored for EU scan):**\n- STNE_CFD.US\n- MDB_CFD.US\n- SBGI_CFD.US\n- ALK_CFD.US\n- NBIS_CFD.US\n- COIN_CFD.US\n- SNOW_CFD.US\n- GOOS_CFD.US\n- WLK_CFD.US\n- UHAL_CFD.US\n- IVZ_CFD.US\n- JLL_CFD.US\n\nOkay, I'll focus ONLY on EU symbols for the selection.\n\n**Re-evaluating EU Oversold candidates:**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Good candidate. Volatility 43% (moderate).\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Extreme oversold. High vol (109%). Risky but strong signal.\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Good candidate. Volatility 98%.\n- **EZJ_CFD.UK**: -8.18% change, -10.29% from MA200, -8.26% from MA50. Good candidate. Volatility 86%.\n- **PAGE_CFD.UK**: -10.96% change, -19.31% from MA200, -8.41% from MA50. Good candidate. Volatility 112%.\n- **BYIT_CFD.UK**: -12.67% change, -28.03% from MA200, -14.35% from MA50. Good candidate. Volatility 88%.\n- **UTG_CFD.UK**: +3.46% change, -23.93% from MA200, -3.69% from MA50. Not oversold on daily change, but far from MA200. Mixed signal.\n- **EXPN_CFD.UK**: +8.67% change, -22.50% from MA200, -7.08% from MA50. Overbought daily, but below MAs. Mixed.\n\n**EU Overbought (Potential SHORT candidates):**\n- **VTY_CFD.UK**: +7.75% change, +13.33% from MA200, +9.19% from MA50. Overbought.\n- **WEIR_CFD.UK**: +19.20% change, +37.14% from MA200, +18.21% from MA50. Very overbought. High vol (87%).\n- **CCL_CFD.UK**: +7.91% change, +26.41% from MA200, +14.62% from MA50. Overbought.\n- **CBG_CFD.UK**: -5.90% change, +15.79% from MA200, +3.59% from MA50. Mixed. Down day but above MAs.\n- **KGF_CFD.UK**: +9.28% change, +25.84% from MA200, +13.17% from MA50. Overbought.\n- **IMI_CFD.UK**: +8.27% change, +29.01% from MA200, +11.11% from MA50. Overbought.\n- **CNA_CFD.UK**: +2.34% change, +16.91% from MA200, +8.12% from MA50. Slightly overbought.\n- **HLMA_CFD.UK**: +15.35% change, +23.59% from MA200, +12.10% from MA50. Very overbought. Volatility 82%.\n- **MCG_CFD.UK**: +23.85% change, +12.69% from MA200, +32.02% from MA50. Extremely overbought. High vol (210%).\n- **LSEG_CFD.UK**: +9.55% change, -7.44% from MA200, +3.75% from MA50. Mixed. Up day, but below MA200.\n- **MKS_CFD.UK**: +9.41% change, +10.11% from MA200, +12.60% from MA50. Overbought.\n- **HMSO_CFD.UK**: +14.13% change, +22.52% from MA200, +10.01% from MA50. Very overbought. Volatility 87%.\n- **BT_CFD.UK**: +3.38% change, +10.17% from MA200, +9.33% from MA50. Mildly overbought.\n\n**Strategy**: Select exactly 5 candidates. For mean reversion, I want a mix of clear oversold and clear overbought stocks. The score should reflect the strength of the signal. I'll create a simple scoring system based on the magnitude of the deviation.\n\n**Scoring logic (0-100):**\n- **Oversold (LONG)**: Score based on negative change_1d_pct and negative distance_from_ma50_pct. The more negative, the higher the score (closer to 100).\n- **Overbought (SHORT)**: Score based on positive change_1d_pct and positive distance_from_ma50_pct. The more positive, the higher the score (closer to 100).\n\nLet me pick the top candidates:\n\n**LONG Candidates (Oversold):**\n1. **AML_CFD.UK**: -29.31% change, -31.56% from MA50. Extreme. Score: 95. (High vol, but strong signal)\n2. **BYIT_CFD.UK**: -12.67% change, -14.35% from MA50. Strong. Score: 85.\n3. **PAGE_CFD.UK**: -10.96% change, -8.41% from MA50. Good. Score: 80.\n4. **AT1_CFD.DE**: -12.65% change, -2.91% from MA50. Good daily drop, less stretched from MA50. Score: 78.\n5. **ADS_CFD.DE**: -8.17% change, -6.28% from MA50. Solid. Score: 75.\n6. **EZJ_CFD.UK**: -8.18% change, -8.26% from MA50. Solid. Score: 76.\n\n**SHORT Candidates (Overbought):**\n1. **MCG_CFD.UK**: +23.85% change, +32.02% from MA50. Extreme. Score: 95. (Very high vol)\n2. **WEIR_CFD.UK**: +19.20% change, +18.21% from MA50. Very strong. Score: 90.\n3. **HLMA_CFD.UK**: +15.35% change, +12.10% from MA50. Strong. Score: 85.\n4. **HMSO_CFD.UK**: +14.13% change, +10.01% from MA50. Strong. Score: 82.\n5. **KGF_CFD.UK**: +9.28% change, +13.17% from MA50. Good. Score: 80.\n6. **IMI_CFD.UK**: +8.27% change, +11.11% from MA50. Good. Score: 78.\n\n**Selection of 5:**\nI need to pick exactly 5. For a balanced scan, I should include both long and short candidates. The strategy doesn't specify the ratio, but a mix is logical for a mean-reversion universe scan. Let's pick the strongest signals overall.\n\n1. **AML_CFD.UK (LONG)**: Extremely oversold. Strongest signal. Score 95.\n2. **MCG_CFD.UK (SHORT)**: Extremely overbought. Strongest signal. Score 95.\n3. **WEIR_CFD.UK (SHORT)**: Very overbought. Score 90.\n4. **BYIT_CFD.UK (LONG)**: Strongly oversold. Score 85.\n5. **HLMA_CFD.UK (SHORT)**: Strongly overbought. Score 85.\n\nThis gives me 3 SHORT and 2 LONG candidates. This seems reasonable. The sector constraint is impossible to enforce because all sector fields are empty. I must state this in the rejected reasons.\n\n**Selection Reason Wording**: Must be \"short, concrete, indicator-based\". No narrative.\n- For LONG (oversold): \"Oversold: -X% vs MA50, -Y% 1d\"\n- For SHORT (overbought): \"Overbought: +X% vs MA50, +Y% 1d\"\n\nLet me refine the scores and reasons:\n- **AML_CFD.UK**: change_1d_pct = -29.31, distance_from_ma50_pct = -31.56. Score: 95. Reason: \"Oversold: -31.6% vs MA50, -29.3% 1d\"\n- **MCG_CFD.UK**: change_1d_pct = +23.85, distance_from_ma50_pct = +32.02. Score: 95. Reason: \"Overbought: +32.0% vs MA50, +23.9% 1d\"\n- **WEIR_CFD.UK**: change_1d_pct = +19.20, distance_from_ma50_pct = +18.21. Score: 90. Reason: \"Overbought: +18.2% vs MA50, +19.2% 1d\"\n- **BYIT_CFD.UK**: change_1d_pct = -12.67, distance_from_ma50_pct = -14.35. Score: 85. Reason: \"Oversold: -14.3% vs MA50, -12.7% 1d\"\n- **HLMA_CFD.UK**: change_1d_pct = +15.35, distance_from_ma50_pct = +12.10. Score: 85. Reason: \"Overbought: +12.1% vs MA50, +15.3% 1d\"\n\nThis looks good. The scores are integers 0-100. The reasons are short and indicator-based.\n\n**Rejected Common Reasons**:\n- \"Sector unknown: cannot enforce sector concentration limit\"\n- \"Earnings data unknown: cannot filter on earnings date\"\n- \"News/sentiment data unknown: cannot filter on event-driven moves\"\n- \"Insufficient mean reversion signal: deviation from MA50 and 1d change not significant enough\"\n\nI should list the most relevant reasons why many stocks were rejected. The first three are systemic data gaps. The last one is the actual strategy filter.\n\nLet me double-check the constraints:\n- Exactly 5 candidates: Yes, I have 5.\n- Avoid earnings: Not applicable (all null).\n- Avoid binary/event-driven: Not applicable (no news data).\n- Avoid sector concentration: Not enforceable (all sector fields empty). I must mention this.\n- No narrative: The reasons are concise and data-driven.\n- Only JSON output",
          "reasoning_details": [
            {
              "text": " The user wants me to act as an \"Arena strategy executor\" for a mean-reversion strategy. I need to follow strict rules and output ONLY valid JSON. I cannot invent any data. If a field is null/empty, I must treat it as unknown.\n\nLet me break down the task:\n- **Task**: stage1_universe_scan\n- **Region**: EU\n- **Strategy**: meanrev-v1 (Two-Stage LLM Mean-Reversion)\n- **Rules**:\n  - max_positions: 10\n  - max_buys_per_run: 2\n  - avoid_overtrading: true\n  - max_sells_per_run: 2\n  - max_gross_leverage: 3.0\n  - fixed_position_size: 3000.0\n  - universe_symbols_eu: [] (empty)\n  - universe_symbols_us: [] (empty)\n- **Selection Rules**:\n  - Horizon: 2-10 trading days\n  - Target: short-term mean reversion in liquid equities\n  - Constraints:\n    - Select exactly 5 candidates\n    - Avoid earnings within 5 trading days (if known)\n    - Avoid highly binary/event-driven moves (if known)\n    - Avoid sector concentration: max 1 per sector unless clearly justified (sector may be unknown)\n- **Universe**: A list of 40 stocks with various data points. Most have null/unknown values for fundamentals, earnings, sentiment, etc. The data is mostly technical: price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct.\n\n**Key points:**\n- I must select **exactly 5 candidates** for mean reversion.\n- The selection must be based on **short-term mean reversion** indicators.\n- I should avoid stocks with earnings within 5 days, but **all earnings data is null/unknown**, so this constraint is effectively not applicable based on the provided data.\n- I should avoid binary/event-driven moves, but **no news headlines or sentiment scores are provided**, so I cannot assess this. I must rely on technical indicators.\n- I should avoid sector concentration, but **all sector data is empty/unknown**. Since sectors are unknown, I cannot enforce this constraint strictly. I should select based on technicals and note this limitation.\n- I must output **only valid JSON**.\n- I must be a **constrained selector (no narrative)**.\n- I cannot invent prices, fundamentals, earnings dates, or sentiment.\n\n**Mean Reversion Logic:**\nFor mean reversion, I want stocks that have moved significantly away from their moving averages and are likely to revert. Typically:\n- Large **negative** 1-day change (oversold) -> potential BUY candidate for upward reversion.\n- Large **positive** 1-day change (overbought) -> potential SELL/SHORT candidate for downward reversion.\n- Large absolute distance from MA50 or MA200 suggests deviation from mean.\n- High volatility is common but should be considered for risk.\n\nSince this is a **scan** for candidates, I should identify both potential LONG candidates (oversold) and potential SHORT candidates (overbought). The strategy is \"Two-Stage\", so this scan is likely identifying candidates for both sides. The output schema asks for `selected_stocks` with a `mean_reversion_score` and `selection_reason`. It doesn't explicitly separate long/short, but the reason should indicate the direction.\n\nLet me analyze the universe for mean reversion signals:\n\n**Oversold (Potential LONG candidates):**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Strong oversold signal.\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Very strong oversold, but extreme move might be event-driven (unknown). High volatility (109%).\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Oversold.\n- **STNE_CFD.US**: -13.54% change, -10.99% from MA200, -10.38% from MA50. Oversold. (US symbol, but in EU scan? The symbol has .US, but the task is EU region. The data includes US symbols. I should consider all provided, but maybe prioritize .UK, .DE, .FR, .PT, .BE? The task says \"region\": \"EU\", but the universe mixes them. I'll treat the region as a general context but evaluate all symbols given. The strategy doesn't forbid US symbols in an EU scan explicitly. I'll select the best candidates regardless of suffix, but note that .US symbols might be less relevant. Let me check the instructions again. The strategy has `universe_symbols_eu` and `universe_symbols_us` which are empty. This suggests the universe is being provided dynamically. The region flag might be for context. I'll focus on the technicals and select the best 5 mean-reversion candidates from the *provided list*, interpreting \"EU\" as the primary focus but not strictly limiting to EU tickers if the data is mixed. Actually, looking at the symbols: VTY, WEIR, UTG, CCL, CBG, KGF, ADS, AML, COFB, IMI, AT1, CNA, STNE, HLMA, SHC, IPR, IPO, ATG, MDB, EXPN, CCC, BYIT, WKP, CCH, MCG, EZJ, LSEG, SBGI, MKS, BOSS, ALK, HMSO, BT, MSLH, PAGE, NBIS, COIN, NXT, SDG, SNOW, GOOS, PSN, ARG, WLK, UHAL, IVZ, JLL. Many are .UK, .DE, .FR, .PT, .BE. STNE, MDB, SBGI, ALK, NBIS, COIN, SNOW, GOOS, WLK, UHAL, IVZ, JLL are .US or .CFD.US. The task is EU region. I should probably prioritize EU symbols (.UK, .DE, .FR, .PT, .BE). Let me filter the list to EU symbols first.\n\n**EU Symbols:**\n- VTY_CFD.UK\n- WEIR_CFD.UK\n- UTG_CFD.UK\n- CCL_CFD.UK\n- CBG_CFD.UK\n- KGF_CFD.UK\n- ADS_CFD.DE\n- AML_CFD.UK\n- COFB_CFD.BE\n- IMI_CFD.UK\n- AT1_CFD.DE\n- CNA_CFD.UK\n- HLMA_CFD.UK\n- SHC_CFD.UK\n- IPR_CFD.PT\n- IPO_CFD.UK\n- ATG_CFD.UK\n- EXPN_CFD.UK\n- CCC_CFD.UK\n- BYIT_CFD.UK\n- WKP_CFD.UK\n- CCH_CFD.UK\n- MCG_CFD.UK\n- EZJ_CFD.UK\n- LSEG_CFD.UK\n- MKS_CFD.UK\n- BOSS_CFD.DE\n- HMSO_CFD.UK\n- BT_CFD.UK\n- MSLH_CFD.UK\n- PAGE_CFD.UK\n- NXT_CFD.UK\n- SDG_CFD.FR\n- PSN_CFD.UK\n- ARG_CFD.FR\n\n**US Symbols (to be ignored for EU scan):**\n- STNE_CFD.US\n- MDB_CFD.US\n- SBGI_CFD.US\n- ALK_CFD.US\n- NBIS_CFD.US\n- COIN_CFD.US\n- SNOW_CFD.US\n- GOOS_CFD.US\n- WLK_CFD.US\n- UHAL_CFD.US\n- IVZ_CFD.US\n- JLL_CFD.US\n\nOkay, I'll focus ONLY on EU symbols for the selection.\n\n**Re-evaluating EU Oversold candidates:**\n- **ADS_CFD.DE**: -8.17% change, -16.77% from MA200, -6.28% from MA50. Good candidate. Volatility 43% (moderate).\n- **AML_CFD.UK**: -29.31% change, -41.08% from MA200, -31.56% from MA50. Extreme oversold. High vol (109%). Risky but strong signal.\n- **AT1_CFD.DE**: -12.65% change, -10.80% from MA200, -2.91% from MA50. Good candidate. Volatility 98%.\n- **EZJ_CFD.UK**: -8.18% change, -10.29% from MA200, -8.26% from MA50. Good candidate. Volatility 86%.\n- **PAGE_CFD.UK**: -10.96% change, -19.31% from MA200, -8.41% from MA50. Good candidate. Volatility 112%.\n- **BYIT_CFD.UK**: -12.67% change, -28.03% from MA200, -14.35% from MA50. Good candidate. Volatility 88%.\n- **UTG_CFD.UK**: +3.46% change, -23.93% from MA200, -3.69% from MA50. Not oversold on daily change, but far from MA200. Mixed signal.\n- **EXPN_CFD.UK**: +8.67% change, -22.50% from MA200, -7.08% from MA50. Overbought daily, but below MAs. Mixed.\n\n**EU Overbought (Potential SHORT candidates):**\n- **VTY_CFD.UK**: +7.75% change, +13.33% from MA200, +9.19% from MA50. Overbought.\n- **WEIR_CFD.UK**: +19.20% change, +37.14% from MA200, +18.21% from MA50. Very overbought. High vol (87%).\n- **CCL_CFD.UK**: +7.91% change, +26.41% from MA200, +14.62% from MA50. Overbought.\n- **CBG_CFD.UK**: -5.90% change, +15.79% from MA200, +3.59% from MA50. Mixed. Down day but above MAs.\n- **KGF_CFD.UK**: +9.28% change, +25.84% from MA200, +13.17% from MA50. Overbought.\n- **IMI_CFD.UK**: +8.27% change, +29.01% from MA200, +11.11% from MA50. Overbought.\n- **CNA_CFD.UK**: +2.34% change, +16.91% from MA200, +8.12% from MA50. Slightly overbought.\n- **HLMA_CFD.UK**: +15.35% change, +23.59% from MA200, +12.10% from MA50. Very overbought. Volatility 82%.\n- **MCG_CFD.UK**: +23.85% change, +12.69% from MA200, +32.02% from MA50. Extremely overbought. High vol (210%).\n- **LSEG_CFD.UK**: +9.55% change, -7.44% from MA200, +3.75% from MA50. Mixed. Up day, but below MA200.\n- **MKS_CFD.UK**: +9.41% change, +10.11% from MA200, +12.60% from MA50. Overbought.\n- **HMSO_CFD.UK**: +14.13% change, +22.52% from MA200, +10.01% from MA50. Very overbought. Volatility 87%.\n- **BT_CFD.UK**: +3.38% change, +10.17% from MA200, +9.33% from MA50. Mildly overbought.\n\n**Strategy**: Select exactly 5 candidates. For mean reversion, I want a mix of clear oversold and clear overbought stocks. The score should reflect the strength of the signal. I'll create a simple scoring system based on the magnitude of the deviation.\n\n**Scoring logic (0-100):**\n- **Oversold (LONG)**: Score based on negative change_1d_pct and negative distance_from_ma50_pct. The more negative, the higher the score (closer to 100).\n- **Overbought (SHORT)**: Score based on positive change_1d_pct and positive distance_from_ma50_pct. The more positive, the higher the score (closer to 100).\n\nLet me pick the top candidates:\n\n**LONG Candidates (Oversold):**\n1. **AML_CFD.UK**: -29.31% change, -31.56% from MA50. Extreme. Score: 95. (High vol, but strong signal)\n2. **BYIT_CFD.UK**: -12.67% change, -14.35% from MA50. Strong. Score: 85.\n3. **PAGE_CFD.UK**: -10.96% change, -8.41% from MA50. Good. Score: 80.\n4. **AT1_CFD.DE**: -12.65% change, -2.91% from MA50. Good daily drop, less stretched from MA50. Score: 78.\n5. **ADS_CFD.DE**: -8.17% change, -6.28% from MA50. Solid. Score: 75.\n6. **EZJ_CFD.UK**: -8.18% change, -8.26% from MA50. Solid. Score: 76.\n\n**SHORT Candidates (Overbought):**\n1. **MCG_CFD.UK**: +23.85% change, +32.02% from MA50. Extreme. Score: 95. (Very high vol)\n2. **WEIR_CFD.UK**: +19.20% change, +18.21% from MA50. Very strong. Score: 90.\n3. **HLMA_CFD.UK**: +15.35% change, +12.10% from MA50. Strong. Score: 85.\n4. **HMSO_CFD.UK**: +14.13% change, +10.01% from MA50. Strong. Score: 82.\n5. **KGF_CFD.UK**: +9.28% change, +13.17% from MA50. Good. Score: 80.\n6. **IMI_CFD.UK**: +8.27% change, +11.11% from MA50. Good. Score: 78.\n\n**Selection of 5:**\nI need to pick exactly 5. For a balanced scan, I should include both long and short candidates. The strategy doesn't specify the ratio, but a mix is logical for a mean-reversion universe scan. Let's pick the strongest signals overall.\n\n1. **AML_CFD.UK (LONG)**: Extremely oversold. Strongest signal. Score 95.\n2. **MCG_CFD.UK (SHORT)**: Extremely overbought. Strongest signal. Score 95.\n3. **WEIR_CFD.UK (SHORT)**: Very overbought. Score 90.\n4. **BYIT_CFD.UK (LONG)**: Strongly oversold. Score 85.\n5. **HLMA_CFD.UK (SHORT)**: Strongly overbought. Score 85.\n\nThis gives me 3 SHORT and 2 LONG candidates. This seems reasonable. The sector constraint is impossible to enforce because all sector fields are empty. I must state this in the rejected reasons.\n\n**Selection Reason Wording**: Must be \"short, concrete, indicator-based\". No narrative.\n- For LONG (oversold): \"Oversold: -X% vs MA50, -Y% 1d\"\n- For SHORT (overbought): \"Overbought: +X% vs MA50, +Y% 1d\"\n\nLet me refine the scores and reasons:\n- **AML_CFD.UK**: change_1d_pct = -29.31, distance_from_ma50_pct = -31.56. Score: 95. Reason: \"Oversold: -31.6% vs MA50, -29.3% 1d\"\n- **MCG_CFD.UK**: change_1d_pct = +23.85, distance_from_ma50_pct = +32.02. Score: 95. Reason: \"Overbought: +32.0% vs MA50, +23.9% 1d\"\n- **WEIR_CFD.UK**: change_1d_pct = +19.20, distance_from_ma50_pct = +18.21. Score: 90. Reason: \"Overbought: +18.2% vs MA50, +19.2% 1d\"\n- **BYIT_CFD.UK**: change_1d_pct = -12.67, distance_from_ma50_pct = -14.35. Score: 85. Reason: \"Oversold: -14.3% vs MA50, -12.7% 1d\"\n- **HLMA_CFD.UK**: change_1d_pct = +15.35, distance_from_ma50_pct = +12.10. Score: 85. Reason: \"Overbought: +12.1% vs MA50, +15.3% 1d\"\n\nThis looks good. The scores are integers 0-100. The reasons are short and indicator-based.\n\n**Rejected Common Reasons**:\n- \"Sector unknown: cannot enforce sector concentration limit\"\n- \"Earnings data unknown: cannot filter on earnings date\"\n- \"News/sentiment data unknown: cannot filter on event-driven moves\"\n- \"Insufficient mean reversion signal: deviation from MA50 and 1d change not significant enough\"\n\nI should list the most relevant reasons why many stocks were rejected. The first three are systemic data gaps. The last one is the actual strategy filter.\n\nLet me double-check the constraints:\n- Exactly 5 candidates: Yes, I have 5.\n- Avoid earnings: Not applicable (all null).\n- Avoid binary/event-driven: Not applicable (no news data).\n- Avoid sector concentration: Not enforceable (all sector fields empty). I must mention this.\n- No narrative: The reasons are concise and data-driven.\n- Only JSON output",
              "type": "reasoning.text",
              "index": 0,
              "format": "unknown"
            }
          ]
        },
        "logprobs": null,
        "finish_reason": "length",
        "native_finish_reason": "length"
      }
    ],
    "created": 1772625042,
    "provider": "Nebius"
  }
}