Arena run #1749

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": "BOY_CFD.UK", "name": "BODYCOTE PLC", "sector": "", "last_price": 775.4, "change_1d_pct": 24.163330664531625, "distance_from_ma200_pct": 28.652243313347768, "distance_from_ma50_pct": 18.63612217470385, "volatility_20d_pct": 103.46484337510964, "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": "ASHM_CFD.UK", "name": "ASHMORE GROUP PLC", "sector": "", "last_price": 244.8, "change_1d_pct": 1.4084507042253502, "distance_from_ma200_pct": 40.45532080672305, "distance_from_ma50_pct": 24.88993054528128, "volatility_20d_pct": 134.25405461264828, "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": "GRI_CFD.UK", "name": "GRAINGER PLC", "sector": "", "last_price": 189.6, "change_1d_pct": -1.455301455301461, "distance_from_ma200_pct": -6.198447178618971, "distance_from_ma50_pct": -0.23237017579308406, "volatility_20d_pct": 71.77829509727196, "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": "N91_CFD.UK", "name": "NINETY ONE PLC", "sector": "", "last_price": 241.4, "change_1d_pct": -2.739726027397249, "distance_from_ma200_pct": 22.140556255617817, "distance_from_ma50_pct": 6.809338693563394, "volatility_20d_pct": 84.21236037483679, "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": 372.74, "change_1d_pct": -26.625984251968504, "distance_from_ma200_pct": -13.365429078084645, "distance_from_ma50_pct": -22.129222788055436, "volatility_20d_pct": 169.0552301316783, "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": "GFTU_CFD.UK", "name": "GRAFTON GROUP PLC-UTS", "sector": "", "last_price": 955.8, "change_1d_pct": 1.0434175890393638, "distance_from_ma200_pct": 3.963270661870655, "distance_from_ma50_pct": 3.4837359036161253, "volatility_20d_pct": 51.04515943079597, "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": "TPK_CFD.UK", "name": "TRAVIS PERKINS PLC", "sector": "", "last_price": 601.57, "change_1d_pct": -1.21029986533977, "distance_from_ma200_pct": 0.5267641208837714, "distance_from_ma50_pct": -4.983077425595306, "volatility_20d_pct": 108.19399806448348, "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": "PNN_CFD.UK", "name": "PENNON GROUP PLC", "sector": "", "last_price": 551.0, "change_1d_pct": 7.699224018295192, "distance_from_ma200_pct": 13.698549858568065, "distance_from_ma50_pct": 7.285607918028347, "volatility_20d_pct": 97.33547101641821, "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": "MGNS_CFD.UK", "name": "MORGAN SINDALL GROUP PLC", "sector": "", "last_price": 4860.0, "change_1d_pct": 8.928311274976753, "distance_from_ma200_pct": 19.027421064583947, "distance_from_ma50_pct": 4.129720434840567, "volatility_20d_pct": 174.2493967907277, "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": "INVP_CFD.UK", "name": "INVESTEC PLC", "sector": "", "last_price": 642.0, "change_1d_pct": 9.369676320272568, "distance_from_ma200_pct": 20.33187982293969, "distance_from_ma50_pct": 11.734877154290203, "volatility_20d_pct": 115.28654302325035, "market_cap_usd": null, 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"news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "UKW_CFD.UK", "name": "GREENCOAT UK WIND PLC", "sector": "", "last_price": 100.4, "change_1d_pct": 3.976801988401002, "distance_from_ma200_pct": -9.084405711745047, "distance_from_ma50_pct": 0.06418459649417674, "volatility_20d_pct": 80.6750581480199, "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": "NXI_CFD.FR", "name": "Nexity SA", "sector": "", "last_price": 8.87, "change_1d_pct": 2.9002320185614883, "distance_from_ma200_pct": -3.8172723359173766, "distance_from_ma50_pct": 5.139882271780061, "volatility_20d_pct": 60.36558349705058, "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": "AKAM_CFD.US", "name": "Akamai Technologies Inc", "sector": "", "last_price": 149.65, "change_1d_pct": 29.533454513979063, "distance_from_ma200_pct": 67.35414384628247, "distance_from_ma50_pct": 40.951064126376544, "volatility_20d_pct": 154.28386628635903, "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": "SFM_CFD.US", "name": "SPROUTS FARMERS MARKET INC", "sector": "", "last_price": 83.96, "change_1d_pct": 5.823040080665476, "distance_from_ma200_pct": -11.859033241826022, "distance_from_ma50_pct": 8.726337517838312, "volatility_20d_pct": 109.60367883354618, "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": "APH_CFD.US", "name": "Amphenol Corporation", "sector": "", "last_price": 129.32, "change_1d_pct": -6.405153072302228, "distance_from_ma200_pct": -2.0376873106913607, "distance_from_ma50_pct": -5.5835102608208125, "volatility_20d_pct": 75.0166002676417, "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": "ALB_CFD.US", "name": "Albemarle Corp", "sector": "", "last_price": 206.33, "change_1d_pct": 5.340276714147141, "distance_from_ma200_pct": 55.2867137024927, "distance_from_ma50_pct": 14.528901159776497, "volatility_20d_pct": 129.20248363317458, "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": "GT_CFD.US", "name": "GOODYEAR TIRE & RUBBER CO", "sector": "", "last_price": 6.6, "change_1d_pct": -6.647807637906656, "distance_from_ma200_pct": -19.067063973807162, "distance_from_ma50_pct": -5.438707089231487, "volatility_20d_pct": 91.97055948582533, "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": "TSCO_CFD.US", "name": "TRACTOR SUPPLY COMPANY", "sector": "", "last_price": 30.97, "change_1d_pct": -4.941682013505222, "distance_from_ma200_pct": -40.941691123328326, "distance_from_ma50_pct": -28.859547567855635, "volatility_20d_pct": 73.43852653574369, "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": "CDW_CFD.US", "name": "CDW Corp", "sector": "", "last_price": 105.95, "change_1d_pct": -22.522851919561237, "distance_from_ma200_pct": -26.021248903759897, "distance_from_ma50_pct": -15.385807177072463, "volatility_20d_pct": 133.8807652870453, "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": "SMIN_CFD.UK", "name": "SMITHS GROUP PLC", "sector": "", "last_price": 2557.0, "change_1d_pct": 4.6235679214402525, "distance_from_ma200_pct": 14.883274190387418, "distance_from_ma50_pct": 3.9730049158046343, "volatility_20d_pct": 101.30183705216716, "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": "We are given a task: \"stage1_universe_scan\" with region \"EU\" and strategy \"meanrev-v1\" which indicates a Two-Stage LLM Mean-Reversion strategy. We have selection rules: Horizon is \"2-10 trading days\" for short-term mean reversion in liquid equities. Must select exactly 5 candidates. Must avoid earnings within 5 trading days if known (but we do not have any earnings data, all null). Also avoid highly binary/event-driven moves if known (none given). Avoid sector concentration: max 1 per sector unless clearly justified.\n\nWe have a universe of around 50 symbols, each with a number of technical and price data: last_price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct, market_cap_usd (null), avg_daily_volume_usd (null), days_to_next_earnings (null), etc.\n\nWe need to produce selected_stocks: \"symbol\", \"mean_reversion_score\" (integer between 0 and 100), and \"selection_reason\". The selection reason should be short, concrete, indicator-based.\n\nWe need to reject common reasons for the entire selection: maybe mention some common rejections. The output format expects a JSON with these fields.\n\nWe need to apply the strategy: mean-reversion on liquid equities (presumably UK and EU equities in the dataset). The dataset includes many UK symbols with .UK or .DE, also US symbols, but we need to restrict to region EU: That probably means symbols with .UK .DE .NL .BE .FR (EU region). The dataset includes many .UK, some .DE (Germany), .NL (Netherlands), .BE (Belgium), .FR (France), and some .US. So we need to limit to EU symbols.\n\nWe also need to select exactly 5 candidates. But in the selection rules, we have \"max positions 10\" but \"selection rule: Select exactly 5 candidates\".\n\nThus we need to determine appropriate candidates: short-term mean reversion. Means perhaps those that have moved far from a mean (MA), but we want to buy those that are oversold (negative distance from moving averages) - mean reversion expects price to bounce back. However some negative distance also may be below the moving average; but to be safe, need to think in terms of relative to both MA200 and MA50? The given fields are \"distance_from_ma200_pct\", \"distance_from_ma50_pct\". That is percent difference between price and moving average. A positive difference means price is above the MA, negative means price is below.\n\nMean reversion: We might want to find stocks where price has deviated significantly from average in either direction, expecting reversal. The typical mean-reversion approach often picks oversold stocks when they are far below the moving average (negative distance) or perhaps after large falls.\n\nGiven short-term 2-10 days, perhaps look at distance_from_ma50 more important (since the horizon is shorter). Also volatility: want moderate volatility to allow mean reversion within 2-10 days, but not extremely high (like 9562% volatility for HAIN_CFD.US suggests extremely high, likely due to extremely low price, so not viable). Also want avoid extremely high volatility > 200% perhaps. However some high volatility also may have huge daily moves.\n\nWe also may consider change_1d_pct: Large positive or negative moves. For oversold we might pick those with large negative changes recently (they might be due for rebound), but may also avoid extremely large moves because they could be binary event.\n\nWe need to avoid sector concentration: cannot have more than 1 per sector if sectors unknown; we could just assume we cannot have duplicates, which we can satisfy.\n\nWe need to ensure liquidity: We need to consider avg_daily_volume_usd but it's not provided, but perhaps we can assume they all are some volume.\n\nWe could also prefer stocks with moderate distances (not too extreme). Additionally, distance both above and below could be considered.\n\nWe need exactly 5 candidates. Use each field to compute an overall mean reversion score.\n\nProbably we need to assign scores based on absolute distance from 50-day moving average, but normalized within dataset.\n\nOne possible approach: For each candidate, we can compute a mean_reversion_score: based on absolute distance from moving average (either above or below) and the magnitude of 20d volatility. If price is far from the moving average, might revert. The score maybe 100 - scaled volatility and distance. But we need a deterministic methodology.\n\nWe also need to ensure selection is consistent with the selection rules: Avoid earnings within 5 days (unknown, so ignore). Avoid event-driven (unknown). Avoid sector concentration: unknown sector values.\n\nGiven unknown sectors are blank for all symbols, we cannot apply sector constraints, just avoid selecting more than 1 per unknown sector? Since blank, it's unclear. We might just not worry about sector concentration due to lack of data.\n\nWe also need to avoid extremely low priced stocks: those like HAIN_CFD.US at 0.7051, which has extremely huge volatility (9562%?) likely due to cheap price; not suitable.\n\nWe also need to avoid those with large negative changes maybe because they could be binary events; but for mean reversion, they may be oversold.\n\nGiven data, we need to sort by absolute value of distance_from_ma50 (or distance from ma200?) that are high? Actually large absolute percent difference implies price far from moving average, thus maybe more likely revert.\n\nBut we need to decide direction: If price is above moving average it may revert downward; if below, revert upward; both are reversion.\n\nWe can treat a \"mean reversion score\" that is simply scaled absolute distance. Because we want to capture distance in absolute terms.\n\nGiven volatility: low may indicate slower reversion; high may be too risky. So perhaps we can set score = (abs(distance_ma200) + abs(distance_ma50))/2 - volatility factor something.\n\nBut here we can just produce simple scoring: assign something based on absolute distance from ma50 and magnitude of recent decline (perhaps negative change = oversold). Let's pick those that have had large positive or negative deviations.\n\nBut we also want to avoid selecting huge outliers: e.g., Ocado with -27% change (huge negative). However its volatility is 249% which is high. But that might be due to binary event. Avoid them perhaps.\n\nWe might choose candidates where absolute distance from either MA is high but not extremely extreme, volatility moderate (maybe between 50% and 150%), and price > 1? Not exactly needed, but avoid low-priced.\n\nOur universe includes some heavily discounted large changes: HAS_CFD.UK (-41% change) and Ocado (-27%). Others: CBG (-26% change) also big negative. Many large positives: BOY (+24%), etc.\n\nWe want to find those for potential mean reversion: oversold that have negative change and are below moving averages. Or perhaps we could also consider those that have risen too far above moving averages, expecting reversal down.\n\nGiven typical mean reversion: often you go long oversold and short overbought. But we can just consider both.\n\nBut the selection constraints: \"short-term mean reversion in liquid equities\" likely implies going long (buy) on oversold stocks (price below moving average) because likely to revert upward. Similarly perhaps short on those above.\n\nGiven that we are in a long-only scenario? Not clear. The strategy likely intends to go long. We'll choose oversold ones.\n\nThus we want those with negative distance to ma50 (price below 50-day MA) and negative or moderate price change (they fell recently). But also we could include those above if far above and might revert down (but that would be a short). Since the strategy doesn't mention direction, we can just choose candidates that will likely revert both ways.\n\nSimplify: Choose 5 with largest absolute distances (either positive or negative) but moderate volatility. Exclude extremely high volatility > 200% (like HAS_CFD: 238%, OCADO: 249%, etc). Also exclude huge volatility of 9562% (HAIN). Also exclude extremely large price moves (like HAS had -41% change one day, but that might be too extreme but still maybe can be considered oversold due to big drop - but volatility >238% is high). However some other high changes may be okay.\n\nLet's define criteria:\n\n- distance_from_ma50: absolute >= 15% (indicates overshoot/undershoot)\n- distance_from_ma200: less important\n- volatility_20d: <=150% (moderately high risk)\n- change_1d%: ignore (maybe choose from either negative or positive.)\n- price: avoid low price < 0.5 (makes sense)\n- avoid with extremely high change >+20% maybe? Not necessary.\n\nBut as constraints, we also must avoid sector concentration: unknown sectors; but we can accept any.\n\nNow let's compute for each EU symbol:\n\nList of symbols with region EU:\n\nWe'll enumerate each entry and compute relevant metrics.\n\nWe'll parse each entry.\n\nLet me list all symbol records with region:\n\n- BOY_CFD.UK\n- ASHM_CFD.UK\n- GRI_CFD.UK\n- N91_CFD.UK\n- CBG_CFD.UK\n- DLN_CFD.UK\n- GFTU_CFD.UK\n- TPK_CFD.UK\n- PNN_CFD.UK\n- MGNS_CFD.UK\n- INVP_CFD.UK\n- BRNK_CFD.DE\n- BNZL_CFD.UK\n- HAIN_CFD.US (non-EU)\n- OPTU_CFD.US (non-EU)\n- MCG_CFD.UK\n- HFG_CFD.UK\n- HE_CFD.US (non-EU)\n- WKP_CFD.UK\n- OCDO_CFD.UK\n- ADJ_CFD.DE\n- PSON_CFD.UK\n- LMP_CFD.UK\n- HAS_CFD.UK\n- SBT_CFD.FR\n- ATG_CFD.UK\n- MOS_CFD.US (non-EU)\n- CPG_CFD.UK\n- GPK_CFD.US (non-EU)\n- HMSO_CFD.UK\n- SPX_CFD.UK\n- BLND_CFD.UK\n- CTEC_CFD.UK\n- ECMPA_CFD.NL\n- PHP_CFD.UK\n- REC_CFD.BE\n- MNDI_CFD.UK\n- GLW_CFD.US (non-EU)\n- TER_CFD.US (non-EU)\n- UKW_CFD.UK\n- NXI_CFD.FR\n- AKAM_CFD.US (non-EU)\n- SFM_CFD.US (non-EU)\n- APH_CFD.US (non-EU)\n- ALB_CFD.US (non-EU)\n- GT_CFD.US (non-EU)\n- TSCO_CFD.US (non-EU)\n- CDW_CFD.US (non-EU)\n- SMIN_CFD.UK\n\nAnd possibly some missing.\n\nWe will only consider EU region. That excludes US ones.\n\nThus filter out only those with .UK .DE .NL .BE .FR.\n\nThus from above list, all .UK and .DE .NL .BE .FR are EU, .US are not.\n\nList again each EU symbol:\n\n- BOY_CFD.UK: price 775.4, change +24.163%, distance_ma200 +28.65% (above), distance_ma50 +18.636%, volatility 103.4648% (moderate high)\n- ASHM_CFD.UK: price 244.8, change +1.408%, d_ma200 +40.455% (above), d_ma50 +24.8899% (above), volatility 134.254% (high)\n- GRI_CFD.UK: price 189.6, change -1.455%, d_ma200 -6.198% (below), d_ma50 -0.232% (slightly below), volatility 71.78% (moderate)\n- N91_CFD.UK: price 241.4, change -2.74%, d_ma200 +22.1406% (above), d_ma50 +6.809% (above), volatility 84.212% (moderate)\n- CBG_CFD.UK: price 372.74, change -26.626%, d_ma200 -13.365% (below), d_ma50 -22.129% (below), volatility 169.055% (high)\n- DLN_CFD.UK: price 1953, change +10.401%, d_ma200 +5.069% (above), d_ma50 +12.150% (above), volatility 115.39% (high)\n- GFTU_CFD.UK: price 955.8, change +1.043%, d_ma200 +3.963% (above), d_ma50 +3.484% (above), volatility 51.045% (moderate low)\n- TPK_CFD.UK: price 601.57, change -1.210%, d_ma200 +0.527% (above), d_ma50 -4.983% (below), volatility 108.19% (moderate)\n- PNN_CFD.UK: price 551, change +7.699%, d_ma200 +13.6985% (above), d_ma50 +7.286% (above), volatility 97.335% (moderate)\n- MGNS_CFD.UK: price 4860, change +8.928%, d_ma200 +19.027% (above), d_ma50 +4.1297% (above), volatility 174.249% (high)\n- INVP_CFD.UK: price 642, change +9.3697%, d_ma200 +20.3319% (above), d_ma50 +11.7349% (above), volatility 115.2865% (high)\n- BRNK_CFD.DE: price 1.45, change +5.0725%, d_ma200 -21.7367% (below), d_ma50 -6.7416% (below), volatility 192.100% (very high)\n- BNZL_CFD.UK: price 2463, change +11.2466%, d_ma200 +6.7538% (above), d_ma50 +13.3855% (above), volatility 95.7245% (moderate)\n- MCG_CFD.UK: price 26.38, change -4.14244%, d_ma200 -3.65775% (below), d_ma50 +12.386% (above), volatility 328.690% (very high)\n- HFG_CFD.UK: price 548, change +10.2616%, d_ma200 -29.343% (below), d_ma50 -8.64099% (below), volatility 169.6338% (high)\n- WKP_CFD.UK: price 423, change +3.6765%, d_ma200 +1.864% (above), d_ma50 +5.808% (above), volatility 70.223% (moderate)\n- OCDO_CFD.UK: price 186, change -27.372%, d_ma200 -27.532% (below), d_ma50 -19.095% (below), volatility 249.455% (very high)\n- ADJ_CFD.DE: price 0.172, change 0%, d_ma200 -12.8827% (below), d_ma50 -4.60344% (below), volatility 273.640% (very high)\n- PSON_CFD.UK: price 1130.5, change +10.8877%, d_ma200 +8.6569% (above), d_ma50 +14.0706% (above), volatility 113.3546% (moderate high)\n- LMP_CFD.UK: price 192.3, change -1.5865%, d_ma200 +0.9847% (above), d_ma50 -0.5701% (slightly below), volatility 115.3366% (moderate high)\n- HAS_CFD.UK: price 33.32, change -41.1307%, d_ma200 -48.07% (below), d_ma50 -36.9355% (below), volatility 238.6498% (very high)\n- SBT_CFD.FR: price 9.06, change -3.617%, d_ma200 -2.4709% (below), d_ma50 -2.093% (slightly below), volatility 44.8267% (low)\n- ATG_CFD.UK: price 326.5, change +12.0069%, d_ma200 -24.9873% (below), d_ma50 +5.8443% (above), volatility 172.4501% (high)\n- CPG_CFD.UK: price 29.86, change +10.3882%, d_ma200 -98.6279% (way below), d_ma50 -97.6459% (way below), volatility 603.5169% (extremely high)\n- HMSO_CFD.UK: price 355.4, change +14.1297%, d_ma200 +22.516% (above), d_ma50 +10.0069% (above), volatility 87.5494% (moderate)\n- SPX_CFD.UK: price 7168, change +7.06497%, d_ma200 +9.23398% (above), d_ma50 +1.20707% (above), volatility 136.3122% (high)\n- BLND_CFD.UK: price 391.7, change +13.0774%, d_ma200 +4.46325% (above), d_ma50 -0.2295% (near), volatility 119.3708% (moderate)\n- CTEC_CFD.UK: price 207.6, change -9.34498%, d_ma200 -16.703% (below), d_ma50 -11.1109% (below), volatility 116.5523% (moderate)\n- ECMPA_CFD.NL: price 27.55, change -1.0772%, d_ma200 +3.6903% (above), d_ma50 +1.123% (above), volatility 61.3682% (moderate low)\n-",
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              "text": "We are given a task: \"stage1_universe_scan\" with region \"EU\" and strategy \"meanrev-v1\" which indicates a Two-Stage LLM Mean-Reversion strategy. We have selection rules: Horizon is \"2-10 trading days\" for short-term mean reversion in liquid equities. Must select exactly 5 candidates. Must avoid earnings within 5 trading days if known (but we do not have any earnings data, all null). Also avoid highly binary/event-driven moves if known (none given). Avoid sector concentration: max 1 per sector unless clearly justified.\n\nWe have a universe of around 50 symbols, each with a number of technical and price data: last_price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct, market_cap_usd (null), avg_daily_volume_usd (null), days_to_next_earnings (null), etc.\n\nWe need to produce selected_stocks: \"symbol\", \"mean_reversion_score\" (integer between 0 and 100), and \"selection_reason\". The selection reason should be short, concrete, indicator-based.\n\nWe need to reject common reasons for the entire selection: maybe mention some common rejections. The output format expects a JSON with these fields.\n\nWe need to apply the strategy: mean-reversion on liquid equities (presumably UK and EU equities in the dataset). The dataset includes many UK symbols with .UK or .DE, also US symbols, but we need to restrict to region EU: That probably means symbols with .UK .DE .NL .BE .FR (EU region). The dataset includes many .UK, some .DE (Germany), .NL (Netherlands), .BE (Belgium), .FR (France), and some .US. So we need to limit to EU symbols.\n\nWe also need to select exactly 5 candidates. But in the selection rules, we have \"max positions 10\" but \"selection rule: Select exactly 5 candidates\".\n\nThus we need to determine appropriate candidates: short-term mean reversion. Means perhaps those that have moved far from a mean (MA), but we want to buy those that are oversold (negative distance from moving averages) - mean reversion expects price to bounce back. However some negative distance also may be below the moving average; but to be safe, need to think in terms of relative to both MA200 and MA50? The given fields are \"distance_from_ma200_pct\", \"distance_from_ma50_pct\". That is percent difference between price and moving average. A positive difference means price is above the MA, negative means price is below.\n\nMean reversion: We might want to find stocks where price has deviated significantly from average in either direction, expecting reversal. The typical mean-reversion approach often picks oversold stocks when they are far below the moving average (negative distance) or perhaps after large falls.\n\nGiven short-term 2-10 days, perhaps look at distance_from_ma50 more important (since the horizon is shorter). Also volatility: want moderate volatility to allow mean reversion within 2-10 days, but not extremely high (like 9562% volatility for HAIN_CFD.US suggests extremely high, likely due to extremely low price, so not viable). Also want avoid extremely high volatility > 200% perhaps. However some high volatility also may have huge daily moves.\n\nWe also may consider change_1d_pct: Large positive or negative moves. For oversold we might pick those with large negative changes recently (they might be due for rebound), but may also avoid extremely large moves because they could be binary event.\n\nWe need to avoid sector concentration: cannot have more than 1 per sector if sectors unknown; we could just assume we cannot have duplicates, which we can satisfy.\n\nWe need to ensure liquidity: We need to consider avg_daily_volume_usd but it's not provided, but perhaps we can assume they all are some volume.\n\nWe could also prefer stocks with moderate distances (not too extreme). Additionally, distance both above and below could be considered.\n\nWe need exactly 5 candidates. Use each field to compute an overall mean reversion score.\n\nProbably we need to assign scores based on absolute distance from 50-day moving average, but normalized within dataset.\n\nOne possible approach: For each candidate, we can compute a mean_reversion_score: based on absolute distance from moving average (either above or below) and the magnitude of 20d volatility. If price is far from the moving average, might revert. The score maybe 100 - scaled volatility and distance. But we need a deterministic methodology.\n\nWe also need to ensure selection is consistent with the selection rules: Avoid earnings within 5 days (unknown, so ignore). Avoid event-driven (unknown). Avoid sector concentration: unknown sector values.\n\nGiven unknown sectors are blank for all symbols, we cannot apply sector constraints, just avoid selecting more than 1 per unknown sector? Since blank, it's unclear. We might just not worry about sector concentration due to lack of data.\n\nWe also need to avoid extremely low priced stocks: those like HAIN_CFD.US at 0.7051, which has extremely huge volatility (9562%?) likely due to cheap price; not suitable.\n\nWe also need to avoid those with large negative changes maybe because they could be binary events; but for mean reversion, they may be oversold.\n\nGiven data, we need to sort by absolute value of distance_from_ma50 (or distance from ma200?) that are high? Actually large absolute percent difference implies price far from moving average, thus maybe more likely revert.\n\nBut we need to decide direction: If price is above moving average it may revert downward; if below, revert upward; both are reversion.\n\nWe can treat a \"mean reversion score\" that is simply scaled absolute distance. Because we want to capture distance in absolute terms.\n\nGiven volatility: low may indicate slower reversion; high may be too risky. So perhaps we can set score = (abs(distance_ma200) + abs(distance_ma50))/2 - volatility factor something.\n\nBut here we can just produce simple scoring: assign something based on absolute distance from ma50 and magnitude of recent decline (perhaps negative change = oversold). Let's pick those that have had large positive or negative deviations.\n\nBut we also want to avoid selecting huge outliers: e.g., Ocado with -27% change (huge negative). However its volatility is 249% which is high. But that might be due to binary event. Avoid them perhaps.\n\nWe might choose candidates where absolute distance from either MA is high but not extremely extreme, volatility moderate (maybe between 50% and 150%), and price > 1? Not exactly needed, but avoid low-priced.\n\nOur universe includes some heavily discounted large changes: HAS_CFD.UK (-41% change) and Ocado (-27%). Others: CBG (-26% change) also big negative. Many large positives: BOY (+24%), etc.\n\nWe want to find those for potential mean reversion: oversold that have negative change and are below moving averages. Or perhaps we could also consider those that have risen too far above moving averages, expecting reversal down.\n\nGiven typical mean reversion: often you go long oversold and short overbought. But we can just consider both.\n\nBut the selection constraints: \"short-term mean reversion in liquid equities\" likely implies going long (buy) on oversold stocks (price below moving average) because likely to revert upward. Similarly perhaps short on those above.\n\nGiven that we are in a long-only scenario? Not clear. The strategy likely intends to go long. We'll choose oversold ones.\n\nThus we want those with negative distance to ma50 (price below 50-day MA) and negative or moderate price change (they fell recently). But also we could include those above if far above and might revert down (but that would be a short). Since the strategy doesn't mention direction, we can just choose candidates that will likely revert both ways.\n\nSimplify: Choose 5 with largest absolute distances (either positive or negative) but moderate volatility. Exclude extremely high volatility > 200% (like HAS_CFD: 238%, OCADO: 249%, etc). Also exclude huge volatility of 9562% (HAIN). Also exclude extremely large price moves (like HAS had -41% change one day, but that might be too extreme but still maybe can be considered oversold due to big drop - but volatility >238% is high). However some other high changes may be okay.\n\nLet's define criteria:\n\n- distance_from_ma50: absolute >= 15% (indicates overshoot/undershoot)\n- distance_from_ma200: less important\n- volatility_20d: <=150% (moderately high risk)\n- change_1d%: ignore (maybe choose from either negative or positive.)\n- price: avoid low price < 0.5 (makes sense)\n- avoid with extremely high change >+20% maybe? Not necessary.\n\nBut as constraints, we also must avoid sector concentration: unknown sectors; but we can accept any.\n\nNow let's compute for each EU symbol:\n\nList of symbols with region EU:\n\nWe'll enumerate each entry and compute relevant metrics.\n\nWe'll parse each entry.\n\nLet me list all symbol records with region:\n\n- BOY_CFD.UK\n- ASHM_CFD.UK\n- GRI_CFD.UK\n- N91_CFD.UK\n- CBG_CFD.UK\n- DLN_CFD.UK\n- GFTU_CFD.UK\n- TPK_CFD.UK\n- PNN_CFD.UK\n- MGNS_CFD.UK\n- INVP_CFD.UK\n- BRNK_CFD.DE\n- BNZL_CFD.UK\n- HAIN_CFD.US (non-EU)\n- OPTU_CFD.US (non-EU)\n- MCG_CFD.UK\n- HFG_CFD.UK\n- HE_CFD.US (non-EU)\n- WKP_CFD.UK\n- OCDO_CFD.UK\n- ADJ_CFD.DE\n- PSON_CFD.UK\n- LMP_CFD.UK\n- HAS_CFD.UK\n- SBT_CFD.FR\n- ATG_CFD.UK\n- MOS_CFD.US (non-EU)\n- CPG_CFD.UK\n- GPK_CFD.US (non-EU)\n- HMSO_CFD.UK\n- SPX_CFD.UK\n- BLND_CFD.UK\n- CTEC_CFD.UK\n- ECMPA_CFD.NL\n- PHP_CFD.UK\n- REC_CFD.BE\n- MNDI_CFD.UK\n- GLW_CFD.US (non-EU)\n- TER_CFD.US (non-EU)\n- UKW_CFD.UK\n- NXI_CFD.FR\n- AKAM_CFD.US (non-EU)\n- SFM_CFD.US (non-EU)\n- APH_CFD.US (non-EU)\n- ALB_CFD.US (non-EU)\n- GT_CFD.US (non-EU)\n- TSCO_CFD.US (non-EU)\n- CDW_CFD.US (non-EU)\n- SMIN_CFD.UK\n\nAnd possibly some missing.\n\nWe will only consider EU region. That excludes US ones.\n\nThus filter out only those with .UK .DE .NL .BE .FR.\n\nThus from above list, all .UK and .DE .NL .BE .FR are EU, .US are not.\n\nList again each EU symbol:\n\n- BOY_CFD.UK: price 775.4, change +24.163%, distance_ma200 +28.65% (above), distance_ma50 +18.636%, volatility 103.4648% (moderate high)\n- ASHM_CFD.UK: price 244.8, change +1.408%, d_ma200 +40.455% (above), d_ma50 +24.8899% (above), volatility 134.254% (high)\n- GRI_CFD.UK: price 189.6, change -1.455%, d_ma200 -6.198% (below), d_ma50 -0.232% (slightly below), volatility 71.78% (moderate)\n- N91_CFD.UK: price 241.4, change -2.74%, d_ma200 +22.1406% (above), d_ma50 +6.809% (above), volatility 84.212% (moderate)\n- CBG_CFD.UK: price 372.74, change -26.626%, d_ma200 -13.365% (below), d_ma50 -22.129% (below), volatility 169.055% (high)\n- DLN_CFD.UK: price 1953, change +10.401%, d_ma200 +5.069% (above), d_ma50 +12.150% (above), volatility 115.39% (high)\n- GFTU_CFD.UK: price 955.8, change +1.043%, d_ma200 +3.963% (above), d_ma50 +3.484% (above), volatility 51.045% (moderate low)\n- TPK_CFD.UK: price 601.57, change -1.210%, d_ma200 +0.527% (above), d_ma50 -4.983% (below), volatility 108.19% (moderate)\n- PNN_CFD.UK: price 551, change +7.699%, d_ma200 +13.6985% (above), d_ma50 +7.286% (above), volatility 97.335% (moderate)\n- MGNS_CFD.UK: price 4860, change +8.928%, d_ma200 +19.027% (above), d_ma50 +4.1297% (above), volatility 174.249% (high)\n- INVP_CFD.UK: price 642, change +9.3697%, d_ma200 +20.3319% (above), d_ma50 +11.7349% (above), volatility 115.2865% (high)\n- BRNK_CFD.DE: price 1.45, change +5.0725%, d_ma200 -21.7367% (below), d_ma50 -6.7416% (below), volatility 192.100% (very high)\n- BNZL_CFD.UK: price 2463, change +11.2466%, d_ma200 +6.7538% (above), d_ma50 +13.3855% (above), volatility 95.7245% (moderate)\n- MCG_CFD.UK: price 26.38, change -4.14244%, d_ma200 -3.65775% (below), d_ma50 +12.386% (above), volatility 328.690% (very high)\n- HFG_CFD.UK: price 548, change +10.2616%, d_ma200 -29.343% (below), d_ma50 -8.64099% (below), volatility 169.6338% (high)\n- WKP_CFD.UK: price 423, change +3.6765%, d_ma200 +1.864% (above), d_ma50 +5.808% (above), volatility 70.223% (moderate)\n- OCDO_CFD.UK: price 186, change -27.372%, d_ma200 -27.532% (below), d_ma50 -19.095% (below), volatility 249.455% (very high)\n- ADJ_CFD.DE: price 0.172, change 0%, d_ma200 -12.8827% (below), d_ma50 -4.60344% (below), volatility 273.640% (very high)\n- PSON_CFD.UK: price 1130.5, change +10.8877%, d_ma200 +8.6569% (above), d_ma50 +14.0706% (above), volatility 113.3546% (moderate high)\n- LMP_CFD.UK: price 192.3, change -1.5865%, d_ma200 +0.9847% (above), d_ma50 -0.5701% (slightly below), volatility 115.3366% (moderate high)\n- HAS_CFD.UK: price 33.32, change -41.1307%, d_ma200 -48.07% (below), d_ma50 -36.9355% (below), volatility 238.6498% (very high)\n- SBT_CFD.FR: price 9.06, change -3.617%, d_ma200 -2.4709% (below), d_ma50 -2.093% (slightly below), volatility 44.8267% (low)\n- ATG_CFD.UK: price 326.5, change +12.0069%, d_ma200 -24.9873% (below), d_ma50 +5.8443% (above), volatility 172.4501% (high)\n- CPG_CFD.UK: price 29.86, change +10.3882%, d_ma200 -98.6279% (way below), d_ma50 -97.6459% (way below), volatility 603.5169% (extremely high)\n- HMSO_CFD.UK: price 355.4, change +14.1297%, d_ma200 +22.516% (above), d_ma50 +10.0069% (above), volatility 87.5494% (moderate)\n- SPX_CFD.UK: price 7168, change +7.06497%, d_ma200 +9.23398% (above), d_ma50 +1.20707% (above), volatility 136.3122% (high)\n- BLND_CFD.UK: price 391.7, change +13.0774%, d_ma200 +4.46325% (above), d_ma50 -0.2295% (near), volatility 119.3708% (moderate)\n- CTEC_CFD.UK: price 207.6, change -9.34498%, d_ma200 -16.703% (below), d_ma50 -11.1109% (below), volatility 116.5523% (moderate)\n- ECMPA_CFD.NL: price 27.55, change -1.0772%, d_ma200 +3.6903% (above), d_ma50 +1.123% (above), volatility 61.3682% (moderate low)\n-",
              "type": "reasoning.text",
              "index": 0,
              "format": "unknown"
            }
          ]
        },
        "logprobs": null,
        "finish_reason": "length",
        "native_finish_reason": "length"
      }
    ],
    "created": 1778412200,
    "provider": "Minimax",
    "system_fingerprint": null
  }
}

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": "ABF_CFD.UK", "qty": 0.0, "avg_price": 1868.5, "last_price": 1849.5, "unrealized_pnl": 0.0, "opened_at": "2026-03-06T18:02:13.212382+00:00"}, {"symbol": "AEO_CFD.US", "qty": 0.0, "avg_price": 22.265, "last_price": 18.64, "unrealized_pnl": 0.0, "opened_at": "2026-03-03T21:11:27.888829+00:00"}, {"symbol": "AGFB_CFD.BE", "qty": 0.0, "avg_price": 0.465, "last_price": 0.452, "unrealized_pnl": 0.0, "opened_at": "2026-03-03T21:11:27.888829+00:00"}, {"symbol": "AML_CFD.UK", "qty": 0.0, "avg_price": 36.92, "last_price": 36.04, "unrealized_pnl": 0.0, "opened_at": "2026-03-04T10:26:53.527562+00:00"}, {"symbol": "ATG_CFD.UK", "qty": 0.0, "avg_price": 296.0, "last_price": 326.5, "unrealized_pnl": 0.0, "opened_at": "2026-03-04T18:49:26.891792+00:00"}, {"symbol": "ATO_CFD.FR", "qty": 0.0, "avg_price": 40.34, "last_price": 39.505, "unrealized_pnl": 0.0, "opened_at": "2026-02-25T10:57:06.928276+00:00"}, {"symbol": "AUTO_CFD.UK", "qty": 0.0, "avg_price": 482.79999999999995, "last_price": 482.8, "unrealized_pnl": 0.0, "opened_at": "2026-03-19T18:04:14.272849+00:00"}, {"symbol": "BIG_CFD.FR", "qty": 0.0, "avg_price": 0.453, "last_price": 0.296, "unrealized_pnl": 0.0, "opened_at": "2026-02-22T09:32:59.506254+00:00"}, {"symbol": "BTRW_CFD.UK", "qty": 0.0, "avg_price": 250.8, "last_price": 272.44, "unrealized_pnl": 0.0, "opened_at": "2026-03-23T18:04:09.108429+00:00"}, {"symbol": "BYIT_CFD.UK", "qty": 0.0, "avg_price": 292.6, "last_price": 289.6, "unrealized_pnl": -61.517429938482564, "opened_at": "2026-02-16T09:30:55.514112+00:00"}, {"symbol": "CBG_CFD.UK", "qty": 8.748396127376648, "avg_price": 342.92, "last_price": 342.92, "unrealized_pnl": 0.0, "opened_at": "2026-03-13T18:17:31.188931+00:00"}, {"symbol": "CCL_CFD.UK", "qty": 0.0, "avg_price": 1945.9999999999998, "last_price": 1989.0, "unrealized_pnl": 0.0, "opened_at": "2026-03-06T18:02:13.212382+00:00"}, {"symbol": "CDW_CFD.US", "qty": 28.66698518872432, "avg_price": 104.65, "last_price": 104.65, "unrealized_pnl": 0.0, "opened_at": "2026-05-09T18:07:46.147407+00:00"}, {"symbol": "CRST_CFD.UK", "qty": 0.0, "avg_price": 118.83, "last_price": 118.83, "unrealized_pnl": 0.0, "opened_at": "2026-03-23T10:03:51.599067+00:00"}, {"symbol": "CRWD_CFD.US", "qty": 0.0, "avg_price": 349.265, "last_price": 365.38, "unrealized_pnl": 0.0, "opened_at": "2026-02-24T18:14:37.115518+00:00"}, {"symbol": "DKNG_CFD.US", "qty": 0.0, "avg_price": 25.28, "last_price": 24.91, "unrealized_pnl": 0.0, "opened_at": "2026-03-05T18:20:59.102726+00:00"}, {"symbol": "DSY_CFD.FR", "qty": 0.0, "avg_price": 17.881, "last_price": 17.133, "unrealized_pnl": 0.0, "opened_at": "2026-02-16T09:30:55.514112+00:00"}, {"symbol": "DT_CFD.US", "qty": 0.0, "avg_price": 34.105000000000004, "last_price": 35.864999999999995, "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": 48.88, "unrealized_pnl": 0.0, "opened_at": "2026-02-19T17:33:27.020111+00:00"}, {"symbol": "FUTR_CFD.UK", "qty": 0.0, "avg_price": 284.2, "last_price": 297.6, "unrealized_pnl": 0.0, "opened_at": "2026-02-19T00:47:27.070066+00:00"}, {"symbol": "GO_CFD.US", "qty": 0.0, "avg_price": 6.53, "last_price": 6.34, "unrealized_pnl": -87.28943338437996, "opened_at": "2026-03-05T18:20:59.102726+00:00"}, {"symbol": "HAIN_CFD.US", "qty": 0.0, "avg_price": 0.63, "last_price": 0.6, "unrealized_pnl": 0.0, "opened_at": "2026-03-20T18:25:08.105367+00:00"}, {"symbol": "HIK_CFD.UK", "qty": 0.0, "avg_price": 1409.0, "last_price": 1244.0, "unrealized_pnl": 0.0, "opened_at": "2026-02-26T10:33:24.101894+00:00"}, {"symbol": "HYQ_CFD.DE", "qty": 0.0, "avg_price": 82.3, "last_price": 77.3, "unrealized_pnl": 0.0, "opened_at": "2026-03-19T10:09:24.325041+00:00"}, {"symbol": "ICG_CFD.UK", "qty": 0.0, "avg_price": 1496.0, "last_price": 1496.0, "unrealized_pnl": 0.0, "opened_at": "2026-03-31T17:54:15.044829+00:00"}, {"symbol": "IHG_CFD.UK", "qty": 23.032629558541267, "avg_price": 130.25, "last_price": 149.35, "unrealized_pnl": 439.92322456813804, "opened_at": "2026-04-02T18:04:13.492519+00:00"}, {"symbol": "INF_CFD.UK", "qty": 0.0, "avg_price": 795.2, "last_price": 785.85, "unrealized_pnl": 0.0, "opened_at": "2026-03-02T18:21:09.503139+00:00"}, {"symbol": "IWG_CFD.UK", "qty": 34.84320557491289, "avg_price": 172.2, "last_price": 187.0, "unrealized_pnl": 515.6794425087112, "opened_at": "2026-03-28T18:06:26.607078+00:00"}, {"symbol": "LIO_CFD.UK", "qty": 0.0, "avg_price": 250.0, "last_price": 250.0, "unrealized_pnl": 0.0, "opened_at": "2026-02-26T10:33:24.101894+00:00"}, {"symbol": "LMND_CFD.US", "qty": 0.0, "avg_price": 48.96, "last_price": 52.07, "unrealized_pnl": 0.0, "opened_at": "2026-02-24T11:23:36.694156+00:00"}, {"symbol": "LMP_CFD.UK", "qty": 0.0, "avg_price": 181.3, "last_price": 181.3, "unrealized_pnl": 0.0, "opened_at": "2026-03-23T10:03:51.599067+00:00"}, {"symbol": "LPE_CFD.FR", "qty": 0.0, "avg_price": 88.414, "last_price": 86.679, "unrealized_pnl": 0.0, "opened_at": "2026-03-02T18:21:09.503139+00:00"}, {"symbol": "LXS_CFD.DE", "qty": 0.0, "avg_price": 13.14, "last_price": 13.24, "unrealized_pnl": 0.0, "opened_at": "2026-03-06T10:33:43.857578+00:00"}, {"symbol": "MAN_CFD.US", "qty": 0.0, "avg_price": 25.68, "last_price": 27.94, "unrealized_pnl": 0.0, "opened_at": "2026-02-25T10:57:06.928276+00:00"}, {"symbol": "MCG_CFD.UK", "qty": 0.0, "avg_price": 17.39, "last_price": 19.28, "unrealized_pnl": 0.0, "opened_at": "2026-03-31T10:31:52.715854+00:00"}, {"symbol": "MDB_CFD.US", "qty": 0.0, "avg_price": 252.01, "last_price": 263.69, "unrealized_pnl": 0.0, "opened_at": "2026-03-04T10:26:53.527562+00:00"}, {"symbol": "MGNS_CFD.UK", "qty": 0.7170172084130019, "avg_price": 4184.0, "last_price": 4670.93, "unrealized_pnl": 349.1371892925432, "opened_at": "2026-04-11T10:24:31.165590+00:00"}, {"symbol": "OCDO_CFD.UK", "qty": 0.0, "avg_price": 204.3, "last_price": 204.3, "unrealized_pnl": 0.0, "opened_at": "2026-02-22T09:32:59.506254+00:00"}, {"symbol": "PAGE_CFD.UK", "qty": 90.29345372460496, "avg_price": 132.9, "last_price": 139.0, "unrealized_pnl": 550.7900677200897, "opened_at": "2026-03-06T10:33:43.857578+00:00"}, {"symbol": "PBB_CFD.DE", "qty": 0.0, "avg_price": 2.826, "last_price": 2.7640000000000002, "unrealized_pnl": 0.0, "opened_at": "2026-03-19T10:09:24.325041+00:00"}, {"symbol": "PROX_CFD.BE", "qty": 0.0, "avg_price": 6.98, "last_price": 7.3, "unrealized_pnl": 0.0, "opened_at": "2026-03-01T10:30:13.622938+00:00"}, {"symbol": "RCO_CFD.FR", "qty": 0.0, "avg_price": 44.34, "last_price": 43.58, "unrealized_pnl": 0.0, "opened_at": "2026-02-23T10:32:09.961595+00:00"}, {"symbol": "RIVN_CFD.US", "qty": 0.0, "avg_price": 15.62, "last_price": 15.1, "unrealized_pnl": 0.0, "opened_at": "2026-03-12T18:01:52.554929+00:00"}, {"symbol": "RMV_CFD.UK", "qty": 0.0, "avg_price": 424.3, "last_price": 417.16, "unrealized_pnl": 0.0, "opened_at": "2026-02-19T00:47:27.070066+00:00"}, {"symbol": "RS1_CFD.UK", "qty": 5.323868677905945, "avg_price": 563.5, "last_price": 579.5, "unrealized_pnl": 85.18189884649512, "opened_at": "2026-04-11T10:24:31.165590+00:00"}, {"symbol": "RSW_CFD.UK", "qty": 0.0, "avg_price": 3790.0000000000005, "last_price": 3790.0, "unrealized_pnl": 0.0, "opened_at": "2026-03-12T18:01:52.554929+00:00"}, {"symbol": "SGRO_CFD.UK", "qty": 0.0, "avg_price": 789.8, "last_price": 735.4, "unrealized_pnl": 0.0, "opened_at": "2026-03-08T18:08:31.007911+00:00"}, {"symbol": "SYNT_CFD.UK", "qty": 0.0, "avg_price": 27.2, "last_price": 34.9, "unrealized_pnl": 0.0, "opened_at": "2026-03-21T10:00:02.317290+00:00"}, {"symbol": "TCAP_CFD.UK", "qty": 0.0, "avg_price": 247.99999999999997, "last_price": 255.5, "unrealized_pnl": 0.0, "opened_at": "2026-03-04T18:49:26.891792+00:00"}, {"symbol": "TRN_CFD.UK", "qty": 0.0, "avg_price": 184.5, "last_price": 204.2, "unrealized_pnl": 0.0, "opened_at": "2026-03-08T18:08:31.007911+00:00"}, {"symbol": "TRST_CFD.UK", "qty": 0.0, "avg_price": 151.8, "last_price": 224.82, "unrealized_pnl": 0.0, "opened_at": "2026-02-19T09:33:49.074031+00:00"}, {"symbol": "UKW_CFD.UK", "qty": 0.0, "avg_price": 92.35, "last_price": 94.79, "unrealized_pnl": 0.0, "opened_at": "2026-02-25T17:57:28.313088+00:00"}, {"symbol": "UTG_CFD.UK", "qty": 0.0, "avg_price": 461.2, "last_price": 461.2, "unrealized_pnl": 0.0, "opened_at": "2026-03-10T18:24:41.570614+00:00"}, {"symbol": "VIRI_CFD.FR", "qty": 0.0, "avg_price": 123.79999999999998, "last_price": 114.0, "unrealized_pnl": 0.0, "opened_at": "2026-04-07T10:08:43.931517+00:00"}, {"symbol": "VTY_CFD.UK", "qty": 0.0, "avg_price": 385.0, "last_price": 354.1, "unrealized_pnl": 0.0, "opened_at": "2026-03-10T18:24:41.570614+00:00"}, {"symbol": "WIZZ_CFD.UK", "qty": 0.0, "avg_price": 908.5, "last_price": 878.0, "unrealized_pnl": 0.0, "opened_at": "2026-03-03T10:20:03.276878+00:00"}, {"symbol": "WPP_CFD.UK", "qty": 0.0, "avg_price": 263.4, "last_price": 257.0, "unrealized_pnl": 0.0, "opened_at": "2026-02-27T18:23:55.960577+00:00"}, {"symbol": "WTB_CFD.UK", "qty": 0.0, "avg_price": 2345.0, "last_price": 2308.0, "unrealized_pnl": 0.0, "opened_at": "2026-03-13T18:17:31.188931+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": "Held position at break-even (0% deviation); no clear mean reversion signal to sell",
        "symbol": "CBG_CFD.UK"
      },
      {
        "reason": "Held position at break-even (0% deviation); no clear mean reversion signal to sell",
        "symbol": "CDW_CFD.US"
      },
      {
        "reason": "Unrealized gain +14.6% but last_price 149.35 vs avg 130.25 - held for now as gains are moderate and no clear overextension",
        "symbol": "IHG_CFD.UK"
      },
      {
        "reason": "Unrealized gain +8.6% but last_price 187.0 vs avg 172.2 - held as position shows healthy mean-reversion upside potential",
        "symbol": "IWG_CFD.UK"
      },
      {
        "reason": "Unrealized gain +11.6% but last_price 4670.93 vs avg 4184.0 - held as recent momentum positive",
        "symbol": "MGNS_CFD.UK"
      },
      {
        "reason": "Unrealized gain +4.6% but last_price 139.0 vs avg 132.9 - held as still showing positive drift",
        "symbol": "PAGE_CFD.UK"
      },
      {
        "reason": "Unrealized gain +2.8% - held as minimal gain; no clear mean reversion trigger",
        "symbol": "RS1_CFD.UK"
      }
    ],
    "sell": []
  },
  "portfolio_rationale": "No action taken: (1) Universe symbols lists are empty - no market data provided to identify new mean-reversion BUY candidates. (2) All 7 existing positions show unrealized gains or break-even - no clear mean-reversion SELL triggers as prices haven't extended significantly above average cost. (3) Current 7 positions is within max 10 allowed, but without candidates, cannot justify new BUYs. Strategy holds: only sell when price has mean-reverted back above avg_price (or significant extension), only buy when candidates provided with price data showing last_price significantly below fundamental/fair value."
}

{
  "raw": {
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          "reasoning": "We are given a task: \"stage1_universe_scan\" with region \"EU\" and strategy \"meanrev-v1\" which indicates a Two-Stage LLM Mean-Reversion strategy. We have selection rules: Horizon is \"2-10 trading days\" for short-term mean reversion in liquid equities. Must select exactly 5 candidates. Must avoid earnings within 5 trading days if known (but we do not have any earnings data, all null). Also avoid highly binary/event-driven moves if known (none given). Avoid sector concentration: max 1 per sector unless clearly justified.\n\nWe have a universe of around 50 symbols, each with a number of technical and price data: last_price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct, market_cap_usd (null), avg_daily_volume_usd (null), days_to_next_earnings (null), etc.\n\nWe need to produce selected_stocks: \"symbol\", \"mean_reversion_score\" (integer between 0 and 100), and \"selection_reason\". The selection reason should be short, concrete, indicator-based.\n\nWe need to reject common reasons for the entire selection: maybe mention some common rejections. The output format expects a JSON with these fields.\n\nWe need to apply the strategy: mean-reversion on liquid equities (presumably UK and EU equities in the dataset). The dataset includes many UK symbols with .UK or .DE, also US symbols, but we need to restrict to region EU: That probably means symbols with .UK .DE .NL .BE .FR (EU region). The dataset includes many .UK, some .DE (Germany), .NL (Netherlands), .BE (Belgium), .FR (France), and some .US. So we need to limit to EU symbols.\n\nWe also need to select exactly 5 candidates. But in the selection rules, we have \"max positions 10\" but \"selection rule: Select exactly 5 candidates\".\n\nThus we need to determine appropriate candidates: short-term mean reversion. Means perhaps those that have moved far from a mean (MA), but we want to buy those that are oversold (negative distance from moving averages) - mean reversion expects price to bounce back. However some negative distance also may be below the moving average; but to be safe, need to think in terms of relative to both MA200 and MA50? The given fields are \"distance_from_ma200_pct\", \"distance_from_ma50_pct\". That is percent difference between price and moving average. A positive difference means price is above the MA, negative means price is below.\n\nMean reversion: We might want to find stocks where price has deviated significantly from average in either direction, expecting reversal. The typical mean-reversion approach often picks oversold stocks when they are far below the moving average (negative distance) or perhaps after large falls.\n\nGiven short-term 2-10 days, perhaps look at distance_from_ma50 more important (since the horizon is shorter). Also volatility: want moderate volatility to allow mean reversion within 2-10 days, but not extremely high (like 9562% volatility for HAIN_CFD.US suggests extremely high, likely due to extremely low price, so not viable). Also want avoid extremely high volatility > 200% perhaps. However some high volatility also may have huge daily moves.\n\nWe also may consider change_1d_pct: Large positive or negative moves. For oversold we might pick those with large negative changes recently (they might be due for rebound), but may also avoid extremely large moves because they could be binary event.\n\nWe need to avoid sector concentration: cannot have more than 1 per sector if sectors unknown; we could just assume we cannot have duplicates, which we can satisfy.\n\nWe need to ensure liquidity: We need to consider avg_daily_volume_usd but it's not provided, but perhaps we can assume they all are some volume.\n\nWe could also prefer stocks with moderate distances (not too extreme). Additionally, distance both above and below could be considered.\n\nWe need exactly 5 candidates. Use each field to compute an overall mean reversion score.\n\nProbably we need to assign scores based on absolute distance from 50-day moving average, but normalized within dataset.\n\nOne possible approach: For each candidate, we can compute a mean_reversion_score: based on absolute distance from moving average (either above or below) and the magnitude of 20d volatility. If price is far from the moving average, might revert. The score maybe 100 - scaled volatility and distance. But we need a deterministic methodology.\n\nWe also need to ensure selection is consistent with the selection rules: Avoid earnings within 5 days (unknown, so ignore). Avoid event-driven (unknown). Avoid sector concentration: unknown sector values.\n\nGiven unknown sectors are blank for all symbols, we cannot apply sector constraints, just avoid selecting more than 1 per unknown sector? Since blank, it's unclear. We might just not worry about sector concentration due to lack of data.\n\nWe also need to avoid extremely low priced stocks: those like HAIN_CFD.US at 0.7051, which has extremely huge volatility (9562%?) likely due to cheap price; not suitable.\n\nWe also need to avoid those with large negative changes maybe because they could be binary events; but for mean reversion, they may be oversold.\n\nGiven data, we need to sort by absolute value of distance_from_ma50 (or distance from ma200?) that are high? Actually large absolute percent difference implies price far from moving average, thus maybe more likely revert.\n\nBut we need to decide direction: If price is above moving average it may revert downward; if below, revert upward; both are reversion.\n\nWe can treat a \"mean reversion score\" that is simply scaled absolute distance. Because we want to capture distance in absolute terms.\n\nGiven volatility: low may indicate slower reversion; high may be too risky. So perhaps we can set score = (abs(distance_ma200) + abs(distance_ma50))/2 - volatility factor something.\n\nBut here we can just produce simple scoring: assign something based on absolute distance from ma50 and magnitude of recent decline (perhaps negative change = oversold). Let's pick those that have had large positive or negative deviations.\n\nBut we also want to avoid selecting huge outliers: e.g., Ocado with -27% change (huge negative). However its volatility is 249% which is high. But that might be due to binary event. Avoid them perhaps.\n\nWe might choose candidates where absolute distance from either MA is high but not extremely extreme, volatility moderate (maybe between 50% and 150%), and price > 1? Not exactly needed, but avoid low-priced.\n\nOur universe includes some heavily discounted large changes: HAS_CFD.UK (-41% change) and Ocado (-27%). Others: CBG (-26% change) also big negative. Many large positives: BOY (+24%), etc.\n\nWe want to find those for potential mean reversion: oversold that have negative change and are below moving averages. Or perhaps we could also consider those that have risen too far above moving averages, expecting reversal down.\n\nGiven typical mean reversion: often you go long oversold and short overbought. But we can just consider both.\n\nBut the selection constraints: \"short-term mean reversion in liquid equities\" likely implies going long (buy) on oversold stocks (price below moving average) because likely to revert upward. Similarly perhaps short on those above.\n\nGiven that we are in a long-only scenario? Not clear. The strategy likely intends to go long. We'll choose oversold ones.\n\nThus we want those with negative distance to ma50 (price below 50-day MA) and negative or moderate price change (they fell recently). But also we could include those above if far above and might revert down (but that would be a short). Since the strategy doesn't mention direction, we can just choose candidates that will likely revert both ways.\n\nSimplify: Choose 5 with largest absolute distances (either positive or negative) but moderate volatility. Exclude extremely high volatility > 200% (like HAS_CFD: 238%, OCADO: 249%, etc). Also exclude huge volatility of 9562% (HAIN). Also exclude extremely large price moves (like HAS had -41% change one day, but that might be too extreme but still maybe can be considered oversold due to big drop - but volatility >238% is high). However some other high changes may be okay.\n\nLet's define criteria:\n\n- distance_from_ma50: absolute >= 15% (indicates overshoot/undershoot)\n- distance_from_ma200: less important\n- volatility_20d: <=150% (moderately high risk)\n- change_1d%: ignore (maybe choose from either negative or positive.)\n- price: avoid low price < 0.5 (makes sense)\n- avoid with extremely high change >+20% maybe? Not necessary.\n\nBut as constraints, we also must avoid sector concentration: unknown sectors; but we can accept any.\n\nNow let's compute for each EU symbol:\n\nList of symbols with region EU:\n\nWe'll enumerate each entry and compute relevant metrics.\n\nWe'll parse each entry.\n\nLet me list all symbol records with region:\n\n- BOY_CFD.UK\n- ASHM_CFD.UK\n- GRI_CFD.UK\n- N91_CFD.UK\n- CBG_CFD.UK\n- DLN_CFD.UK\n- GFTU_CFD.UK\n- TPK_CFD.UK\n- PNN_CFD.UK\n- MGNS_CFD.UK\n- INVP_CFD.UK\n- BRNK_CFD.DE\n- BNZL_CFD.UK\n- HAIN_CFD.US (non-EU)\n- OPTU_CFD.US (non-EU)\n- MCG_CFD.UK\n- HFG_CFD.UK\n- HE_CFD.US (non-EU)\n- WKP_CFD.UK\n- OCDO_CFD.UK\n- ADJ_CFD.DE\n- PSON_CFD.UK\n- LMP_CFD.UK\n- HAS_CFD.UK\n- SBT_CFD.FR\n- ATG_CFD.UK\n- MOS_CFD.US (non-EU)\n- CPG_CFD.UK\n- GPK_CFD.US (non-EU)\n- HMSO_CFD.UK\n- SPX_CFD.UK\n- BLND_CFD.UK\n- CTEC_CFD.UK\n- ECMPA_CFD.NL\n- PHP_CFD.UK\n- REC_CFD.BE\n- MNDI_CFD.UK\n- GLW_CFD.US (non-EU)\n- TER_CFD.US (non-EU)\n- UKW_CFD.UK\n- NXI_CFD.FR\n- AKAM_CFD.US (non-EU)\n- SFM_CFD.US (non-EU)\n- APH_CFD.US (non-EU)\n- ALB_CFD.US (non-EU)\n- GT_CFD.US (non-EU)\n- TSCO_CFD.US (non-EU)\n- CDW_CFD.US (non-EU)\n- SMIN_CFD.UK\n\nAnd possibly some missing.\n\nWe will only consider EU region. That excludes US ones.\n\nThus filter out only those with .UK .DE .NL .BE .FR.\n\nThus from above list, all .UK and .DE .NL .BE .FR are EU, .US are not.\n\nList again each EU symbol:\n\n- BOY_CFD.UK: price 775.4, change +24.163%, distance_ma200 +28.65% (above), distance_ma50 +18.636%, volatility 103.4648% (moderate high)\n- ASHM_CFD.UK: price 244.8, change +1.408%, d_ma200 +40.455% (above), d_ma50 +24.8899% (above), volatility 134.254% (high)\n- GRI_CFD.UK: price 189.6, change -1.455%, d_ma200 -6.198% (below), d_ma50 -0.232% (slightly below), volatility 71.78% (moderate)\n- N91_CFD.UK: price 241.4, change -2.74%, d_ma200 +22.1406% (above), d_ma50 +6.809% (above), volatility 84.212% (moderate)\n- CBG_CFD.UK: price 372.74, change -26.626%, d_ma200 -13.365% (below), d_ma50 -22.129% (below), volatility 169.055% (high)\n- DLN_CFD.UK: price 1953, change +10.401%, d_ma200 +5.069% (above), d_ma50 +12.150% (above), volatility 115.39% (high)\n- GFTU_CFD.UK: price 955.8, change +1.043%, d_ma200 +3.963% (above), d_ma50 +3.484% (above), volatility 51.045% (moderate low)\n- TPK_CFD.UK: price 601.57, change -1.210%, d_ma200 +0.527% (above), d_ma50 -4.983% (below), volatility 108.19% (moderate)\n- PNN_CFD.UK: price 551, change +7.699%, d_ma200 +13.6985% (above), d_ma50 +7.286% (above), volatility 97.335% (moderate)\n- MGNS_CFD.UK: price 4860, change +8.928%, d_ma200 +19.027% (above), d_ma50 +4.1297% (above), volatility 174.249% (high)\n- INVP_CFD.UK: price 642, change +9.3697%, d_ma200 +20.3319% (above), d_ma50 +11.7349% (above), volatility 115.2865% (high)\n- BRNK_CFD.DE: price 1.45, change +5.0725%, d_ma200 -21.7367% (below), d_ma50 -6.7416% (below), volatility 192.100% (very high)\n- BNZL_CFD.UK: price 2463, change +11.2466%, d_ma200 +6.7538% (above), d_ma50 +13.3855% (above), volatility 95.7245% (moderate)\n- MCG_CFD.UK: price 26.38, change -4.14244%, d_ma200 -3.65775% (below), d_ma50 +12.386% (above), volatility 328.690% (very high)\n- HFG_CFD.UK: price 548, change +10.2616%, d_ma200 -29.343% (below), d_ma50 -8.64099% (below), volatility 169.6338% (high)\n- WKP_CFD.UK: price 423, change +3.6765%, d_ma200 +1.864% (above), d_ma50 +5.808% (above), volatility 70.223% (moderate)\n- OCDO_CFD.UK: price 186, change -27.372%, d_ma200 -27.532% (below), d_ma50 -19.095% (below), volatility 249.455% (very high)\n- ADJ_CFD.DE: price 0.172, change 0%, d_ma200 -12.8827% (below), d_ma50 -4.60344% (below), volatility 273.640% (very high)\n- PSON_CFD.UK: price 1130.5, change +10.8877%, d_ma200 +8.6569% (above), d_ma50 +14.0706% (above), volatility 113.3546% (moderate high)\n- LMP_CFD.UK: price 192.3, change -1.5865%, d_ma200 +0.9847% (above), d_ma50 -0.5701% (slightly below), volatility 115.3366% (moderate high)\n- HAS_CFD.UK: price 33.32, change -41.1307%, d_ma200 -48.07% (below), d_ma50 -36.9355% (below), volatility 238.6498% (very high)\n- SBT_CFD.FR: price 9.06, change -3.617%, d_ma200 -2.4709% (below), d_ma50 -2.093% (slightly below), volatility 44.8267% (low)\n- ATG_CFD.UK: price 326.5, change +12.0069%, d_ma200 -24.9873% (below), d_ma50 +5.8443% (above), volatility 172.4501% (high)\n- CPG_CFD.UK: price 29.86, change +10.3882%, d_ma200 -98.6279% (way below), d_ma50 -97.6459% (way below), volatility 603.5169% (extremely high)\n- HMSO_CFD.UK: price 355.4, change +14.1297%, d_ma200 +22.516% (above), d_ma50 +10.0069% (above), volatility 87.5494% (moderate)\n- SPX_CFD.UK: price 7168, change +7.06497%, d_ma200 +9.23398% (above), d_ma50 +1.20707% (above), volatility 136.3122% (high)\n- BLND_CFD.UK: price 391.7, change +13.0774%, d_ma200 +4.46325% (above), d_ma50 -0.2295% (near), volatility 119.3708% (moderate)\n- CTEC_CFD.UK: price 207.6, change -9.34498%, d_ma200 -16.703% (below), d_ma50 -11.1109% (below), volatility 116.5523% (moderate)\n- ECMPA_CFD.NL: price 27.55, change -1.0772%, d_ma200 +3.6903% (above), d_ma50 +1.123% (above), volatility 61.3682% (moderate low)\n-",
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            {
              "text": "We are given a task: \"stage1_universe_scan\" with region \"EU\" and strategy \"meanrev-v1\" which indicates a Two-Stage LLM Mean-Reversion strategy. We have selection rules: Horizon is \"2-10 trading days\" for short-term mean reversion in liquid equities. Must select exactly 5 candidates. Must avoid earnings within 5 trading days if known (but we do not have any earnings data, all null). Also avoid highly binary/event-driven moves if known (none given). Avoid sector concentration: max 1 per sector unless clearly justified.\n\nWe have a universe of around 50 symbols, each with a number of technical and price data: last_price, change_1d_pct, distance_from_ma200_pct, distance_from_ma50_pct, volatility_20d_pct, market_cap_usd (null), avg_daily_volume_usd (null), days_to_next_earnings (null), etc.\n\nWe need to produce selected_stocks: \"symbol\", \"mean_reversion_score\" (integer between 0 and 100), and \"selection_reason\". The selection reason should be short, concrete, indicator-based.\n\nWe need to reject common reasons for the entire selection: maybe mention some common rejections. The output format expects a JSON with these fields.\n\nWe need to apply the strategy: mean-reversion on liquid equities (presumably UK and EU equities in the dataset). The dataset includes many UK symbols with .UK or .DE, also US symbols, but we need to restrict to region EU: That probably means symbols with .UK .DE .NL .BE .FR (EU region). The dataset includes many .UK, some .DE (Germany), .NL (Netherlands), .BE (Belgium), .FR (France), and some .US. So we need to limit to EU symbols.\n\nWe also need to select exactly 5 candidates. But in the selection rules, we have \"max positions 10\" but \"selection rule: Select exactly 5 candidates\".\n\nThus we need to determine appropriate candidates: short-term mean reversion. Means perhaps those that have moved far from a mean (MA), but we want to buy those that are oversold (negative distance from moving averages) - mean reversion expects price to bounce back. However some negative distance also may be below the moving average; but to be safe, need to think in terms of relative to both MA200 and MA50? The given fields are \"distance_from_ma200_pct\", \"distance_from_ma50_pct\". That is percent difference between price and moving average. A positive difference means price is above the MA, negative means price is below.\n\nMean reversion: We might want to find stocks where price has deviated significantly from average in either direction, expecting reversal. The typical mean-reversion approach often picks oversold stocks when they are far below the moving average (negative distance) or perhaps after large falls.\n\nGiven short-term 2-10 days, perhaps look at distance_from_ma50 more important (since the horizon is shorter). Also volatility: want moderate volatility to allow mean reversion within 2-10 days, but not extremely high (like 9562% volatility for HAIN_CFD.US suggests extremely high, likely due to extremely low price, so not viable). Also want avoid extremely high volatility > 200% perhaps. However some high volatility also may have huge daily moves.\n\nWe also may consider change_1d_pct: Large positive or negative moves. For oversold we might pick those with large negative changes recently (they might be due for rebound), but may also avoid extremely large moves because they could be binary event.\n\nWe need to avoid sector concentration: cannot have more than 1 per sector if sectors unknown; we could just assume we cannot have duplicates, which we can satisfy.\n\nWe need to ensure liquidity: We need to consider avg_daily_volume_usd but it's not provided, but perhaps we can assume they all are some volume.\n\nWe could also prefer stocks with moderate distances (not too extreme). Additionally, distance both above and below could be considered.\n\nWe need exactly 5 candidates. Use each field to compute an overall mean reversion score.\n\nProbably we need to assign scores based on absolute distance from 50-day moving average, but normalized within dataset.\n\nOne possible approach: For each candidate, we can compute a mean_reversion_score: based on absolute distance from moving average (either above or below) and the magnitude of 20d volatility. If price is far from the moving average, might revert. The score maybe 100 - scaled volatility and distance. But we need a deterministic methodology.\n\nWe also need to ensure selection is consistent with the selection rules: Avoid earnings within 5 days (unknown, so ignore). Avoid event-driven (unknown). Avoid sector concentration: unknown sector values.\n\nGiven unknown sectors are blank for all symbols, we cannot apply sector constraints, just avoid selecting more than 1 per unknown sector? Since blank, it's unclear. We might just not worry about sector concentration due to lack of data.\n\nWe also need to avoid extremely low priced stocks: those like HAIN_CFD.US at 0.7051, which has extremely huge volatility (9562%?) likely due to cheap price; not suitable.\n\nWe also need to avoid those with large negative changes maybe because they could be binary events; but for mean reversion, they may be oversold.\n\nGiven data, we need to sort by absolute value of distance_from_ma50 (or distance from ma200?) that are high? Actually large absolute percent difference implies price far from moving average, thus maybe more likely revert.\n\nBut we need to decide direction: If price is above moving average it may revert downward; if below, revert upward; both are reversion.\n\nWe can treat a \"mean reversion score\" that is simply scaled absolute distance. Because we want to capture distance in absolute terms.\n\nGiven volatility: low may indicate slower reversion; high may be too risky. So perhaps we can set score = (abs(distance_ma200) + abs(distance_ma50))/2 - volatility factor something.\n\nBut here we can just produce simple scoring: assign something based on absolute distance from ma50 and magnitude of recent decline (perhaps negative change = oversold). Let's pick those that have had large positive or negative deviations.\n\nBut we also want to avoid selecting huge outliers: e.g., Ocado with -27% change (huge negative). However its volatility is 249% which is high. But that might be due to binary event. Avoid them perhaps.\n\nWe might choose candidates where absolute distance from either MA is high but not extremely extreme, volatility moderate (maybe between 50% and 150%), and price > 1? Not exactly needed, but avoid low-priced.\n\nOur universe includes some heavily discounted large changes: HAS_CFD.UK (-41% change) and Ocado (-27%). Others: CBG (-26% change) also big negative. Many large positives: BOY (+24%), etc.\n\nWe want to find those for potential mean reversion: oversold that have negative change and are below moving averages. Or perhaps we could also consider those that have risen too far above moving averages, expecting reversal down.\n\nGiven typical mean reversion: often you go long oversold and short overbought. But we can just consider both.\n\nBut the selection constraints: \"short-term mean reversion in liquid equities\" likely implies going long (buy) on oversold stocks (price below moving average) because likely to revert upward. Similarly perhaps short on those above.\n\nGiven that we are in a long-only scenario? Not clear. The strategy likely intends to go long. We'll choose oversold ones.\n\nThus we want those with negative distance to ma50 (price below 50-day MA) and negative or moderate price change (they fell recently). But also we could include those above if far above and might revert down (but that would be a short). Since the strategy doesn't mention direction, we can just choose candidates that will likely revert both ways.\n\nSimplify: Choose 5 with largest absolute distances (either positive or negative) but moderate volatility. Exclude extremely high volatility > 200% (like HAS_CFD: 238%, OCADO: 249%, etc). Also exclude huge volatility of 9562% (HAIN). Also exclude extremely large price moves (like HAS had -41% change one day, but that might be too extreme but still maybe can be considered oversold due to big drop - but volatility >238% is high). However some other high changes may be okay.\n\nLet's define criteria:\n\n- distance_from_ma50: absolute >= 15% (indicates overshoot/undershoot)\n- distance_from_ma200: less important\n- volatility_20d: <=150% (moderately high risk)\n- change_1d%: ignore (maybe choose from either negative or positive.)\n- price: avoid low price < 0.5 (makes sense)\n- avoid with extremely high change >+20% maybe? Not necessary.\n\nBut as constraints, we also must avoid sector concentration: unknown sectors; but we can accept any.\n\nNow let's compute for each EU symbol:\n\nList of symbols with region EU:\n\nWe'll enumerate each entry and compute relevant metrics.\n\nWe'll parse each entry.\n\nLet me list all symbol records with region:\n\n- BOY_CFD.UK\n- ASHM_CFD.UK\n- GRI_CFD.UK\n- N91_CFD.UK\n- CBG_CFD.UK\n- DLN_CFD.UK\n- GFTU_CFD.UK\n- TPK_CFD.UK\n- PNN_CFD.UK\n- MGNS_CFD.UK\n- INVP_CFD.UK\n- BRNK_CFD.DE\n- BNZL_CFD.UK\n- HAIN_CFD.US (non-EU)\n- OPTU_CFD.US (non-EU)\n- MCG_CFD.UK\n- HFG_CFD.UK\n- HE_CFD.US (non-EU)\n- WKP_CFD.UK\n- OCDO_CFD.UK\n- ADJ_CFD.DE\n- PSON_CFD.UK\n- LMP_CFD.UK\n- HAS_CFD.UK\n- SBT_CFD.FR\n- ATG_CFD.UK\n- MOS_CFD.US (non-EU)\n- CPG_CFD.UK\n- GPK_CFD.US (non-EU)\n- HMSO_CFD.UK\n- SPX_CFD.UK\n- BLND_CFD.UK\n- CTEC_CFD.UK\n- ECMPA_CFD.NL\n- PHP_CFD.UK\n- REC_CFD.BE\n- MNDI_CFD.UK\n- GLW_CFD.US (non-EU)\n- TER_CFD.US (non-EU)\n- UKW_CFD.UK\n- NXI_CFD.FR\n- AKAM_CFD.US (non-EU)\n- SFM_CFD.US (non-EU)\n- APH_CFD.US (non-EU)\n- ALB_CFD.US (non-EU)\n- GT_CFD.US (non-EU)\n- TSCO_CFD.US (non-EU)\n- CDW_CFD.US (non-EU)\n- SMIN_CFD.UK\n\nAnd possibly some missing.\n\nWe will only consider EU region. That excludes US ones.\n\nThus filter out only those with .UK .DE .NL .BE .FR.\n\nThus from above list, all .UK and .DE .NL .BE .FR are EU, .US are not.\n\nList again each EU symbol:\n\n- BOY_CFD.UK: price 775.4, change +24.163%, distance_ma200 +28.65% (above), distance_ma50 +18.636%, volatility 103.4648% (moderate high)\n- ASHM_CFD.UK: price 244.8, change +1.408%, d_ma200 +40.455% (above), d_ma50 +24.8899% (above), volatility 134.254% (high)\n- GRI_CFD.UK: price 189.6, change -1.455%, d_ma200 -6.198% (below), d_ma50 -0.232% (slightly below), volatility 71.78% (moderate)\n- N91_CFD.UK: price 241.4, change -2.74%, d_ma200 +22.1406% (above), d_ma50 +6.809% (above), volatility 84.212% (moderate)\n- CBG_CFD.UK: price 372.74, change -26.626%, d_ma200 -13.365% (below), d_ma50 -22.129% (below), volatility 169.055% (high)\n- DLN_CFD.UK: price 1953, change +10.401%, d_ma200 +5.069% (above), d_ma50 +12.150% (above), volatility 115.39% (high)\n- GFTU_CFD.UK: price 955.8, change +1.043%, d_ma200 +3.963% (above), d_ma50 +3.484% (above), volatility 51.045% (moderate low)\n- TPK_CFD.UK: price 601.57, change -1.210%, d_ma200 +0.527% (above), d_ma50 -4.983% (below), volatility 108.19% (moderate)\n- PNN_CFD.UK: price 551, change +7.699%, d_ma200 +13.6985% (above), d_ma50 +7.286% (above), volatility 97.335% (moderate)\n- MGNS_CFD.UK: price 4860, change +8.928%, d_ma200 +19.027% (above), d_ma50 +4.1297% (above), volatility 174.249% (high)\n- INVP_CFD.UK: price 642, change +9.3697%, d_ma200 +20.3319% (above), d_ma50 +11.7349% (above), volatility 115.2865% (high)\n- BRNK_CFD.DE: price 1.45, change +5.0725%, d_ma200 -21.7367% (below), d_ma50 -6.7416% (below), volatility 192.100% (very high)\n- BNZL_CFD.UK: price 2463, change +11.2466%, d_ma200 +6.7538% (above), d_ma50 +13.3855% (above), volatility 95.7245% (moderate)\n- MCG_CFD.UK: price 26.38, change -4.14244%, d_ma200 -3.65775% (below), d_ma50 +12.386% (above), volatility 328.690% (very high)\n- HFG_CFD.UK: price 548, change +10.2616%, d_ma200 -29.343% (below), d_ma50 -8.64099% (below), volatility 169.6338% (high)\n- WKP_CFD.UK: price 423, change +3.6765%, d_ma200 +1.864% (above), d_ma50 +5.808% (above), volatility 70.223% (moderate)\n- OCDO_CFD.UK: price 186, change -27.372%, d_ma200 -27.532% (below), d_ma50 -19.095% (below), volatility 249.455% (very high)\n- ADJ_CFD.DE: price 0.172, change 0%, d_ma200 -12.8827% (below), d_ma50 -4.60344% (below), volatility 273.640% (very high)\n- PSON_CFD.UK: price 1130.5, change +10.8877%, d_ma200 +8.6569% (above), d_ma50 +14.0706% (above), volatility 113.3546% (moderate high)\n- LMP_CFD.UK: price 192.3, change -1.5865%, d_ma200 +0.9847% (above), d_ma50 -0.5701% (slightly below), volatility 115.3366% (moderate high)\n- HAS_CFD.UK: price 33.32, change -41.1307%, d_ma200 -48.07% (below), d_ma50 -36.9355% (below), volatility 238.6498% (very high)\n- SBT_CFD.FR: price 9.06, change -3.617%, d_ma200 -2.4709% (below), d_ma50 -2.093% (slightly below), volatility 44.8267% (low)\n- ATG_CFD.UK: price 326.5, change +12.0069%, d_ma200 -24.9873% (below), d_ma50 +5.8443% (above), volatility 172.4501% (high)\n- CPG_CFD.UK: price 29.86, change +10.3882%, d_ma200 -98.6279% (way below), d_ma50 -97.6459% (way below), volatility 603.5169% (extremely high)\n- HMSO_CFD.UK: price 355.4, change +14.1297%, d_ma200 +22.516% (above), d_ma50 +10.0069% (above), volatility 87.5494% (moderate)\n- SPX_CFD.UK: price 7168, change +7.06497%, d_ma200 +9.23398% (above), d_ma50 +1.20707% (above), volatility 136.3122% (high)\n- BLND_CFD.UK: price 391.7, change +13.0774%, d_ma200 +4.46325% (above), d_ma50 -0.2295% (near), volatility 119.3708% (moderate)\n- CTEC_CFD.UK: price 207.6, change -9.34498%, d_ma200 -16.703% (below), d_ma50 -11.1109% (below), volatility 116.5523% (moderate)\n- ECMPA_CFD.NL: price 27.55, change -1.0772%, d_ma200 +3.6903% (above), d_ma50 +1.123% (above), volatility 61.3682% (moderate low)\n-",
              "type": "reasoning.text",
              "index": 0,
              "format": "unknown"
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        "finish_reason": "length",
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    ],
    "created": 1778412200,
    "provider": "Minimax",
    "system_fingerprint": null
  }
}