Arena run #58

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

{"task": "stage1_universe_scan", "region": "US", "strategy": {"key": "meanrev-v1", "name": "Two-Stage LLM Mean-Reversion (Arena v1)", "rules": {"max_positions": 10, "max_buys_per_run": 2, "avoid_overtrading": true, "max_sells_per_run": 2, "max_gross_leverage": 3.0, "fixed_position_size": 3000.0, "universe_symbols_eu": [], "universe_symbols_us": []}}, "selection_rules": {"horizon": "2-10 trading days", "target": "short-term mean reversion in liquid equities", "constraints": ["Select exactly 5 candidates", "Avoid earnings within 5 trading days (if known)", "Avoid highly binary/event-driven moves (if known)", "Avoid sector concentration: max 1 per sector unless clearly justified (sector may be unknown)"]}, "universe": [{"symbol": "FLTR_CFD.UK", "name": "FLUTTER ENTERTAINMENT PLC-DI", "sector": "", "last_price": 12305.0, "change_1d_pct": -25.78407720144753, "distance_from_ma200_pct": -37.10737840264257, "distance_from_ma50_pct": -32.350543135250895, "volatility_20d_pct": 128.69185529878447, "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": "IRBT_CFD.US", "name": "IROBOT CORP", "sector": "", "last_price": 0.49, "change_1d_pct": -45.55555555555556, "distance_from_ma200_pct": -85.08802629376588, "distance_from_ma50_pct": -82.85994123408423, "volatility_20d_pct": 786.5727671965057, "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": "CPG_CFD.UK", "name": "", "sector": "", "last_price": 2202.0, "change_1d_pct": -3.16622691292876, "distance_from_ma200_pct": -11.975119419721757, "distance_from_ma50_pct": -5.6971915407383005, "volatility_20d_pct": 75.58872399381154, "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": "BPT_CFD.UK", "name": "", "sector": "", "last_price": 274.2, "change_1d_pct": -5.912225920461179, "distance_from_ma200_pct": -11.483684525527371, "distance_from_ma50_pct": -7.545463557334964, "volatility_20d_pct": 77.69917499624198, "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": "BBOX_CFD.UK", "name": "", "sector": "", "last_price": 166.0, "change_1d_pct": 15.598885793871876, "distance_from_ma200_pct": 15.063751209988908, "distance_from_ma50_pct": 9.510960991667927, "volatility_20d_pct": 57.84262866116462, "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": "AUTO_CFD.UK", "name": "AUTOTRADER GROUP PLC", "sector": "", "last_price": 487.5, "change_1d_pct": -37.876721929835746, "distance_from_ma200_pct": -38.2752275317555, "distance_from_ma50_pct": -34.54270662081388, "volatility_20d_pct": 121.55657728615927, "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": "ICG_CFD.UK", "name": "ICG Plc", "sector": "", "last_price": 1714.0, "change_1d_pct": -16.876818622696412, "distance_from_ma200_pct": -15.871458066903788, "distance_from_ma50_pct": -14.252272815048862, "volatility_20d_pct": 67.97233404982396, "market_cap_usd": null, "avg_daily_volume_usd": null, 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"earnings_confirmed": null, "news_sentiment_score": null, "news_headlines_last_3": [], "data_quality_note": "snapshot_ok_prices_technicals_only"}, {"symbol": "HIAB_CFD.FI", "name": "", "sector": "", "last_price": 50.85, "change_1d_pct": 0.5934718100890191, "distance_from_ma200_pct": 0.9514049517210132, "distance_from_ma50_pct": 2.1373931725220174, "volatility_20d_pct": 95.89223468715024, "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": "GN_CFD.DK", "name": "", "sector": "", "last_price": 97.18, "change_1d_pct": -9.092609915809168, "distance_from_ma200_pct": -7.089781979356669, "distance_from_ma50_pct": -9.820792255612298, "volatility_20d_pct": 97.13892488265931, "market_cap_usd": null, "avg_daily_volume_usd": null, "days_to_next_earnings": null, "earnings_confirmed": null, 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          "content": "{\n  \"selected_stocks\": [\n    {\n      \"symbol\": \"JBLU_CFD.US\",\n      \"mean_reversion_score\": 88,\n      \"selection_reason\": \"Overextended +10.8% 1D move, +29% vs MA200, high volatility indicates potential mean reversion short.\"\n    },\n    {\n      \"symbol\": \"DHER_CFD.DE\",\n      \"mean_reversion_score\": 85,\n      \"selection_reason\": \"Oversold -9.7% 1D move, high volatility, trading near MA50/",
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          "reasoning": "**Refining Stock Selection**\n\nI'm now focusing on implementing the constraints for the \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy. Selecting the initial candidates is underway, considering earnings dates to avoid any within the next five days. I am making sure to select only 5 stocks. The goal is to get this stage completed, so I can start the backtesting.\n\n\n**Prioritizing Candidate Selection**\n\nI'm now determining criteria for candidate selection, including avoiding binary events and sector concentration (max 1 per sector). I am focused on short-term mean reversion, and using data like price, 1d change, and volatility. I'm handling unknown earnings dates by checking for price movements suggesting earnings reactions, and large moves often point to news. The strategy suggests selecting 5 candidates, but the output schema seems ambiguous on direction.\n\n\n**Evaluating Candidate Potential**\n\nI've analyzed the candidates' mean reversion potential, focusing on extreme deviations and recent sharp moves. FLTR_CFD.UK, IRBT_CFD.US, and AUTO_CFD.UK show massive drops and high volatility, making them potentially binary and risky. GNS_CFD.UK shows a large increase. I will exclude these four, as their price moves are too substantial for my strategy.\n\n\n**Identifying Mean Reversion Candidates**\n\nI've filtered potential candidates for mean reversion, focusing on significant but not extreme deviations from the moving average and recent sharp moves, excluding penny stocks. I identified several potential long positions: GN_CFD.DK, DHER_CFD.DE, DOCS_CFD.UK, BPT_CFD.UK, ICG_CFD.UK, and CPG_CFD.UK. I am considering the daily change and volatility. The next step is a deeper dive.\n\n\n**Finalizing Position Selections**\n\nI've narrowed down the potential short candidates, considering recent 1-day changes and deviations from the 200-day moving average. I am selecting from the remaining pool of stocks, trying to diversify geographically and by sector. I am making the final selection of 5 positions, avoiding the previously excluded stocks due to their high volatility.\n\n\n**Confirming Portfolio Composition**\n\nI've finalized my selections, aiming for mean reversion potential and considering volatility and deviation from the 200-day moving average. I have five candidates: JBLU_CFD.US (short), DHER_CFD.DE, DOCS_CFD.UK, GSK_CFD.UK (short), and COLM_CFD.US. I have also verified their sectors, and the group looks well-diversified.\n\n\n**Finalizing Portfolio Allocation**\n\nI've made a final tweak to diversify further, swapping COLM for CPB to reduce concentration in Consumer Cyclical. Then, I considered ICG, which experienced a significant drop, but decided to stick to the less extreme price changes to avoid potential binary events and keep the selections safer. My selected positions now include JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short), with consideration to their one-day changes. I am also making sure to stick to the sector limits.\n\n\n**Validating Portfolio Parameters**\n\nI've validated the portfolio against the defined constraints. I am checking for the correct number of positions, absence of upcoming earnings events (assumed based on price moves), and sector diversification. Each position has been scored based on mean reversion potential, deviation, and volatility, with a scale of 0-100. I have confirmed that the final selections meet the criteria, while also rejecting the stocks due to their high volatility. The final JSON format will be checked. I also noted the empty \"universe\" symbols.\n\n\n**Confirming Portfolio Composition**\n\nI'm now sticking with my initial portfolio: JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short). While GSK showed a significant spike (+7.8% 1D), indicating possible news or a positive trend, I'm sticking with the mean reversion strategy. I am also making sure to diversify across the universe. I've re-confirmed no overlapping sectors.\n\n\n**Revisiting Sector Allocation**\n\nI'm revisiting my portfolio's sector allocation, re-evaluating IRM's recent positive move. After comparing it with alternative candidates like APO and FISV, I'm returning to GN, despite initial concerns. The updated list comprises JBLU, DHER, GSK, CPB, and GN, aiming for optimized diversification. I am happy with the selections now, after sector considerations.\n\n\n",
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              "text": "**Refining Stock Selection**\n\nI'm now focusing on implementing the constraints for the \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy. Selecting the initial candidates is underway, considering earnings dates to avoid any within the next five days. I am making sure to select only 5 stocks. The goal is to get this stage completed, so I can start the backtesting.\n\n\n**Prioritizing Candidate Selection**\n\nI'm now determining criteria for candidate selection, including avoiding binary events and sector concentration (max 1 per sector). I am focused on short-term mean reversion, and using data like price, 1d change, and volatility. I'm handling unknown earnings dates by checking for price movements suggesting earnings reactions, and large moves often point to news. The strategy suggests selecting 5 candidates, but the output schema seems ambiguous on direction.\n\n\n**Evaluating Candidate Potential**\n\nI've analyzed the candidates' mean reversion potential, focusing on extreme deviations and recent sharp moves. FLTR_CFD.UK, IRBT_CFD.US, and AUTO_CFD.UK show massive drops and high volatility, making them potentially binary and risky. GNS_CFD.UK shows a large increase. I will exclude these four, as their price moves are too substantial for my strategy.\n\n\n**Identifying Mean Reversion Candidates**\n\nI've filtered potential candidates for mean reversion, focusing on significant but not extreme deviations from the moving average and recent sharp moves, excluding penny stocks. I identified several potential long positions: GN_CFD.DK, DHER_CFD.DE, DOCS_CFD.UK, BPT_CFD.UK, ICG_CFD.UK, and CPG_CFD.UK. I am considering the daily change and volatility. The next step is a deeper dive.\n\n\n**Finalizing Position Selections**\n\nI've narrowed down the potential short candidates, considering recent 1-day changes and deviations from the 200-day moving average. I am selecting from the remaining pool of stocks, trying to diversify geographically and by sector. I am making the final selection of 5 positions, avoiding the previously excluded stocks due to their high volatility.\n\n\n**Confirming Portfolio Composition**\n\nI've finalized my selections, aiming for mean reversion potential and considering volatility and deviation from the 200-day moving average. I have five candidates: JBLU_CFD.US (short), DHER_CFD.DE, DOCS_CFD.UK, GSK_CFD.UK (short), and COLM_CFD.US. I have also verified their sectors, and the group looks well-diversified.\n\n\n**Finalizing Portfolio Allocation**\n\nI've made a final tweak to diversify further, swapping COLM for CPB to reduce concentration in Consumer Cyclical. Then, I considered ICG, which experienced a significant drop, but decided to stick to the less extreme price changes to avoid potential binary events and keep the selections safer. My selected positions now include JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short), with consideration to their one-day changes. I am also making sure to stick to the sector limits.\n\n\n**Validating Portfolio Parameters**\n\nI've validated the portfolio against the defined constraints. I am checking for the correct number of positions, absence of upcoming earnings events (assumed based on price moves), and sector diversification. Each position has been scored based on mean reversion potential, deviation, and volatility, with a scale of 0-100. I have confirmed that the final selections meet the criteria, while also rejecting the stocks due to their high volatility. The final JSON format will be checked. I also noted the empty \"universe\" symbols.\n\n\n**Confirming Portfolio Composition**\n\nI'm now sticking with my initial portfolio: JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short). While GSK showed a significant spike (+7.8% 1D), indicating possible news or a positive trend, I'm sticking with the mean reversion strategy. I am also making sure to diversify across the universe. I've re-confirmed no overlapping sectors.\n\n\n**Revisiting Sector Allocation**\n\nI'm revisiting my portfolio's sector allocation, re-evaluating IRM's recent positive move. After comparing it with alternative candidates like APO and FISV, I'm returning to GN, despite initial concerns. The updated list comprises JBLU, DHER, GSK, CPB, and GN, aiming for optimized diversification. I am happy with the selections now, after sector considerations.\n\n\n",
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mekXMIlNFaaC1AqNHvDGlJCivhfgvXH97pZFAvcw3QC2QQ/PEyWFSmQwrN8FbHBJPnZnD7S5aWdqThTfmykFEGq1Y4HWjKOH7EcSGLqhSkDlsIYvXi0Vh3LSEUcZNSJkAtBlUAUugopC71/JgFf4WiVvCjeYb8H6ftG7xVyLBrkudaui2RMJP+DSEZePSnp4yjuCqJofKvF6p6ruNvdjq6ch8Wx1XfCw2rAVMFvV3Tlp5T6SdYb5ua28TgPZMF6ssBIq/gUo8cFxRHcB3cyKfblyWRGJtUqV9oWsP/TbeLvhAL7tKXUar8LR593MymUhOqwufXQkZERwx+6vRcQTIXAsVUajqiT5XS6BPecv9YGUCNQSnpSRHH6hkixk2Q5FrM7J0EefdH1mU0P0Sj5KtIxM7L4RDjfaIvVVcwRpfLIhXyudo7lha7P6t6FRXxlHkkP6ZHmM6XtpcXuhOHjcuERnnrZRqqpWOAMbYowAvnDmhMRfgBgKfRYe1GTh0ZA2lXVVdyP2orKN6aXT5SwxG9uta43t8Iw0Txf9KEFe5txCsB1N+9VvFsBfpLYRyvzot2dS0IlRiyCYxCDjeRQ4FGN+m7qFJ9GQPeNBBh8JG+5SoX7/Lu+Hx/8SMIMuzdjcNk41VGTHPLo5RBcpRGqF4JHM+14oMzD0sOP3Ae+3IKmzvCh0jWzoAYHfpNJslpvQkjXsxPjjBA0krIqGvGeUy3UyebQ9TPs3e9CFzjoid7RD28O7vie/DWMY+bLI1hDTZLsbM8TyAXfc4rRSPws6W5sZodNuBqQNOZk5AbBFPRaRc9JSVR39QEUO8na3NeyTGwJD1yCjquYzqidUA7Uq+WXrE3Fc4TLifbTkiNUbX0XelJHmA8SFQ9uhZ8Chm+f0bfQv2r0HheZMmEjwMzcFG5IAZYWkL0aZ0zrz8SDOjDA/2sdGehDWaJWoBC2jdkVc0XeoUOhg246iDZylAjyEmyfOsOfVR84gBewNqtNL5CkMywrmPIC+vdCsCzSrAzWNemMR8v2nFw8dKoW83tyhEeR8Fm89deYuzrz37SRwoEIG8Ac+kmIpt/ZKKDpc8hqjlKgaxMUwxKc9WAiZsddihYPRC5+B7fNUpnzitNmed4kZDf9qwjurYu2wCgSmVnD4uU/VYKJ8CcC0YELcTMMyO9nux3mlsrSDtNrz9yGMU/2vraDwagct9Cz3ZVr/HzEEnJWpE/HvQfj0KO4Ld1MgGVQY8cBsge4GlEHKP84ZPDsG5b/xtSeKI8nUeC0IcE87y2xbVkqYrolSHm05p17N2895adPBBpaft2wk+0Tpm+cwAvzXaA0tlJ6tG2acUy8JqOBUjNqGb/krRoLge2mykQaGpWk6jUlCJAVQCqdGlPr9S7hbaAHMQbz/gpOl7msM7tNhq+/JpqTBlxNdh6fAbULCL7TpfT/RUec/QfKBZqr3eZwoF2mTUmSOlHshdbqi8t9zxuWbK+nFEgmYUKc3Ud+2H2gAyGL+SGXlzxqZgbcyARUxb6Jt7x0wVHUbTUSnndO54hOSJRsborTVg2O7Z53ErFs/amx+UtVO+82ZNw0zf0/p0yL8vIZmVhaYgaEzTk7C4rk+J8hTKAHXlDgyX4pJz3IqLxdHcEBGuMDJvO22ELhiPAINyfOMJ9LNl3GcRh+1dGNlg/NNun+xoBYhkRPcT0RmCw/FnKsf4J1gg0T9aTketJbiPzOmR5HpW64PXCX/0l/MFm6PVvFI/yT9gKxRf3290kK7f5JQfwOMdglIzDywhvwXkUn4OnSZ3ye9DOHMjTkSfz0ejkYCTQ1jBV6kziQpGRd6WP9tU7PyYA2zFOA1RHip6c2/K6Um4mjhFu4v7Cn0dqyHnewfvb0Iogbn7rSLrVygzBAgIccXZn4UyXxQ/WqMaqtYBjOcQNWD39uD2ZvZvYHS8To1SpmfQvfko1DsFD/Z6M7krQg/S887jE5QHBtyM8d9QzITVb4jsuyxwgExFyLfQU5D7Am+8CoDHf31dVMNmA1wv4N3y3AOyR8Z/mFH5NO0uoS3UEiSkv1fFcnByewgcn6iezcvGdo4RXD8jbNF9YmI65QlRG2Ly45np1uq6fipRWTKBAQrIOw70k0h8IDStRsMZOavy4S8gDR2Gzg5ptA1/9QAPCYeqa4FVWyfKMhs/+0S3qzm1J0A9bMUgYMcanMPI9VsGxJIzxJkkHicTNJHMActTpJOBVXIM7yKP7Pz9eGKkhn8g53irtulmfXzLX4FisS9zE+cVPW1ReQV75Fp4Z9/7URtfDDKadGTkKDcXqpLiN8b6SLtfe6yEiTkmqJQLV9mhVB+Qhg4lQhkZFIU6y9x+dNGSmhRnhvi8TnaXyIDwKg1YlrvEUAkEIci9k/kPVWQ0vFgmWWb8XGUeNogYUAuL7MraXoCKZq5bZIg1X7ax4eDhkFgJFRbS/NbxwL4Z7DfgkLtgDM7Pkv6ZxAQLiXJNpRDiXA4Qea8MLcqe1Yg6TuB+Kj4i5v2rDZojCnOYkMZLONm4/PlmHHLrVDih1WHssLySwSdvvHY0twiOmbXr37upiXh/YszZJjLAt3e5sO0WsbMju+oKtQCyQhFyR0EzBPF4SPVv/ZCFF3bBtwdfMW1vpHhfB3JNaAEBtgiz97b8yLYaAJE815VK4lAs+81fDZGnv9E3aJcmMbzMeMh5rBnpEuz7Q08gafV+t+SUT8KwFdm3sPjuWdqrZbi9r5CSjDE7EqanSak3kvcheiTyjXmgkJBmt1//b0YcX3X6Zdky5zjAdRCQMfznPtqzgArhpPNagP3SO2ZirA8QjU92q9ztCNOO7oLSJFTh/QTuTxJQ4ws85Wa6XugR0hVoXQjn1DygUTtD6npvbf6S2wx9VqTqm06STuSEv51bAkL7gp6n+tMuOAscc1y2P6A7wgRwHnLV96QGYiXwFAiJ9i+Bnp+P6bb4VNOK6thHk6f3WO0rrgLwO4Yq2IsBrPFzmy4d69u7mBzUxIB5Wdyi/g2bTK6cktl2FqjxeogLgm8JwwWKP78WLRlbSxpk5fUHTnH2Kb+50AJ+pJJxzSnqZIvjBPH2NM6N0amKyxF+v9X8v2oNmykXWB0ZShtpYgvleBe+vbnpL2T4RqFEUbRYjx7f/dVoA8gjVwftGzA6NluO1l/vRgDXbwTKptfamykoeD3j3jT84hnnI2B+ez9v4UlbVr994UA76C2FyhfAkSmmdAh1wxCT8PSsqpPhBMZzrwzLqFtqZ6TbLejSuFQi0qYd8+qmNrKUKkyw6WuQ4+CWznvBtcSXka6hOXpMSFWISPQRCpO3vgY1bW1DdIDrt74HUhpaWi1+iIOaic5izQVBr9NlTd3rjGr9odECR5aLaNql6rOW+xhwWqHe5UNZWI7/rlncLm9xcXPR+Tvb7edDni5n+cwnMcT4LTGT5vWoPpNRVMUWBc4goAUjDXIpKGLo52o66bOxQMnI/WEdi2jF3Zrt3zUtm5G3hcpdhPUQdpff+qxEyoow7iADVGHmi8gwDz3JFVjml6MgJw7vCHhCWv+IkxCsTvx6ztCk5x/WSjMA2hh42oPmbPEn7iSY9SVzsvEzTDlwjYbHoUDwnaC/zixe8eSz6ms2HjEZCuKkuhMt3zCCp2/ky1PDqt3DdP/atS9Ow0UPSLdcrat4RoIZrUyqVBJYc1MotkZDrmYF05qNeeo6WaWkLgcxRw74bFnPt4Rwi966ZwjccGMCjD+sgpMGYCUHJtA2Scgp/CmbIQDy+X8iwcnTqXQ+MNd7s8/JZ5hL8o7gcKFD6gzSuRxurD0GiLlCkI+Tls/Qrv/X9Mt20fR4iTChDfrnwgBF8qTFHSIl46XwBg/93ePyqgwbqHLGO7MGh6lIaKPWwWvnk15oNclBlLTJTdt6m/xH1c6CCmFWAJLDxHSZpxZNtaTSyz2D/luDQfboSVJO2msBgxRnQs5JCr5F8biSwrpLE61VbIj4Cmr+fP+egS+MTs6MC3TaZyfFGVET0ZofzyV9xCLfCT/NeHOtvlukXmgLm3ZdbnQ7VyRY307mFMEWjLv+zTaFa3+Z+3H0lLUlH3y/v2FXwNAgUFly2z1yhm87GKZQXFR20JPslQqGwLMAFRHF/UpDgWmWJ+mAN79FruJRbGnyHhgYtfRbuAklnozTblspCx8St5Rauqi+BRPxf/ebmpWLak/C2Ko6KBcm27oHVoAFZJk+p3ITQWz09fRq3kqRpkEuozSAG+0KWsUNhVAJ7JBphxs0OzqRevL9LzDIlM0nCowf4f+prQh0jdUunRbTS7ILqYo5iUY4xHKMemCu4xWotmUuzj9GOnBilSFq6d4rA8UZ04+aAOCWCk2NJEgjgEhhQME/oREMABy3w3ICokZUIxP4UkWH2NyPG1o5+upsJ9vYiTHKQ22r99nE1uAy0WR93WYPHdFqxCPBuM1Mxno7jqUwyYh44Wo8nAY0ZFIuEGPigjhg4M39mfIqNnGaRFX0XBw3odKkj6uALq9T7xOc8bAXgrQF/2mIdZlx/b76zLh5OqEVViMjzeZm6GCzrCaThukVduu5pwjVMc1hTckpZKiATXdxvMUuRXoMxzalUdrjWsaghV+xsP3NTT5f5+EUFOaxPDgp9fKQqz0LMLmwe8+/V4aY7/4/utL1X6XGMagAA7Z8RnUZ+ZrKVRewG0iUV2i79DNfG/sXvx8ZUGKkf358Pux6nraZy+bAvAeAtodp9H8HdYU0NMehDJwppnh6kWMlrxl7FTbjPBndIXIGjfIRSK6eYPMe66+5n1k4K/Mj20YXYhaY+dWf0UyQ11eAiMuq2KNp3F8ce9nzMd0WRi7h3nSFtYiT6hhLRr4RG0JS7XbU8tciUl9hlnepfcywPrHS08u6n/0tE9a43O4nE6NGiooE4tGHHwdqjgGEoEMHq77DtMECxk3uj0Dq8cxGjkPtgDAyFgVojskjkSaEm6eMKytrlnegDSa0D/ZjS7nI4QEaFmzwX3z6vqTbUR104AxDMO0bNqHOdrNFmvG6BbiY/odprd+iHFMlglinYZSai9GEHbAQ9b9p2YU2tu8XF+Ewy3IQSw28BkyB/PM13Ekyn7dyKEPj3AitjUhAnmnlTWOFkXnCVv6NhzxvTkTgMaZcOWPNrSon/aK7u0Q/dECy39Kg2nZngQzO7Vm+npVc11mFcaDXdMMzfkBnnC6QcYjYjz2GDgSpOYyMD6yu5y7p3vDdjU2/IxZRKVYMbzTuPi07hj4uZyOqMxFlhKLKNS3Sq+SPSnPCggLqvVMQAkmV/1e0jIfbveafP4AOMDhRJ4Sidz9YZUUbBxj/p6HqAcIoZTlrgoGqjOnzMXvtziNx13N53y9D2O2KOwI/CiHXZQWwu4xpvwHJP4P7K5t9Iq6xz2E3O98/KE+oxerMzwlhmIbHEB/W/301oc1TQhmaaYuZx0wrEXEWcpu3n/nO+6BaptLezUZC+bNvCjzBqnNVGAzfxLWGOeu8QZVxjdWn5H1MG0a97zcbFbMVTeM0Ss1FqzLyy2WX1xgcd6n7iz52KwHDs6Wt4SnP948yK9qO1IoU0OsUaJbGj/Og+fl9spdMSsPpUVh5HojTnZO607OLV23Md8aQssKNfxc5RZgv+SaE+CLyhOM4mJDWww9Fn77fFDYNG3mmDSMFz9AX8rtEME7CAdDWTjQXppKQsbjzOfoQol2b10+zIg4rUwE84NzdrldzZeytD8es8DLKivFj1CrA+2dvXDay2wEa/69ZA+McbDDXoaVglbiAK1hemGEDAKvqAJUCrNvhQgcevO50tzhDIBC7ywgB+5pi6kLlny2+jQWPqlBKkskEGhUPL9zz7IruHcwOorO9WgLRk2N/BlbWKSDeamK/Hzb6wIM2//NOuN2zdLPmDF5/EpjmKYS14vz5v/md7nTMacWgoDhqmOV4nf/qLOAtvtjyMojTzq+9C44vxkBkhudDCbWLGbLDQTBlK8bdbTlUkvOxnlHYTNZMxwgt4LJnFZOui2J9zNz4pkCLiLWKIK0cqlHYi861bWGulzyslfz+3h6scc+CAJDZSdYZK37boMD5D0E+vmuaJRvUdHhPh1OB7PW/uH2poP5hzorr3mZu2AFPgfVdJJFeYV808YOYNZ4ALM6f8cA82N0L7E8OaS/DcYQu/Nidbq+1LW/kLLS9Cl3TZE4+wlxqtobVRDN3FocMF2Feu+Ue9A653tOEqTw4STmnUHx7Kt7dUwzO/ZamHN995xx3DwlZe7NKM2MulHzylp545eVEhssX4mlU+AdDm4dv26UDiP+Svqb05cpwC918FW4515gU5gzMlSlTLBQeNU1gS43BgR2Rl6XoVmrNJHQHiEtsH+cu0OOnsPIXM/kpwF+hPlEj3eSqg9ChjQV4q/Inu81nuRcnS8iSSUzlEba7q9bRTtDh5HSj+9AfwRYFuBUlDW0cAI803kP0aDQXndMIPmO9qqh7Sau2BK1NQ4oCJSBuFNyL61uPk/EdqXDq5ow3PCris7tNme3y0QRCdc9tw3W5itEns3O+N/8hBH6V1SZTYfOcr18+BwoV9Ob2yNzJFgBvmlQkeo87yyjAoT/XqihFMCn1IhKvHrf6kfyHsY1QzfSTpEcs/rej3XPQG86CYKYO0PlOl2uhAMvqeJUVUvQw90caBkf09r3hLvzSls4AeRrxD86Rew0jTRVqf2/FxLHfU87+nhXzD3Bp/S0PMtnweNCPKC73Y3JCukAaTr0I+Q2BegPatt55rDLdWXEO6VFgOKhkNYM0mip7h+HnglZsfc8HdF4fiz2bNTbSaxkujOTEtFU5yW+Qa6/bRTqTTd1QrrEeCFFz2XdWzu47GwkO/gsHHJBL3iT7Xh0vHwLdZPuiHiU2MTZ4fzSa4ZaXhbqT1FmJ1PBCXM5/IPHsS2LGqqyse7JFA5H6DIdrfsadyYbDG3QfxOCCeOMbkEzZx/p+Io8dSZXvnQfshLTeDOrCOLEJYk4XKlfrwuELtzkVB9PQCIiXHyPIznTfCtUAAzY+W+Ews5CeCu/56o5dJyyHIVM2zSfmnicHKJqvBD0h7WyEGWzOAymI1w1KQzh/TIf91qBxxNpd2enrVjKqlok5cwbXk2CHi6wJ8q8enU2xOq63108OuNrV4/eARkMz1uut9yg32RYtq0yKk0Z8DUx3ae2yR6SchCDwB6svr6Zp78MiSF7jEh7q8qMxvUXH65dx1okc6TM9Q5t/EE/ycelmpSekBR4pVwB+Nc89rH4uUYj/PEj8gNZlJzPr7V+EMwBgSsV4Nglm5+KzJSVkvoRxQacnyirFy9d1UC2BaDH578UxvjWNV2qFAQfNYgg0RgsOvc7aObBu5I4aydhDHSoasxppAdz+PRcTxw83QQnQJOaLCNK0i2/Cwt14bfCueQzoJ5Ex/IRwjr/Vetgn6+/1nTc1EukofSpKrUUJr/DTzSSDHt1jp8Dnw5Y6MrRei32jrgelNfgnypYSm80Qje5GVQXw40DjHNTFq+K3Ly75HJ7MgEJDy4+EO9yFSy6OWRN2qnn3+2VzFnG4WqrHv2wxiMH+Q+6EVWim1qSt+nTeAPJgFK+GFIOwPbzSi1TjLJLo68cBe88sfeIftQK4xdD96aSXsn2xSSdWhLti3JlPKZ42xISzEbaQS1jiedQ2QUxARVvBZ+q9n0x+b6QaYHlGo2x6DjhtDFSEx763/CkJz8iN93Q2NoJSRquEX0Nl5hyGxwAQcVXgDwlGdoBtXNHjecPGR/xjjZMKJH/SeqtCxwgtuLVfRuJf49jJV+diJYNmk97QMwVqHh1rX4kUFPEpeWo/8L4FOyQx6ubUc7sFiZd2uJ2PzzyM29DBFcO+TnpUngn5k2Uio5X37CHHdGOzrteUWw+Z5bWVmdyiojcTjq3j+UbCP7JwBzi9Bu1y5cwEl8NgItS1YfRIGO/4zRelmJYkG1js63yP5A74o9hJq+oSJdQmWJ7TfiB7BIaMRPKQk3oFmcHTnZG37CtFs/C84uKW/uyj8K3iMPv3kk8iCsbpqPuv5YlfTSarFTmK05d9sNam6KO91a5uyqW3o6L6HklW2kEh6fJhU2KcSqkHuXx+y7UTV6nyoW1Q19Kzz/At122/f0D57S/oW6Rqo7hxjgI+ecdJxJtu74VQEkzr+CGhhNYNa5BSCKei9ZsDkz/dxk5aTFwA4NL7E4OdNKri++05gwHOh67AnrYFk2MCuvB+HaDz3e8An7XE9Mua8P4/N1SflAI2+L6FTk+fivwneg9FGFyQpbJPVB4DlIPTjK0PQc1Qf9JFXorssHM+T96Iq5sEzGgzHLemvBuG0rKGeaJJ9pIqKVWSLRN8zDrNkHNopI0hyEzizHb/CuO7LnymmZxuCo3eKwUDE5j1CBvDCVUfeOuBUO1ct0OOOfoaFJ4FS7W59IyeBVHC1jIWVOCj7hZw8tpQV7bUVX/7j61DN0EOxHHcTwgkUgn56HWLDjo/I3Z1T0tSu/XMqs+8Cp4cAOb0pRDsDDSvPLfztGrnaWXUqPyv9fcZz+yMHk6ENYCAUwZ1Jqn5SKN+f0wHbWgJ6Xqh8sy6Ll+TH0/gGEf77ekc6tZPuHuMKuAt1FlvnNC2UO7y1hrtcs4YWOqDOLklH6+yo1KpR4BV0uSGMY0WtlzSvqWnmMHeNgSSR3t59qnqZ+P2YJ4nKV1VQW3MS1I7lGZAsNXSFiqWAvl4snTX1QwuD2AtJ5csE/oeKS9HKBSL+SV2pYw7fNx28U6JyndJw5O3kyQNl8PdaC+WOfNHbp0wP93p0r1D6nyWZe2vlcu1T7BbpF6QTCa1lYV5VHE8AkURKPACGdoVeMAz2mYkd06M3GNxSsWDEL2T5TIi9y0alMKg73Q6PBJkFe3vriyhTGorFNmAxDVvZu7C9A7aAu30cXKUZjLkdizIMr6dDA8nbBfKQz0GpJP+QxwlcI4TJG+E6hU7H3YhpJdPRYoqCsVFWH4ZrU/q71HJR3YJMOwYKdQsCAYnGft+9BPpT/VkkY9tddD6qkCZlHxN0YYm5I4+0ZtfJhCuqzpqiOZjEANsVBua5FXOU2r3vx3hLW8TIwaVVkx74hQJC2NoCeNq4480q4vWcaF7Fk/bflnfZ9m2vdMgqJqWZFJZg2qhytOWlEdWUuptdoR+GgzGDZ8BUJhoonVMgOUEvrAUT1A5uJIKgDuJtQ9r9rLsAcU2Z1jf8wP2Dywt4iToGpJ/MwP+ToUi4nnpywsd0X9PugYzd6jS/rpBUDhkDLeRnsDlk9/oKwum14g07S+LZrilChYRV8/w1jbIBeRMIbuqL9/xV9D8Rt3wmiWzpbeC/v/lltsArSoksD7whS067O6/tBB7ElfHwb2JGwckYLCEXH7ps9CJQmOnn9SddGbST2/ezmC9g8+mJMZRLjeZkodLfIaGUtqhG0c25SfLvdzqk88seKWVrtiA9kN3bwIWzhbOpbzsAyAejfvuV+htlbOfk9NTdFKByn3QDz69tgiaBh1mS2w+yRe4s4xySBi52ZHKPP5GHSI+e3EQQGPJx64HLnREmj1oxVuN33gjvHxmgk68sQWXckEWIO0ncgPIufHWr/J6S/YfdARqo7hcUl2owB7QzpqDapJZqae2PThqJLB3Uk6HcO85YQugXxqY0NJ5UwHbDp0aONpsrZ4V6Qpuy0cBo9VOO/VDVLGf59pFA4QLKkreYhOtyvBJCmou3G0slG9CqQm/R1zhUMl8mEozB3X2ZmpeJKGwZOwYnG9MVtzqIMfNgZKqaFH5vdbBoBPk95k5S9qX7Z9twXKx6cGeytWUoa4WiMvGXcTmOf98M/VpTsFwB/SmfU5mMjowMb1h7e0Rdw7D7S+bF2gUKStyn22K6mEa8Ut8uwurmdV1YIe8i+LnYy8DgVIbof7RQmqSKIuz7rM0F4DxuG0XQT+6ki2N5zZE9h4CWhCOamTkqOL4K6GFqc5B7f1wb/Yszn4iZ+1X/aycpuhdVywSSDhJNlnH8Vli8zqHhSxvzwmhi1zq0WO81AcaUQv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              "type": "reasoning.encrypted",
              "index": 0,
              "format": "google-gemini-v1"
            }
          ]
        },
        "logprobs": null,
        "finish_reason": "length",
        "native_finish_reason": "MAX_TOKENS"
      }
    ],
    "created": 1770658424,
    "provider": "Google AI Studio"
  }
}

You are an Arena portfolio manager. You must follow the strategy rules strictly and output ONLY valid JSON. You are NOT allowed to invent prices/fundamentals/news/earnings. If a field is null/empty, treat it as unknown. Avoid overtrading: if changes are marginal, prefer HOLD. Do not churn.

{"task": "stage2_portfolio_management", "region": "US", "strategy": {"key": "meanrev-v1", "name": "Two-Stage LLM Mean-Reversion (Arena v1)", "rules": {"max_positions": 10, "max_buys_per_run": 2, "avoid_overtrading": true, "max_sells_per_run": 2, "max_gross_leverage": 3.0, "fixed_position_size": 3000.0, "universe_symbols_eu": [], "universe_symbols_us": []}}, "portfolio_constraints": {"initial_equity": 10000.0, "max_positions": 10, "fixed_position_size": 3000.0, "max_gross_leverage": 3.0, "overtrading_rules": ["Max 2 SELL actions per run unless risk event forces more", "Max 2 BUY actions per run", "If no clear improvement vs current holdings, output HOLD for everything"]}, "current_portfolio": [{"symbol": "ADP_CFD.US", "qty": 13.213530655391121, "avg_price": 227.04, "last_price": 226.2, "unrealized_pnl": -11.099365750528587, "opened_at": "2026-02-09T15:39:39.370495+00:00"}, {"symbol": "BFAM_CFD.US", "qty": 35.33568904593639, "avg_price": 84.9, "last_price": 83.42, "unrealized_pnl": -52.296819787986, "opened_at": "2026-02-09T15:39:39.370495+00:00"}], "candidates": [], "output_schema": {"actions": {"sell": [{"symbol": "string", "reason": "string"}], "hold": [{"symbol": "string", "reason": "string"}], "buy": [{"symbol": "string", "position_size": "number (currency amount)", "reason": "string"}]}, "portfolio_rationale": "string"}}

{
  "actions": {
    "buy": [],
    "hold": [
      {
        "reason": "Position recently opened, slight drawdown does not warrant exit under mean reversion logic without new signals.",
        "symbol": "ADP_CFD.US"
      },
      {
        "reason": "Position recently opened, currently in minor drawdown; holding to allow mean reversion thesis to materialize.",
        "symbol": "BFAM_CFD.US"
      }
    ],
    "sell": []
  },
  "portfolio_rationale": "No new candidates were provided for evaluation. Existing positions in ADP and BFAM were opened recently and show minor fluctuations. In the absence of profit targets being hit, stop-loss triggers, or better alternative opportunities, the strategy dictates holding current positions to avoid overtrading and allow the mean reversion setup to play out."
}

{
  "raw": {
    "id": "gen-1770658424-3BK3fJOkG29fZbAsHEOU",
    "model": "google/gemini-3-pro-preview",
    "usage": {
      "cost": 0.071134,
      "is_byok": false,
      "cost_details": {
        "upstream_inference_cost": 0.071134,
        "upstream_inference_prompt_cost": 0.023362,
        "upstream_inference_completions_cost": 0.047772
      },
      "total_tokens": 15662,
      "prompt_tokens": 11681,
      "completion_tokens": 3981,
      "prompt_tokens_details": {
        "audio_tokens": 0,
        "video_tokens": 0,
        "cached_tokens": 0,
        "cache_write_tokens": 0
      },
      "completion_tokens_details": {
        "image_tokens": 0,
        "reasoning_tokens": 3837
      }
    },
    "object": "chat.completion",
    "choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "{\n  \"selected_stocks\": [\n    {\n      \"symbol\": \"JBLU_CFD.US\",\n      \"mean_reversion_score\": 88,\n      \"selection_reason\": \"Overextended +10.8% 1D move, +29% vs MA200, high volatility indicates potential mean reversion short.\"\n    },\n    {\n      \"symbol\": \"DHER_CFD.DE\",\n      \"mean_reversion_score\": 85,\n      \"selection_reason\": \"Oversold -9.7% 1D move, high volatility, trading near MA50/",
          "refusal": null,
          "reasoning": "**Refining Stock Selection**\n\nI'm now focusing on implementing the constraints for the \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy. Selecting the initial candidates is underway, considering earnings dates to avoid any within the next five days. I am making sure to select only 5 stocks. The goal is to get this stage completed, so I can start the backtesting.\n\n\n**Prioritizing Candidate Selection**\n\nI'm now determining criteria for candidate selection, including avoiding binary events and sector concentration (max 1 per sector). I am focused on short-term mean reversion, and using data like price, 1d change, and volatility. I'm handling unknown earnings dates by checking for price movements suggesting earnings reactions, and large moves often point to news. The strategy suggests selecting 5 candidates, but the output schema seems ambiguous on direction.\n\n\n**Evaluating Candidate Potential**\n\nI've analyzed the candidates' mean reversion potential, focusing on extreme deviations and recent sharp moves. FLTR_CFD.UK, IRBT_CFD.US, and AUTO_CFD.UK show massive drops and high volatility, making them potentially binary and risky. GNS_CFD.UK shows a large increase. I will exclude these four, as their price moves are too substantial for my strategy.\n\n\n**Identifying Mean Reversion Candidates**\n\nI've filtered potential candidates for mean reversion, focusing on significant but not extreme deviations from the moving average and recent sharp moves, excluding penny stocks. I identified several potential long positions: GN_CFD.DK, DHER_CFD.DE, DOCS_CFD.UK, BPT_CFD.UK, ICG_CFD.UK, and CPG_CFD.UK. I am considering the daily change and volatility. The next step is a deeper dive.\n\n\n**Finalizing Position Selections**\n\nI've narrowed down the potential short candidates, considering recent 1-day changes and deviations from the 200-day moving average. I am selecting from the remaining pool of stocks, trying to diversify geographically and by sector. I am making the final selection of 5 positions, avoiding the previously excluded stocks due to their high volatility.\n\n\n**Confirming Portfolio Composition**\n\nI've finalized my selections, aiming for mean reversion potential and considering volatility and deviation from the 200-day moving average. I have five candidates: JBLU_CFD.US (short), DHER_CFD.DE, DOCS_CFD.UK, GSK_CFD.UK (short), and COLM_CFD.US. I have also verified their sectors, and the group looks well-diversified.\n\n\n**Finalizing Portfolio Allocation**\n\nI've made a final tweak to diversify further, swapping COLM for CPB to reduce concentration in Consumer Cyclical. Then, I considered ICG, which experienced a significant drop, but decided to stick to the less extreme price changes to avoid potential binary events and keep the selections safer. My selected positions now include JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short), with consideration to their one-day changes. I am also making sure to stick to the sector limits.\n\n\n**Validating Portfolio Parameters**\n\nI've validated the portfolio against the defined constraints. I am checking for the correct number of positions, absence of upcoming earnings events (assumed based on price moves), and sector diversification. Each position has been scored based on mean reversion potential, deviation, and volatility, with a scale of 0-100. I have confirmed that the final selections meet the criteria, while also rejecting the stocks due to their high volatility. The final JSON format will be checked. I also noted the empty \"universe\" symbols.\n\n\n**Confirming Portfolio Composition**\n\nI'm now sticking with my initial portfolio: JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short). While GSK showed a significant spike (+7.8% 1D), indicating possible news or a positive trend, I'm sticking with the mean reversion strategy. I am also making sure to diversify across the universe. I've re-confirmed no overlapping sectors.\n\n\n**Revisiting Sector Allocation**\n\nI'm revisiting my portfolio's sector allocation, re-evaluating IRM's recent positive move. After comparing it with alternative candidates like APO and FISV, I'm returning to GN, despite initial concerns. The updated list comprises JBLU, DHER, GSK, CPB, and GN, aiming for optimized diversification. I am happy with the selections now, after sector considerations.\n\n\n",
          "annotations": [],
          "reasoning_details": [
            {
              "text": "**Refining Stock Selection**\n\nI'm now focusing on implementing the constraints for the \"Two-Stage LLM Mean-Reversion (Arena v1)\" strategy. Selecting the initial candidates is underway, considering earnings dates to avoid any within the next five days. I am making sure to select only 5 stocks. The goal is to get this stage completed, so I can start the backtesting.\n\n\n**Prioritizing Candidate Selection**\n\nI'm now determining criteria for candidate selection, including avoiding binary events and sector concentration (max 1 per sector). I am focused on short-term mean reversion, and using data like price, 1d change, and volatility. I'm handling unknown earnings dates by checking for price movements suggesting earnings reactions, and large moves often point to news. The strategy suggests selecting 5 candidates, but the output schema seems ambiguous on direction.\n\n\n**Evaluating Candidate Potential**\n\nI've analyzed the candidates' mean reversion potential, focusing on extreme deviations and recent sharp moves. FLTR_CFD.UK, IRBT_CFD.US, and AUTO_CFD.UK show massive drops and high volatility, making them potentially binary and risky. GNS_CFD.UK shows a large increase. I will exclude these four, as their price moves are too substantial for my strategy.\n\n\n**Identifying Mean Reversion Candidates**\n\nI've filtered potential candidates for mean reversion, focusing on significant but not extreme deviations from the moving average and recent sharp moves, excluding penny stocks. I identified several potential long positions: GN_CFD.DK, DHER_CFD.DE, DOCS_CFD.UK, BPT_CFD.UK, ICG_CFD.UK, and CPG_CFD.UK. I am considering the daily change and volatility. The next step is a deeper dive.\n\n\n**Finalizing Position Selections**\n\nI've narrowed down the potential short candidates, considering recent 1-day changes and deviations from the 200-day moving average. I am selecting from the remaining pool of stocks, trying to diversify geographically and by sector. I am making the final selection of 5 positions, avoiding the previously excluded stocks due to their high volatility.\n\n\n**Confirming Portfolio Composition**\n\nI've finalized my selections, aiming for mean reversion potential and considering volatility and deviation from the 200-day moving average. I have five candidates: JBLU_CFD.US (short), DHER_CFD.DE, DOCS_CFD.UK, GSK_CFD.UK (short), and COLM_CFD.US. I have also verified their sectors, and the group looks well-diversified.\n\n\n**Finalizing Portfolio Allocation**\n\nI've made a final tweak to diversify further, swapping COLM for CPB to reduce concentration in Consumer Cyclical. Then, I considered ICG, which experienced a significant drop, but decided to stick to the less extreme price changes to avoid potential binary events and keep the selections safer. My selected positions now include JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short), with consideration to their one-day changes. I am also making sure to stick to the sector limits.\n\n\n**Validating Portfolio Parameters**\n\nI've validated the portfolio against the defined constraints. I am checking for the correct number of positions, absence of upcoming earnings events (assumed based on price moves), and sector diversification. Each position has been scored based on mean reversion potential, deviation, and volatility, with a scale of 0-100. I have confirmed that the final selections meet the criteria, while also rejecting the stocks due to their high volatility. The final JSON format will be checked. I also noted the empty \"universe\" symbols.\n\n\n**Confirming Portfolio Composition**\n\nI'm now sticking with my initial portfolio: JBLU (short), DHER (long), GSK (short), GN (long), and CPB (short). While GSK showed a significant spike (+7.8% 1D), indicating possible news or a positive trend, I'm sticking with the mean reversion strategy. I am also making sure to diversify across the universe. I've re-confirmed no overlapping sectors.\n\n\n**Revisiting Sector Allocation**\n\nI'm revisiting my portfolio's sector allocation, re-evaluating IRM's recent positive move. After comparing it with alternative candidates like APO and FISV, I'm returning to GN, despite initial concerns. The updated list comprises JBLU, DHER, GSK, CPB, and GN, aiming for optimized diversification. I am happy with the selections now, after sector considerations.\n\n\n",
              "type": "reasoning.text",
              "index": 0,
              "format": "google-gemini-v1"
            },
            {
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",
              "type": "reasoning.encrypted",
              "index": 0,
              "format": "google-gemini-v1"
            }
          ]
        },
        "logprobs": null,
        "finish_reason": "length",
        "native_finish_reason": "MAX_TOKENS"
      }
    ],
    "created": 1770658424,
    "provider": "Google AI Studio"
  }
}