id stringlengths 20 20 | template stringclasses 6
values | complexity int64 1 3 | split stringclasses 1
value | market_regime stringclasses 1
value | asset_class stringclasses 1
value | assets listlengths 1 4 | decision_date timestamp[s]date 2015-02-05 00:00:00 2022-12-28 00:00:00 | context_summary stringlengths 52 153 | question stringlengths 245 9.63k | answer stringlengths 2 63 | answer_numeric float64 -3.07 9.2 | explanation stringlengths 100 240 | metadata unknown |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T3_all_20160222_0803 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2016-02-22T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.1042, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=0.0008, std=0.0338, worst_day=-0.1328
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to BTC-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.9601 | 0.9601 | Step 1: Compute |VaR(99%)| from historical returns = 0.1042 (i.e., a 10.42% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1042 = 0.9601, capped at 1.0.
Maximum position size = 0.9601 (96.0% of portfolio). | {
"var_99": -0.104158,
"expected_loss": 0.104158,
"max_drawdown_threshold": 0.1,
"position_size": 0.9601000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20200212_0806 | T3 | 1 | train | sideways | all | [
"MATIC-USD"
] | 2020-02-12T00:00:00 | MATIC-USD: 60-day history, VaR(99%)=-0.1243, max drawdown threshold=10%. | Asset: MATIC-USD
Daily returns (past 60 days): mean=0.0080, std=0.0661, worst_day=-0.1452
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to MATIC-USD, given the drawdown constraint. Report as a decimal between 0.00 a... | 0.8048 | 0.8048 | Step 1: Compute |VaR(99%)| from historical returns = 0.1243 (i.e., a 12.43% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1243 = 0.8048, capped at 1.0.
Maximum position size = 0.8048 (80.5% of portfolio). | {
"var_99": -0.12425599999999999,
"expected_loss": 0.12425599999999999,
"max_drawdown_threshold": 0.1,
"position_size": 0.8048000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20200925_0809 | T3 | 1 | train | sideways | all | [
"MATIC-USD"
] | 2020-09-25T00:00:00 | MATIC-USD: 60-day history, VaR(99%)=-0.1527, max drawdown threshold=10%. | Asset: MATIC-USD
Daily returns (past 60 days): mean=0.0002, std=0.0577, worst_day=-0.1751
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to MATIC-USD, given the drawdown constraint. Report as a decimal between 0.00 a... | 0.6547 | 0.6547 | Step 1: Compute |VaR(99%)| from historical returns = 0.1527 (i.e., a 15.27% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1527 = 0.6547, capped at 1.0.
Maximum position size = 0.6547 (65.5% of portfolio). | {
"var_99": -0.152748,
"expected_loss": 0.152748,
"max_drawdown_threshold": 0.1,
"position_size": 0.6547000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20170724_0811 | T3 | 1 | train | sideways | all | [
"EFA"
] | 2017-07-24T00:00:00 | EFA: 60-day history, VaR(99%)=-0.0108, max drawdown threshold=10%. | Asset: EFA
Daily returns (past 60 days): mean=0.0010, std=0.0052, worst_day=-0.0114
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2017-07-21] ["XLU, EXC, PCG, AEP: Large Inflows Detected at ETF Looking today at week-over-week shares outstanding changes among the universe... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0108 (i.e., a 1.08% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0108 = 9.2264, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.010837999999999999,
"expected_loss": 0.010837999999999999,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20201016_0813 | T3 | 1 | train | sideways | all | [
"ADA-USD"
] | 2020-10-16T00:00:00 | ADA-USD: 60-day history, VaR(99%)=-0.1411, max drawdown threshold=10%. | Asset: ADA-USD
Daily returns (past 60 days): mean=-0.0030, std=0.0526, worst_day=-0.1683
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to ADA-USD, given the drawdown constraint. Report as a decimal between 0.00 and ... | 0.7086 | 0.7086 | Step 1: Compute |VaR(99%)| from historical returns = 0.1411 (i.e., a 14.11% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1411 = 0.7086, capped at 1.0.
Maximum position size = 0.7086 (70.9% of portfolio). | {
"var_99": -0.141124,
"expected_loss": 0.141124,
"max_drawdown_threshold": 0.1,
"position_size": 0.7086,
"has_text": false,
"text_chars": 0
} |
T3_all_20170320_0815 | T3 | 1 | train | sideways | all | [
"VEA"
] | 2017-03-20T00:00:00 | VEA: 60-day history, VaR(99%)=-0.0079, max drawdown threshold=10%. | Asset: VEA
Daily returns (past 60 days): mean=0.0013, std=0.0049, worst_day=-0.0086
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2017-03-17] ["5 Stocks To Watch For March 17, 2017", "Wunderlich Upgrades Adobe Systems to Buy, Raises Price Target to $145.00", "A Peek Into... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0079 (i.e., a 0.79% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0079 = 12.5976, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.007937999999999999,
"expected_loss": 0.007937999999999999,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20200731_0818 | T3 | 1 | train | sideways | all | [
"LINK-USD"
] | 2020-07-31T00:00:00 | LINK-USD: 60-day history, VaR(99%)=-0.0996, max drawdown threshold=10%. | Asset: LINK-USD
Daily returns (past 60 days): mean=0.0108, std=0.0511, worst_day=-0.1016
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to LINK-USD, given the drawdown constraint. Report as a decimal between 0.00 and... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0996 (i.e., a 9.96% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0996 = 1.0039, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.099608,
"expected_loss": 0.099608,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20200310_0820 | T3 | 1 | train | sideways | all | [
"USMV"
] | 2020-03-10T00:00:00 | USMV: 60-day history, VaR(99%)=-0.0253, max drawdown threshold=10%. | Asset: USMV
Daily returns (past 60 days): mean=-0.0010, std=0.0100, worst_day=-0.0253
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-03-09] ["101 Stocks Moving In Monday's Mid-Day Session", "Investor Movement Index Summary: February 2020", "Crude Awakening: Energy Se... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0253 (i.e., a 2.53% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0253 = 3.9512, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.025308999999999998,
"expected_loss": 0.025308999999999998,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20150416_0822 | T3 | 1 | train | sideways | all | [
"XLB"
] | 2015-04-16T00:00:00 | XLB: 60-day history, VaR(99%)=-0.0177, max drawdown threshold=10%. | Asset: XLB
Daily returns (past 60 days): mean=0.0009, std=0.0091, worst_day=-0.0182
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2015-04-15] ["3 Biotech Stocks Under $10 with Amazing Growth Prospects - Analyst Blog The biotech sector is witnessing changing dynamics with... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0177 (i.e., a 1.77% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0177 = 5.6423, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.017723,
"expected_loss": 0.017723,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20181210_0824 | T3 | 1 | train | sideways | all | [
"XLV"
] | 2018-12-10T00:00:00 | XLV: 60-day history, VaR(99%)=-0.0282, max drawdown threshold=10%. | Asset: XLV
Daily returns (past 60 days): mean=-0.0004, std=0.0119, worst_day=-0.0294
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-12-07] ["What the Arrest of a Chinese Executive Means for the Stock Market A massive drop in the Dow Jones Industrial Average was spurr... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0282 (i.e., a 2.82% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0282 = 3.5433, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.028221999999999997,
"expected_loss": 0.028221999999999997,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180918_0826 | T3 | 1 | train | sideways | all | [
"QUAL"
] | 2018-09-18T00:00:00 | QUAL: 60-day history, VaR(99%)=-0.0110, max drawdown threshold=10%. | Asset: QUAL
Daily returns (past 60 days): mean=0.0011, std=0.0049, worst_day=-0.0128
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-09-17] ["New Apple Watch doesn\u2019t have the feature most consumers want Price is the key factor for Apple smartwatch sales, analyst ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0110 (i.e., a 1.10% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0110 = 9.0855, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.011007,
"expected_loss": 0.011007,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20191127_0828 | T3 | 1 | train | sideways | all | [
"QUAL"
] | 2019-11-27T00:00:00 | QUAL: 60-day history, VaR(99%)=-0.0169, max drawdown threshold=10%. | Asset: QUAL
Daily returns (past 60 days): mean=0.0015, std=0.0065, worst_day=-0.0174
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-11-26] 5 Momentum Stocks to Buy on the Rebound In early September, we saw an unprecedented shift in the investment landscape from momen... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0169 (i.e., a 1.69% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0169 = 5.9219, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.016885999999999998,
"expected_loss": 0.016885999999999998,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20210210_0830 | T3 | 1 | train | sideways | all | [
"MATIC-USD"
] | 2021-02-10T00:00:00 | MATIC-USD: 60-day history, VaR(99%)=-0.1770, max drawdown threshold=10%. | Asset: MATIC-USD
Daily returns (past 60 days): mean=0.0349, std=0.1149, worst_day=-0.1846
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to MATIC-USD, given the drawdown constraint. Report as a decimal between 0.00 a... | 0.5650 | 0.565 | Step 1: Compute |VaR(99%)| from historical returns = 0.1770 (i.e., a 17.70% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1770 = 0.5650, capped at 1.0.
Maximum position size = 0.5650 (56.5% of portfolio). | {
"var_99": -0.176982,
"expected_loss": 0.176982,
"max_drawdown_threshold": 0.1,
"position_size": 0.5650000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20181115_0832 | T3 | 1 | train | sideways | all | [
"XLRE"
] | 2018-11-15T00:00:00 | XLRE: 60-day history, VaR(99%)=-0.0277, max drawdown threshold=10%. | Asset: XLRE
Daily returns (past 60 days): mean=-0.0000, std=0.0102, worst_day=-0.0297
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-11-14] ["Apple stock swings lower after Goldman cuts target, another supplier slashes guidance Shares fall to 3 1/2-month low after se... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0277 (i.e., a 2.77% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0277 = 3.6072, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.027722,
"expected_loss": 0.027722,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20170718_0835 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2017-07-18T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.1026, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=0.0032, std=0.0456, worst_day=-0.1050
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to BTC-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.9749 | 0.9749 | Step 1: Compute |VaR(99%)| from historical returns = 0.1026 (i.e., a 10.26% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1026 = 0.9749, capped at 1.0.
Maximum position size = 0.9749 (97.5% of portfolio). | {
"var_99": -0.10257899999999999,
"expected_loss": 0.10257899999999999,
"max_drawdown_threshold": 0.1,
"position_size": 0.9749000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20190329_0837 | T3 | 1 | train | sideways | all | [
"EMB"
] | 2019-03-29T00:00:00 | EMB: 60-day history, VaR(99%)=-0.0057, max drawdown threshold=10%. | Asset: EMB
Daily returns (past 60 days): mean=0.0011, std=0.0034, worst_day=-0.0066
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to EMB, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (e.g... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0057 (i.e., a 0.57% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0057 = 17.3919, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.005750000000000001,
"expected_loss": 0.005750000000000001,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20161005_0839 | T3 | 1 | train | sideways | all | [
"XLE"
] | 2016-10-05T00:00:00 | XLE: 60-day history, VaR(99%)=-0.0316, max drawdown threshold=10%. | Asset: XLE
Daily returns (past 60 days): mean=0.0006, std=0.0134, worst_day=-0.0335
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-10-04] ["Software-As-A-Service Competition Heats Up For Microsoft", "Software-As-A-Service Competition Heats Up For Microsoft", "Tech St... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0316 (i.e., a 3.16% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0316 = 3.1667, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.031578,
"expected_loss": 0.031578,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220729_0842 | T3 | 1 | train | sideways | all | [
"VTI"
] | 2022-07-29T00:00:00 | VTI: 60-day history, VaR(99%)=-0.0335, max drawdown threshold=10%. | Asset: VTI
Daily returns (past 60 days): mean=-0.0001, std=0.0171, worst_day=-0.0335
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[SEC 10-K MSFT 2022-07-28] msft-10k_20220630.htm false FY 0000789019 --06-30 P10Y http://fasb.org/us-gaap/2022#AccountingStandardsUpdate201601Member... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0335 (i.e., a 3.35% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0335 = 2.9889, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.033457,
"expected_loss": 0.033457,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 9046
} |
T3_all_20190715_0846 | T3 | 1 | train | sideways | all | [
"XLU"
] | 2019-07-15T00:00:00 | XLU: 60-day history, VaR(99%)=-0.0188, max drawdown threshold=10%. | Asset: XLU
Daily returns (past 60 days): mean=0.0010, std=0.0080, worst_day=-0.0220
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-07-12] ["Tlwm Buys Marathon Petroleum Corp, UnitedHealth Group Inc, HCP Inc, Sells Invesco S&P ...", "Will Hot Growth Stocks Break Out F... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0188 (i.e., a 1.88% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0188 = 5.3284, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.018768,
"expected_loss": 0.018768,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20150610_0848 | T3 | 1 | train | sideways | all | [
"XLF"
] | 2015-06-10T00:00:00 | XLF: 60-day history, VaR(99%)=-0.0146, max drawdown threshold=10%. | Asset: XLF
Daily returns (past 60 days): mean=0.0003, std=0.0070, worst_day=-0.0161
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2015-06-09] Amgen's Repatha Briefing Document Gives Mixed Review - Analyst Blog Amgen Inc. 's AMGN PCSK9 inhibitor, Repatha (evolocumab), is ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0146 (i.e., a 1.46% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0146 = 6.8358, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.014629,
"expected_loss": 0.014629,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220314_0849 | T3 | 1 | train | sideways | all | [
"XLU"
] | 2022-03-14T00:00:00 | XLU: 60-day history, VaR(99%)=-0.0206, max drawdown threshold=10%. | Asset: XLU
Daily returns (past 60 days): mean=0.0003, std=0.0103, worst_day=-0.0256
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-03-11] ["Notable Friday Option Activity: UNH, ADBE, MCD Looking at options trading activity among components of the S&P 500 index, there... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0206 (i.e., a 2.06% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0206 = 4.8571, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.020589,
"expected_loss": 0.020589,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20190704_0854 | T3 | 1 | train | sideways | all | [
"EWJ"
] | 2019-07-04T00:00:00 | EWJ: 60-day history, VaR(99%)=-0.0202, max drawdown threshold=10%. | Asset: EWJ
Daily returns (past 60 days): mean=0.0003, std=0.0078, worst_day=-0.0228
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-07-03] ["Why Microsoft Stock Owners Shouldn\u2019t Worry About Linux Normally, a competitor\u2019s increased presence, especially in one... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0202 (i.e., a 2.02% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0202 = 4.9469, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.020215,
"expected_loss": 0.020215,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180627_0856 | T3 | 1 | train | sideways | all | [
"ETH-USD"
] | 2018-06-27T00:00:00 | ETH-USD: 60-day history, VaR(99%)=-0.1176, max drawdown threshold=10%. | Asset: ETH-USD
Daily returns (past 60 days): mean=-0.0055, std=0.0483, worst_day=-0.1190
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to ETH-USD, given the drawdown constraint. Report as a decimal between 0.00 and ... | 0.8501 | 0.8501 | Step 1: Compute |VaR(99%)| from historical returns = 0.1176 (i.e., a 11.76% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1176 = 0.8501, capped at 1.0.
Maximum position size = 0.8501 (85.0% of portfolio). | {
"var_99": -0.117632,
"expected_loss": 0.117632,
"max_drawdown_threshold": 0.1,
"position_size": 0.8501000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20180629_0858 | T3 | 1 | train | sideways | all | [
"XLB"
] | 2018-06-29T00:00:00 | XLB: 60-day history, VaR(99%)=-0.0258, max drawdown threshold=10%. | Asset: XLB
Daily returns (past 60 days): mean=0.0004, std=0.0102, worst_day=-0.0274
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-06-28] ["These top-rated funds reveal some of the world\u2019s hottest tech stocks All have beaten the S&P 500 tech sector for five year... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0258 (i.e., a 2.58% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0258 = 3.8799, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.025774,
"expected_loss": 0.025774,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20160512_0860 | T3 | 1 | train | sideways | all | [
"QQQ"
] | 2016-05-12T00:00:00 | QQQ: 60-day history, VaR(99%)=-0.0158, max drawdown threshold=10%. | Asset: QQQ
Daily returns (past 60 days): mean=0.0010, std=0.0095, worst_day=-0.0166
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-05-11] ["WhatsApp launches desktop version for Mac, Windows Popular mobile messaging app rivals Skype, Apple\u2019s iMessage WhatsApp, t... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0158 (i.e., a 1.58% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0158 = 6.3382, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.015777,
"expected_loss": 0.015777,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20181015_0862 | T3 | 1 | train | sideways | all | [
"XLI"
] | 2018-10-15T00:00:00 | XLI: 60-day history, VaR(99%)=-0.0292, max drawdown threshold=10%. | Asset: XLI
Daily returns (past 60 days): mean=-0.0001, std=0.0090, worst_day=-0.0327
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-10-12] ["Here\u2019s a dividend-investment strategy designed to outperform in down markets The Reality Shares DIVCON Leaders Dividend E... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0292 (i.e., a 2.92% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0292 = 3.4238, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.029207,
"expected_loss": 0.029207,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20150306_0866 | T3 | 1 | train | sideways | all | [
"IGOV"
] | 2015-03-06T00:00:00 | IGOV: 46-day history, VaR(99%)=-0.0134, max drawdown threshold=10%. | Asset: IGOV
Daily returns (past 46 days): mean=-0.0010, std=0.0052, worst_day=-0.0136
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to IGOV, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0134 (i.e., a 1.34% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0134 = 7.4428, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.013436,
"expected_loss": 0.013436,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20201130_0869 | T3 | 1 | train | sideways | all | [
"EEM"
] | 2020-11-30T00:00:00 | EEM: 60-day history, VaR(99%)=-0.0217, max drawdown threshold=10%. | Asset: EEM
Daily returns (past 60 days): mean=0.0017, std=0.0107, worst_day=-0.0254
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-11-27] ["Coronavirus Stock Investing: 4 Industry Transformations That Are Just Getting Started The coronavirus pandemic has changed the ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0217 (i.e., a 2.17% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0217 = 4.6003, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.021738,
"expected_loss": 0.021738,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20210924_0871 | T3 | 1 | train | sideways | all | [
"ADA-USD"
] | 2021-09-24T00:00:00 | ADA-USD: 60-day history, VaR(99%)=-0.1041, max drawdown threshold=10%. | Asset: ADA-USD
Daily returns (past 60 days): mean=0.0125, std=0.0616, worst_day=-0.1163
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to ADA-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.9604 | 0.9604 | Step 1: Compute |VaR(99%)| from historical returns = 0.1041 (i.e., a 10.41% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1041 = 0.9604, capped at 1.0.
Maximum position size = 0.9604 (96.0% of portfolio). | {
"var_99": -0.104127,
"expected_loss": 0.104127,
"max_drawdown_threshold": 0.1,
"position_size": 0.9604,
"has_text": false,
"text_chars": 0
} |
T3_all_20190912_0875 | T3 | 1 | train | sideways | all | [
"EEM"
] | 2019-09-12T00:00:00 | EEM: 60-day history, VaR(99%)=-0.0312, max drawdown threshold=10%. | Asset: EEM
Daily returns (past 60 days): mean=0.0004, std=0.0106, worst_day=-0.0341
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-09-11] ["Asian markets gain ahead of ECB meeting Nikkei, Hang Seng edge up as stocks in mainland China dip Asian markets mostly gained i... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0312 (i.e., a 3.12% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0312 = 3.2004, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.031246,
"expected_loss": 0.031246,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20200203_0878 | T3 | 1 | train | sideways | all | [
"EFA"
] | 2020-02-03T00:00:00 | EFA: 60-day history, VaR(99%)=-0.0186, max drawdown threshold=10%. | Asset: EFA
Daily returns (past 60 days): mean=-0.0000, std=0.0060, worst_day=-0.0209
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-01-31] ["Why it can pay to buy the stocks of companies you love to hate Start your search for diamonds in the rough by looking through ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0186 (i.e., a 1.86% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0186 = 5.3644, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.018640999999999998,
"expected_loss": 0.018640999999999998,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20200601_0881 | T3 | 1 | train | sideways | all | [
"LINK-USD"
] | 2020-06-01T00:00:00 | LINK-USD: 60-day history, VaR(99%)=-0.0607, max drawdown threshold=10%. | Asset: LINK-USD
Daily returns (past 60 days): mean=0.0109, std=0.0436, worst_day=-0.0691
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to LINK-USD, given the drawdown constraint. Report as a decimal between 0.00 and... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0607 (i.e., a 6.07% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0607 = 1.6482, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.060673,
"expected_loss": 0.060673,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20151028_0883 | T3 | 1 | train | sideways | all | [
"ITB"
] | 2015-10-28T00:00:00 | ITB: 60-day history, VaR(99%)=-0.0447, max drawdown threshold=10%. | Asset: ITB
Daily returns (past 60 days): mean=-0.0004, std=0.0171, worst_day=-0.0460
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to ITB, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (e.... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0447 (i.e., a 4.47% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0447 = 2.2356, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.044730000000000006,
"expected_loss": 0.044730000000000006,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20220531_0885 | T3 | 1 | train | sideways | all | [
"XLB"
] | 2022-05-31T00:00:00 | XLB: 60-day history, VaR(99%)=-0.0337, max drawdown threshold=10%. | Asset: XLB
Daily returns (past 60 days): mean=0.0009, std=0.0153, worst_day=-0.0337
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-05-27] ["5 Growth Stocks For Your June 2022 Watchlist Are These The Best Growth Stocks To Buy Right Now? There\u2019s no question that t... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0337 (i.e., a 3.37% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0337 = 2.9659, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.033715999999999996,
"expected_loss": 0.033715999999999996,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20200427_0889 | T3 | 1 | train | sideways | all | [
"CPER"
] | 2020-04-27T00:00:00 | CPER: 60-day history, VaR(99%)=-0.0392, max drawdown threshold=10%. | Asset: CPER
Daily returns (past 60 days): mean=-0.0009, std=0.0165, worst_day=-0.0392
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to CPER, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0392 (i.e., a 3.92% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0392 = 2.5519, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.039186,
"expected_loss": 0.039186,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20220215_0891 | T3 | 1 | train | sideways | all | [
"SOL-USD"
] | 2022-02-15T00:00:00 | SOL-USD: 60-day history, VaR(99%)=-0.1358, max drawdown threshold=10%. | Asset: SOL-USD
Daily returns (past 60 days): mean=-0.0087, std=0.0523, worst_day=-0.1589
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-02-14]
Determine the maximum fraction of total portfolio capital that should be allocated to SOL-USD, given the drawdown constrai... | 0.7363 | 0.7363 | Step 1: Compute |VaR(99%)| from historical returns = 0.1358 (i.e., a 13.58% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1358 = 0.7363, capped at 1.0.
Maximum position size = 0.7363 (73.6% of portfolio). | {
"var_99": -0.135818,
"expected_loss": 0.135818,
"max_drawdown_threshold": 0.1,
"position_size": 0.7363000000000001,
"has_text": true,
"text_chars": 20
} |
T3_all_20161228_0893 | T3 | 1 | train | sideways | all | [
"IWM"
] | 2016-12-28T00:00:00 | IWM: 60-day history, VaR(99%)=-0.0153, max drawdown threshold=10%. | Asset: IWM
Daily returns (past 60 days): mean=0.0017, std=0.0097, worst_day=-0.0184
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-12-27] The Zacks Analyst Blog Highlights: IBM, BP, Disney, Adobe and Cisco For Immediate Release Chicago, IL - December 27, 2016 - Zacks... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0153 (i.e., a 1.53% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0153 = 6.5514, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.015264,
"expected_loss": 0.015264,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20150814_0895 | T3 | 1 | train | sideways | all | [
"XLB"
] | 2015-08-14T00:00:00 | XLB: 60-day history, VaR(99%)=-0.0227, max drawdown threshold=10%. | Asset: XLB
Daily returns (past 60 days): mean=-0.0018, std=0.0094, worst_day=-0.0236
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2015-08-13] ["Earnings Scheduled For August 13, 2015", "10 Stocks You Should Be Watching Today", "Option Alert: Applied Materials Sep $18 Ca... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0227 (i.e., a 2.27% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0227 = 4.3991, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.022732,
"expected_loss": 0.022732,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20200603_0897 | T3 | 1 | train | sideways | all | [
"QQQ"
] | 2020-06-03T00:00:00 | QQQ: 60-day history, VaR(99%)=-0.0399, max drawdown threshold=10%. | Asset: QQQ
Daily returns (past 60 days): mean=0.0017, std=0.0227, worst_day=-0.0399
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-06-02] ["American Pie", "Tech Giants Dare Antitrust Deal Watchdogs", "MoneyGram Shares Jump 50% As Western Union Reportedly Looks For Ac... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0399 (i.e., a 3.99% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0399 = 2.5054, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.039913,
"expected_loss": 0.039913,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180807_0900 | T3 | 1 | train | sideways | all | [
"BNB-USD"
] | 2018-08-07T00:00:00 | BNB-USD: 60-day history, VaR(99%)=-0.0962, max drawdown threshold=10%. | Asset: BNB-USD
Daily returns (past 60 days): mean=-0.0020, std=0.0411, worst_day=-0.1114
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to BNB-USD, given the drawdown constraint. Report as a decimal between 0.00 and ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0962 (i.e., a 9.62% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0962 = 1.0397, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.09618299999999999,
"expected_loss": 0.09618299999999999,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20220606_0902 | T3 | 1 | train | sideways | all | [
"ADA-USD"
] | 2022-06-06T00:00:00 | ADA-USD: 60-day history, VaR(99%)=-0.1755, max drawdown threshold=10%. | Asset: ADA-USD
Daily returns (past 60 days): mean=-0.0078, std=0.0682, worst_day=-0.1761
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-06-05]
Determine the maximum fraction of total portfolio capital that should be allocated to ADA-USD, given the drawdown constrai... | 0.5698 | 0.5698 | Step 1: Compute |VaR(99%)| from historical returns = 0.1755 (i.e., a 17.55% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1755 = 0.5698, capped at 1.0.
Maximum position size = 0.5698 (57.0% of portfolio). | {
"var_99": -0.17550000000000002,
"expected_loss": 0.17550000000000002,
"max_drawdown_threshold": 0.1,
"position_size": 0.5698,
"has_text": true,
"text_chars": 20
} |
T3_all_20191211_0904 | T3 | 1 | train | sideways | all | [
"XLF"
] | 2019-12-11T00:00:00 | XLF: 60-day history, VaR(99%)=-0.0209, max drawdown threshold=10%. | Asset: XLF
Daily returns (past 60 days): mean=0.0012, std=0.0081, worst_day=-0.0213
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-12-10] Beware the Valuation Risks of Red-Hot DocuSign Stock E-signature pioneer DocuSign (NASDAQ:) delivered strong third-quarter earnin... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0209 (i.e., a 2.09% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0209 = 4.7823, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.02091,
"expected_loss": 0.02091,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20161012_0906 | T3 | 1 | train | sideways | all | [
"XLRE"
] | 2016-10-12T00:00:00 | XLRE: 60-day history, VaR(99%)=-0.0268, max drawdown threshold=10%. | Asset: XLRE
Daily returns (past 60 days): mean=-0.0016, std=0.0094, worst_day=-0.0375
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-10-11] ["Why Samsung Could Hit Record Profit Even If It Terminates Galaxy Note 7 Samsung Electronics (005930.Korea/SSNLF) said on Mond... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0268 (i.e., a 2.68% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0268 = 3.7257, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.026840000000000003,
"expected_loss": 0.026840000000000003,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20181106_0908 | T3 | 1 | train | sideways | all | [
"VLUE"
] | 2018-11-06T00:00:00 | VLUE: 60-day history, VaR(99%)=-0.0303, max drawdown threshold=10%. | Asset: VLUE
Daily returns (past 60 days): mean=-0.0004, std=0.0098, worst_day=-0.0356
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-11-05] ["When the going gets tough, Apple hides its numbers Apple will stop reporting unit sales for its largest businesses as iPhone ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0303 (i.e., a 3.03% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0303 = 3.2962, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.030337999999999997,
"expected_loss": 0.030337999999999997,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180330_0910 | T3 | 1 | train | sideways | all | [
"^VIX"
] | 2018-03-30T00:00:00 | ^VIX: 60-day history, VaR(99%)=-0.1825, max drawdown threshold=10%. | Asset: ^VIX
Daily returns (past 60 days): mean=0.0049, std=0.1053, worst_day=-0.1825
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-03-29] ["Can the stock market stand up to the tech wreck? Bull market loses its leader The key question for investors is whether a shar... | 0.5480 | 0.548 | Step 1: Compute |VaR(99%)| from historical returns = 0.1825 (i.e., a 18.25% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1825 = 0.5480, capped at 1.0.
Maximum position size = 0.5480 (54.8% of portfolio). | {
"var_99": -0.182471,
"expected_loss": 0.182471,
"max_drawdown_threshold": 0.1,
"position_size": 0.548,
"has_text": true,
"text_chars": 3020
} |
T3_all_20160426_0913 | T3 | 1 | train | sideways | all | [
"JNK"
] | 2016-04-26T00:00:00 | JNK: 60-day history, VaR(99%)=-0.0102, max drawdown threshold=10%. | Asset: JNK
Daily returns (past 60 days): mean=0.0013, std=0.0061, worst_day=-0.0121
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to JNK, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (e.g... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0102 (i.e., a 1.02% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0102 = 9.8033, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.010201,
"expected_loss": 0.010201,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20180517_0916 | T3 | 1 | train | sideways | all | [
"VNQ"
] | 2018-05-17T00:00:00 | VNQ: 60-day history, VaR(99%)=-0.0212, max drawdown threshold=10%. | Asset: VNQ
Daily returns (past 60 days): mean=0.0006, std=0.0094, worst_day=-0.0233
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to VNQ, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (e.g... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0212 (i.e., a 2.12% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0212 = 4.7159, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.021204999999999998,
"expected_loss": 0.021204999999999998,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20180709_0918 | T3 | 1 | train | sideways | all | [
"EWJ"
] | 2018-07-09T00:00:00 | EWJ: 60-day history, VaR(99%)=-0.0146, max drawdown threshold=10%. | Asset: EWJ
Daily returns (past 60 days): mean=-0.0008, std=0.0054, worst_day=-0.0160
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-07-06] ["Nasdaq 100 Movers: LRCX, BIIB In early trading on Friday, shares of Biogen ( BIIB ) topped the list of the day's best performi... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0146 (i.e., a 1.46% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0146 = 6.8319, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.014636999999999999,
"expected_loss": 0.014636999999999999,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20181205_0920 | T3 | 1 | train | sideways | all | [
"XLI"
] | 2018-12-05T00:00:00 | XLI: 60-day history, VaR(99%)=-0.0336, max drawdown threshold=10%. | Asset: XLI
Daily returns (past 60 days): mean=-0.0015, std=0.0128, worst_day=-0.0336
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-12-04] ["Another Apple Supplier Slashes Its Sales Guidance Cirrus Logic, a maker of audio chips, reduced its financial guidance for its... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0336 (i.e., a 3.36% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0336 = 2.9791, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.033567,
"expected_loss": 0.033567,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20160119_0922 | T3 | 1 | train | sideways | all | [
"VLUE"
] | 2016-01-19T00:00:00 | VLUE: 60-day history, VaR(99%)=-0.0264, max drawdown threshold=10%. | Asset: VLUE
Daily returns (past 60 days): mean=-0.0021, std=0.0116, worst_day=-0.0264
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-01-15] ["TSMC: Modesty Is Virtue, Bear Maybank Raises To Hold Analysts have been questioning Apple (AAPL) foundry Taiwan Semiconductor... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0264 (i.e., a 2.64% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0264 = 3.7915, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.026375,
"expected_loss": 0.026375,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20210112_0924 | T3 | 1 | train | sideways | all | [
"XHB"
] | 2021-01-12T00:00:00 | XHB: 60-day history, VaR(99%)=-0.0360, max drawdown threshold=10%. | Asset: XHB
Daily returns (past 60 days): mean=0.0011, std=0.0154, worst_day=-0.0411
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to XHB, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (e.g... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0360 (i.e., a 3.60% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0360 = 2.7791, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.035982,
"expected_loss": 0.035982,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20190705_0926 | T3 | 1 | train | sideways | all | [
"LINK-USD"
] | 2019-07-05T00:00:00 | LINK-USD: 60-day history, VaR(99%)=-0.1425, max drawdown threshold=10%. | Asset: LINK-USD
Daily returns (past 60 days): mean=0.0260, std=0.0840, worst_day=-0.1553
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to LINK-USD, given the drawdown constraint. Report as a decimal between 0.00 and... | 0.7017 | 0.7017 | Step 1: Compute |VaR(99%)| from historical returns = 0.1425 (i.e., a 14.25% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1425 = 0.7017, capped at 1.0.
Maximum position size = 0.7017 (70.2% of portfolio). | {
"var_99": -0.142511,
"expected_loss": 0.142511,
"max_drawdown_threshold": 0.1,
"position_size": 0.7017,
"has_text": false,
"text_chars": 0
} |
T3_all_20150507_0928 | T3 | 1 | train | sideways | all | [
"EEM"
] | 2015-05-07T00:00:00 | EEM: 60-day history, VaR(99%)=-0.0196, max drawdown threshold=10%. | Asset: EEM
Daily returns (past 60 days): mean=0.0011, std=0.0107, worst_day=-0.0225
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2015-05-06] ["Itron (ITRI) Q1 Earnings Trail on Adverse Forex Impact - Analyst Blog", "Itron (ITRI) Q1 Earnings Trail on Adverse Forex Impact... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0196 (i.e., a 1.96% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0196 = 5.0937, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.019632,
"expected_loss": 0.019632,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180214_0930 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2018-02-14T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.2138, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=0.0095, std=0.1154, worst_day=-0.2138
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to XRP-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.4677 | 0.4677 | Step 1: Compute |VaR(99%)| from historical returns = 0.2138 (i.e., a 21.38% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.2138 = 0.4677, capped at 1.0.
Maximum position size = 0.4677 (46.8% of portfolio). | {
"var_99": -0.21380000000000002,
"expected_loss": 0.21380000000000002,
"max_drawdown_threshold": 0.1,
"position_size": 0.4677,
"has_text": false,
"text_chars": 0
} |
T3_all_20191028_0932 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2019-10-28T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.0879, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=-0.0005, std=0.0308, worst_day=-0.1140
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to BTC-USD, given the drawdown constraint. Report as a decimal between 0.00 and ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0879 (i.e., a 8.79% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0879 = 1.1379, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.087882,
"expected_loss": 0.087882,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20190204_0934 | T3 | 1 | train | sideways | all | [
"SCHP"
] | 2019-02-04T00:00:00 | SCHP: 60-day history, VaR(99%)=-0.0037, max drawdown threshold=10%. | Asset: SCHP
Daily returns (past 60 days): mean=0.0004, std=0.0023, worst_day=-0.0045
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to SCHP, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (e... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0037 (i.e., a 0.37% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0037 = 27.0427, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.003698,
"expected_loss": 0.003698,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20170913_0936 | T3 | 1 | train | sideways | all | [
"SHV"
] | 2017-09-13T00:00:00 | SHV: 60-day history, VaR(99%)=-0.0003, max drawdown threshold=10%. | Asset: SHV
Daily returns (past 60 days): mean=0.0000, std=0.0002, worst_day=-0.0003
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to SHV, given the drawdown constraint. Report as a decimal between 0.00 and 1.00 (e.g... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0003 (i.e., a 0.03% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0003 = 367.1313, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.000272,
"expected_loss": 0.000272,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20210416_0938 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2021-04-16T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.0749, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=0.0050, std=0.0366, worst_day=-0.0993
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to BTC-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0749 (i.e., a 7.49% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0749 = 1.3354, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.074886,
"expected_loss": 0.074886,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20191216_0940 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2019-12-16T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.0581, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=-0.0022, std=0.0287, worst_day=-0.0698
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to BTC-USD, given the drawdown constraint. Report as a decimal between 0.00 and ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0581 (i.e., a 5.81% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0581 = 1.7224, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.058057,
"expected_loss": 0.058057,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20210726_0942 | T3 | 1 | train | sideways | all | [
"DOT-USD"
] | 2021-07-26T00:00:00 | DOT-USD: 60-day history, VaR(99%)=-0.1464, max drawdown threshold=10%. | Asset: DOT-USD
Daily returns (past 60 days): mean=-0.0067, std=0.0659, worst_day=-0.1989
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to DOT-USD, given the drawdown constraint. Report as a decimal between 0.00 and ... | 0.6831 | 0.6831 | Step 1: Compute |VaR(99%)| from historical returns = 0.1464 (i.e., a 14.64% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1464 = 0.6831, capped at 1.0.
Maximum position size = 0.6831 (68.3% of portfolio). | {
"var_99": -0.146393,
"expected_loss": 0.146393,
"max_drawdown_threshold": 0.1,
"position_size": 0.6831,
"has_text": false,
"text_chars": 0
} |
T3_all_20220524_0944 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2022-05-24T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.1567, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=-0.0110, std=0.0480, worst_day=-0.1952
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-05-23]
Determine the maximum fraction of total portfolio capital that should be allocated to XRP-USD, given the drawdown constrai... | 0.6383 | 0.6383 | Step 1: Compute |VaR(99%)| from historical returns = 0.1567 (i.e., a 15.67% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1567 = 0.6383, capped at 1.0.
Maximum position size = 0.6383 (63.8% of portfolio). | {
"var_99": -0.15666000000000002,
"expected_loss": 0.15666000000000002,
"max_drawdown_threshold": 0.1,
"position_size": 0.6383,
"has_text": true,
"text_chars": 20
} |
T3_all_20180202_0946 | T3 | 1 | train | sideways | all | [
"^VIX"
] | 2018-02-02T00:00:00 | ^VIX: 60-day history, VaR(99%)=-0.1151, max drawdown threshold=10%. | Asset: ^VIX
Daily returns (past 60 days): mean=0.0065, std=0.0614, worst_day=-0.1222
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-02-01] ["Apple declines in late trading on iPhone supplier concerns Apple Inc. shares fell in late trading Wednesday after chip supplie... | 0.8687 | 0.8687 | Step 1: Compute |VaR(99%)| from historical returns = 0.1151 (i.e., a 11.51% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1151 = 0.8687, capped at 1.0.
Maximum position size = 0.8687 (86.9% of portfolio). | {
"var_99": -0.115121,
"expected_loss": 0.115121,
"max_drawdown_threshold": 0.1,
"position_size": 0.8687,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180706_0949 | T3 | 1 | train | sideways | all | [
"VLUE"
] | 2018-07-06T00:00:00 | VLUE: 60-day history, VaR(99%)=-0.0130, max drawdown threshold=10%. | Asset: VLUE
Daily returns (past 60 days): mean=0.0001, std=0.0063, worst_day=-0.0143
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-07-05] ["PreMarket Prep Recap For July 5: Trading The Range In The S&P 500; Sean Udall Joins The Show", "PreMarket Prep Recap For July ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0130 (i.e., a 1.30% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0130 = 7.7108, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.012969,
"expected_loss": 0.012969,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20221209_0951 | T3 | 1 | train | sideways | all | [
"USMV"
] | 2022-12-09T00:00:00 | USMV: 60-day history, VaR(99%)=-0.0211, max drawdown threshold=10%. | Asset: USMV
Daily returns (past 60 days): mean=0.0004, std=0.0118, worst_day=-0.0222
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-12-08] ["US STOCKS-S&P 500, Nasdaq snap losing streaks after jobless claims rise By David French Dec 8 (Reuters) - The S&P 500 .SPXende... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0211 (i.e., a 2.11% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0211 = 4.7411, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.021092,
"expected_loss": 0.021092,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20210128_0953 | T3 | 1 | train | sideways | all | [
"ETH-USD"
] | 2021-01-28T00:00:00 | ETH-USD: 60-day history, VaR(99%)=-0.1458, max drawdown threshold=10%. | Asset: ETH-USD
Daily returns (past 60 days): mean=0.0151, std=0.0592, worst_day=-0.1593
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to ETH-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.6860 | 0.686 | Step 1: Compute |VaR(99%)| from historical returns = 0.1458 (i.e., a 14.58% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1458 = 0.6860, capped at 1.0.
Maximum position size = 0.6860 (68.6% of portfolio). | {
"var_99": -0.14577,
"expected_loss": 0.14577,
"max_drawdown_threshold": 0.1,
"position_size": 0.686,
"has_text": false,
"text_chars": 0
} |
T3_all_20210813_0955 | T3 | 1 | train | sideways | all | [
"ETH-USD"
] | 2021-08-13T00:00:00 | ETH-USD: 60-day history, VaR(99%)=-0.1203, max drawdown threshold=10%. | Asset: ETH-USD
Daily returns (past 60 days): mean=0.0044, std=0.0494, worst_day=-0.1593
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to ETH-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.8313 | 0.8313 | Step 1: Compute |VaR(99%)| from historical returns = 0.1203 (i.e., a 12.03% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1203 = 0.8313, capped at 1.0.
Maximum position size = 0.8313 (83.1% of portfolio). | {
"var_99": -0.12029899999999999,
"expected_loss": 0.12029899999999999,
"max_drawdown_threshold": 0.1,
"position_size": 0.8313,
"has_text": false,
"text_chars": 0
} |
T3_all_20220715_0957 | T3 | 1 | train | sideways | all | [
"IVV"
] | 2022-07-15T00:00:00 | IVV: 60-day history, VaR(99%)=-0.0328, max drawdown threshold=10%. | Asset: IVV
Daily returns (past 60 days): mean=-0.0021, std=0.0173, worst_day=-0.0328
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-07-14] ["5 Stocks to Outrun the Housing Market InvestorPlace - Stock Market News, Stock Advice & Trading Tips This article is excerpted... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0328 (i.e., a 3.28% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0328 = 3.0503, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.032784,
"expected_loss": 0.032784,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220912_0963 | T3 | 1 | train | sideways | all | [
"USMV"
] | 2022-09-12T00:00:00 | USMV: 60-day history, VaR(99%)=-0.0225, max drawdown threshold=10%. | Asset: USMV
Daily returns (past 60 days): mean=0.0014, std=0.0101, worst_day=-0.0253
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-09-09] ["ROKU Set to Launch The Rich Eisen Show on The Roku Channel Roku Inc. ROKU awaits the debut of The Rich Eisen Show hosted by Ri... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0225 (i.e., a 2.25% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0225 = 4.4536, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.022452999999999997,
"expected_loss": 0.022452999999999997,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20160114_0967 | T3 | 1 | train | sideways | all | [
"QQQ"
] | 2016-01-14T00:00:00 | QQQ: 60-day history, VaR(99%)=-0.0332, max drawdown threshold=10%. | Asset: QQQ
Daily returns (past 60 days): mean=-0.0009, std=0.0124, worst_day=-0.0351
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-01-13] ["CLSA Survey: China\u2019s iPhone Demand Remains Firm Smartphone hardware stocks are sold off this year. Apple (AAPL) has falle... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0332 (i.e., a 3.32% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0332 = 3.0156, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.03316,
"expected_loss": 0.03316,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20211012_0969 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2021-10-12T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.1519, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=0.0046, std=0.0615, worst_day=-0.1902
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to XRP-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.6585 | 0.6585 | Step 1: Compute |VaR(99%)| from historical returns = 0.1519 (i.e., a 15.19% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1519 = 0.6585, capped at 1.0.
Maximum position size = 0.6585 (65.8% of portfolio). | {
"var_99": -0.151871,
"expected_loss": 0.151871,
"max_drawdown_threshold": 0.1,
"position_size": 0.6585000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20210118_0971 | T3 | 1 | train | sideways | all | [
"XLF"
] | 2021-01-18T00:00:00 | XLF: 60-day history, VaR(99%)=-0.0241, max drawdown threshold=10%. | Asset: XLF
Daily returns (past 60 days): mean=0.0029, std=0.0141, worst_day=-0.0259
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2021-01-15] 3 Stocks That Need to Get Back on Track in 2021 Last year was a lucrative one for most investments, but some market favorites wer... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0241 (i.e., a 2.41% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0241 = 4.1495, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.024099,
"expected_loss": 0.024099,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20210223_0973 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2021-02-23T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.1608, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=0.0103, std=0.0873, worst_day=-0.2138
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to XRP-USD, given the drawdown constraint. Report as a decimal between 0.00 and 1... | 0.6219 | 0.6219 | Step 1: Compute |VaR(99%)| from historical returns = 0.1608 (i.e., a 16.08% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1608 = 0.6219, capped at 1.0.
Maximum position size = 0.6219 (62.2% of portfolio). | {
"var_99": -0.16078499999999998,
"expected_loss": 0.16078499999999998,
"max_drawdown_threshold": 0.1,
"position_size": 0.6219,
"has_text": false,
"text_chars": 0
} |
T3_all_20190226_0976 | T3 | 1 | train | sideways | all | [
"QQQ"
] | 2019-02-26T00:00:00 | QQQ: 60-day history, VaR(99%)=-0.0358, max drawdown threshold=10%. | Asset: QQQ
Daily returns (past 60 days): mean=0.0005, std=0.0158, worst_day=-0.0391
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-02-25] ["Apple's approach to video 'will likely remain uninspiring,' says KeyBanc KeyBanc Capital Markets analyst Andy Hargreaves isn't ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0358 (i.e., a 3.58% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0358 = 2.7899, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.035844,
"expected_loss": 0.035844,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20201026_0978 | T3 | 1 | train | sideways | all | [
"IWM"
] | 2020-10-26T00:00:00 | IWM: 60-day history, VaR(99%)=-0.0325, max drawdown threshold=10%. | Asset: IWM
Daily returns (past 60 days): mean=0.0016, std=0.0130, worst_day=-0.0357
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-10-23] What's The Downside For Texas Instruments Stock? Texas Instruments stock (NASDAQ: TXN) is up 64% since the beginning of this year... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0325 (i.e., a 3.25% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0325 = 3.0802, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.032465,
"expected_loss": 0.032465,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20200701_0980 | T3 | 1 | train | sideways | all | [
"EEM"
] | 2020-07-01T00:00:00 | EEM: 60-day history, VaR(99%)=-0.0341, max drawdown threshold=10%. | Asset: EEM
Daily returns (past 60 days): mean=0.0031, std=0.0162, worst_day=-0.0341
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-06-30] ["Dips in Advanced Micro Devices Stock Remain Buying Opportunities InvestorPlace - Stock Market News, Stock Advice & Trading Tips... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0341 (i.e., a 3.41% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0341 = 2.9314, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.034114,
"expected_loss": 0.034114,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20150907_0982 | T3 | 1 | train | sideways | all | [
"EFA"
] | 2015-09-07T00:00:00 | EFA: 60-day history, VaR(99%)=-0.0289, max drawdown threshold=10%. | Asset: EFA
Daily returns (past 60 days): mean=-0.0020, std=0.0122, worst_day=-0.0289
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2015-09-04] Dow 30 Stock Roundup: GE Announces Divestment of Fleet Businesses, Intel Unveils 6th Gen Intel Core chips The Dow endured a mixe... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0289 (i.e., a 2.89% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0289 = 3.4601, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.028901,
"expected_loss": 0.028901,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220203_0985 | T3 | 1 | train | sideways | all | [
"XLU"
] | 2022-02-03T00:00:00 | XLU: 60-day history, VaR(99%)=-0.0215, max drawdown threshold=10%. | Asset: XLU
Daily returns (past 60 days): mean=0.0006, std=0.0096, worst_day=-0.0297
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-02-02] ["Dick's Sporting Goods Inc. and Adobe Inc. highlighted as Zacks Bull and Bear of the Day For Immediate Release Chicago, IL \u201... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0215 (i.e., a 2.15% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0215 = 4.6526, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.021494,
"expected_loss": 0.021494,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220426_0987 | T3 | 1 | train | sideways | all | [
"QQQ"
] | 2022-04-26T00:00:00 | QQQ: 60-day history, VaR(99%)=-0.0385, max drawdown threshold=10%. | Asset: QQQ
Daily returns (past 60 days): mean=-0.0006, std=0.0201, worst_day=-0.0399
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-04-25] ["Adobe Systems (ADBE) Outpaces Stock Market Gains: What You Should Know Adobe Systems (ADBE) closed at $413.95 in the latest tr... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0385 (i.e., a 3.85% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0385 = 2.5950, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.038536,
"expected_loss": 0.038536,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220923_0989 | T3 | 1 | train | sideways | all | [
"EWJ"
] | 2022-09-23T00:00:00 | EWJ: 60-day history, VaR(99%)=-0.0279, max drawdown threshold=10%. | Asset: EWJ
Daily returns (past 60 days): mean=-0.0007, std=0.0111, worst_day=-0.0325
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-09-22] ["My 3 Favorite Stocks Right Now Investing more capital into your best ideas can lead to outsize rewards. The challenge, of cour... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0279 (i.e., a 2.79% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0279 = 3.5860, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.027885999999999998,
"expected_loss": 0.027885999999999998,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220718_0991 | T3 | 1 | train | sideways | all | [
"AVAX-USD"
] | 2022-07-18T00:00:00 | AVAX-USD: 60-day history, VaR(99%)=-0.1355, max drawdown threshold=10%. | Asset: AVAX-USD
Daily returns (past 60 days): mean=-0.0037, std=0.0689, worst_day=-0.1362
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-07-17]
Determine the maximum fraction of total portfolio capital that should be allocated to AVAX-USD, given the drawdown constr... | 0.7382 | 0.7382 | Step 1: Compute |VaR(99%)| from historical returns = 0.1355 (i.e., a 13.55% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1355 = 0.7382, capped at 1.0.
Maximum position size = 0.7382 (73.8% of portfolio). | {
"var_99": -0.13547299999999998,
"expected_loss": 0.13547299999999998,
"max_drawdown_threshold": 0.1,
"position_size": 0.7382000000000001,
"has_text": true,
"text_chars": 20
} |
T3_all_20200803_0993 | T3 | 1 | train | sideways | all | [
"XLRE"
] | 2020-08-03T00:00:00 | XLRE: 60-day history, VaR(99%)=-0.0375, max drawdown threshold=10%. | Asset: XLRE
Daily returns (past 60 days): mean=0.0022, std=0.0167, worst_day=-0.0375
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-07-31] ["Facebook Results Impress Despite Pandemic and Advertising Boycott The social media giant reported second-quarter sales of $18.... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0375 (i.e., a 3.75% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0375 = 2.6646, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.037529,
"expected_loss": 0.037529,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20190404_0995 | T3 | 1 | train | sideways | all | [
"LINK-USD"
] | 2019-04-04T00:00:00 | LINK-USD: 60-day history, VaR(99%)=-0.0871, max drawdown threshold=10%. | Asset: LINK-USD
Daily returns (past 60 days): mean=0.0061, std=0.0445, worst_day=-0.1110
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to LINK-USD, given the drawdown constraint. Report as a decimal between 0.00 and... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0871 (i.e., a 8.71% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0871 = 1.1479, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.087119,
"expected_loss": 0.087119,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20220106_0997 | T3 | 1 | train | sideways | all | [
"LINK-USD"
] | 2022-01-06T00:00:00 | LINK-USD: 60-day history, VaR(99%)=-0.1202, max drawdown threshold=10%. | Asset: LINK-USD
Daily returns (past 60 days): mean=-0.0022, std=0.0573, worst_day=-0.1311
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-01-05]
Determine the maximum fraction of total portfolio capital that should be allocated to LINK-USD, given the drawdown constr... | 0.8316 | 0.8316 | Step 1: Compute |VaR(99%)| from historical returns = 0.1202 (i.e., a 12.02% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1202 = 0.8316, capped at 1.0.
Maximum position size = 0.8316 (83.2% of portfolio). | {
"var_99": -0.120248,
"expected_loss": 0.120248,
"max_drawdown_threshold": 0.1,
"position_size": 0.8316,
"has_text": true,
"text_chars": 20
} |
T3_all_20200507_0999 | T3 | 1 | train | sideways | all | [
"XLB"
] | 2020-05-07T00:00:00 | XLB: 60-day history, VaR(99%)=-0.0337, max drawdown threshold=10%. | Asset: XLB
Daily returns (past 60 days): mean=-0.0014, std=0.0254, worst_day=-0.0337
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-05-06] ["Wednesday Sector Laggards: Utilities, Financial In afternoon trading on Wednesday, Utilities stocks are the worst performing s... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0337 (i.e., a 3.37% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0337 = 2.9659, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.033715999999999996,
"expected_loss": 0.033715999999999996,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T4_all_20160523_0001 | T4 | 2 | train | sideways | all | [
"IVV",
"BIL"
] | 2016-05-23T00:00:00 | IVV σ=0.0071, BIL σ=0.0002, ρ=0.088. Min-variance weights: IVV=0.000, BIL=1.000. | Assets: IVV, BIL
IVV: annualized_mean_return=0.2016, daily_std=0.0071
BIL: annualized_mean_return=0.0000, daily_std=0.0002
Minimum required portfolio return (annualized): 0.0001
Market regime: sideways
Compute portfolio weights (w_IVV, w_BIL) that minimize portfolio variance while satisfying the minimum return constra... | w_IVV=0.0006, w_BIL=0.9994 | 0.0006 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000000 - 0.000000) / (0.000050 + 0.000000 - 0.000000)
Unconstrained: w_IVV=-0.0015
After long-only clamp: w_IVV=0.0000, w_BIL=1.0000. | {
"weights": {
"IVV": 0.0006000000000000001,
"BIL": 0.9994000000000001
},
"sigma_1": 0.00706,
"sigma_2": 0.00017,
"covariance": 0,
"correlation": 0.0877,
"has_text": true,
"text_chars": 3020,
"mu_floor": 0.0001,
"constraint_binding": false
} |
T4_all_20180613_0004 | T4 | 2 | train | sideways | all | [
"XRP-USD",
"ICSH"
] | 2018-06-13T00:00:00 | XRP-USD σ=0.0546, ICSH σ=0.0003, ρ=0.230. Min-variance weights: XRP-USD=0.000, ICSH=1.000. | Assets: XRP-USD, ICSH
XRP-USD: annualized_mean_return=-0.1512, daily_std=0.0546
ICSH: annualized_mean_return=0.0252, daily_std=0.0003
Minimum required portfolio return (annualized): -0.0667
Market regime: sideways
Compute portfolio weights (w_XRP-USD, w_ICSH) that minimize portfolio variance while satisfying the minim... | w_XRP-USD=0.0000, w_ICSH=1.0000 | 0 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000000 - 0.000003) / (0.002980 + 0.000000 - 0.000007)
Unconstrained: w_XRP-USD=-0.0011
After long-only clamp: w_XRP-USD=0.0000, w_ICSH=1.0000. | {
"weights": {
"XRP-USD": 0,
"ICSH": 1
},
"sigma_1": 0.05459000000000001,
"sigma_2": 0.000259,
"covariance": 0.000003,
"correlation": 0.2303,
"has_text": false,
"text_chars": 0,
"mu_floor": -0.06670000000000001,
"constraint_binding": false
} |
T4_all_20210326_0009 | T4 | 2 | train | sideways | all | [
"SOL-USD",
"ICSH"
] | 2021-03-26T00:00:00 | SOL-USD σ=0.0885, ICSH σ=0.0002, ρ=0.269. Min-variance weights: SOL-USD=0.000, ICSH=1.000. | Assets: SOL-USD, ICSH
SOL-USD: annualized_mean_return=6.3000, daily_std=0.0885
ICSH: annualized_mean_return=0.0000, daily_std=0.0002
Minimum required portfolio return (annualized): -0.0000
Market regime: sideways
Compute portfolio weights (w_SOL-USD, w_ICSH) that minimize portfolio variance while satisfying the minimu... | w_SOL-USD=0.0000, w_ICSH=1.0000 | 0 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000000 - 0.000005) / (0.007838 + 0.000000 - 0.000010)
Unconstrained: w_SOL-USD=-0.0007
After long-only clamp: w_SOL-USD=0.0000, w_ICSH=1.0000. | {
"weights": {
"SOL-USD": 0,
"ICSH": 1
},
"sigma_1": 0.088531,
"sigma_2": 0.00021899999999999998,
"covariance": 0.0000049999999999999996,
"correlation": 0.26880000000000004,
"has_text": false,
"text_chars": 0,
"mu_floor": 0,
"constraint_binding": false
} |
T4_all_20210520_0012 | T4 | 2 | train | sideways | all | [
"EEM",
"INDS"
] | 2021-05-20T00:00:00 | EEM σ=0.0131, INDS σ=0.0101, ρ=0.391. Min-variance weights: EEM=0.299, INDS=0.701. | Assets: EEM, INDS
EEM: annualized_mean_return=-0.2016, daily_std=0.0131
INDS: annualized_mean_return=0.3528, daily_std=0.0101
Minimum required portfolio return (annualized): 0.2527
Market regime: sideways
Compute portfolio weights (w_EEM, w_INDS) that minimize portfolio variance while satisfying the minimum return con... | w_EEM=0.1806, w_INDS=0.8194 | 0.1806 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000103 - 0.000052) / (0.000171 + 0.000103 - 0.000104)
Unconstrained: w_EEM=0.2994
After long-only clamp: w_EEM=0.2994, w_INDS=0.7006. | {
"weights": {
"EEM": 0.1806,
"INDS": 0.8194
},
"sigma_1": 0.013087,
"sigma_2": 0.010145,
"covariance": 0.000052,
"correlation": 0.391,
"has_text": true,
"text_chars": 3020,
"mu_floor": 0.25270000000000004,
"constraint_binding": true
} |
T4_all_20220127_0015 | T4 | 2 | train | sideways | all | [
"BNB-USD",
"SGOV"
] | 2022-01-27T00:00:00 | BNB-USD σ=0.0389, SGOV σ=0.0001, ρ=0.060. Min-variance weights: BNB-USD=0.000, SGOV=1.000. | Assets: BNB-USD, SGOV
BNB-USD: annualized_mean_return=-1.7640, daily_std=0.0389
SGOV: annualized_mean_return=0.0000, daily_std=0.0001
Minimum required portfolio return (annualized): -0.0752
Market regime: sideways
Compute portfolio weights (w_BNB-USD, w_SGOV) that minimize portfolio variance while satisfying the minim... | w_BNB-USD=0.0000, w_SGOV=1.0000 | 0 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000000 - 0.000000) / (0.001510 + 0.000000 - 0.000000)
Unconstrained: w_BNB-USD=-0.0001
After long-only clamp: w_BNB-USD=0.0000, w_SGOV=1.0000. | {
"weights": {
"BNB-USD": 0,
"SGOV": 1
},
"sigma_1": 0.038861,
"sigma_2": 0.000059,
"covariance": 0,
"correlation": 0.0601,
"has_text": true,
"text_chars": 20,
"mu_floor": -0.0752,
"constraint_binding": false
} |
T4_all_20210218_0018 | T4 | 2 | train | sideways | all | [
"IWM",
"DBA"
] | 2021-02-18T00:00:00 | IWM σ=0.0121, DBA σ=0.0063, ρ=0.023. Min-variance weights: IWM=0.208, DBA=0.792. | Assets: IWM, DBA
IWM: annualized_mean_return=1.0080, daily_std=0.0121
DBA: annualized_mean_return=0.3780, daily_std=0.0063
Minimum required portfolio return (annualized): 0.7981
Market regime: sideways
Compute portfolio weights (w_IWM, w_DBA) that minimize portfolio variance while satisfying the minimum return constra... | w_IWM=0.6668, w_DBA=0.3332 | 0.6668 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000040 - 0.000002) / (0.000147 + 0.000040 - 0.000003)
Unconstrained: w_IWM=0.2083
After long-only clamp: w_IWM=0.2083, w_DBA=0.7917. | {
"weights": {
"IWM": 0.6668000000000001,
"DBA": 0.3332
},
"sigma_1": 0.012105999999999999,
"sigma_2": 0.006312,
"covariance": 0.000002,
"correlation": 0.022600000000000002,
"has_text": true,
"text_chars": 3020,
"mu_floor": 0.7981,
"constraint_binding": true
} |
T4_all_20200624_0023 | T4 | 2 | train | sideways | all | [
"XRP-USD",
"IYR"
] | 2020-06-24T00:00:00 | XRP-USD σ=0.0262, IYR σ=0.0210, ρ=-0.103. Min-variance weights: XRP-USD=0.403, IYR=0.597. | Assets: XRP-USD, IYR
XRP-USD: annualized_mean_return=-0.0252, daily_std=0.0262
IYR: annualized_mean_return=0.3276, daily_std=0.0210
Minimum required portfolio return (annualized): 0.0929
Market regime: sideways
Compute portfolio weights (w_XRP-USD, w_IYR) that minimize portfolio variance while satisfying the minimum r... | w_XRP-USD=0.4029, w_IYR=0.5971 | 0.4029 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000443 - -0.000057) / (0.000684 + 0.000443 - -0.000114)
Unconstrained: w_XRP-USD=0.4028
After long-only clamp: w_XRP-USD=0.4028, w_IYR=0.5972. | {
"weights": {
"XRP-USD": 0.40290000000000004,
"IYR": 0.5971000000000001
},
"sigma_1": 0.026154,
"sigma_2": 0.021047,
"covariance": -0.000056999999999999996,
"correlation": -0.1034,
"has_text": false,
"text_chars": 0,
"mu_floor": 0.09290000000000001,
"constraint_binding": false
} |
T4_all_20200901_0028 | T4 | 2 | train | sideways | all | [
"XLV",
"REZ"
] | 2020-09-01T00:00:00 | XLV σ=0.0094, REZ σ=0.0164, ρ=0.504. Min-variance weights: XLV=0.946, REZ=0.054. | Assets: XLV, REZ
XLV: annualized_mean_return=0.3276, daily_std=0.0094
REZ: annualized_mean_return=-0.0504, daily_std=0.0164
Minimum required portfolio return (annualized): 0.3210
Market regime: sideways
Compute portfolio weights (w_XLV, w_REZ) that minimize portfolio variance while satisfying the minimum return constr... | w_XLV=0.9825, w_REZ=0.0175 | 0.9825 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000270 - 0.000078) / (0.000089 + 0.000270 - 0.000156)
Unconstrained: w_XLV=0.9464
After long-only clamp: w_XLV=0.9464, w_REZ=0.0536. | {
"weights": {
"XLV": 0.9825,
"REZ": 0.0175
},
"sigma_1": 0.009422999999999999,
"sigma_2": 0.016423999999999998,
"covariance": 0.000078,
"correlation": 0.5036,
"has_text": true,
"text_chars": 3020,
"mu_floor": 0.321,
"constraint_binding": true
} |
T4_all_20180727_0033 | T4 | 2 | train | sideways | all | [
"IWM",
"CORN"
] | 2018-07-27T00:00:00 | IWM σ=0.0064, CORN σ=0.0116, ρ=0.162. Min-variance weights: IWM=0.806, CORN=0.194. | Assets: IWM, CORN
IWM: annualized_mean_return=0.3780, daily_std=0.0064
CORN: annualized_mean_return=-0.3024, daily_std=0.0116
Minimum required portfolio return (annualized): 0.1073
Market regime: sideways
Compute portfolio weights (w_IWM, w_CORN) that minimize portfolio variance while satisfying the minimum return con... | w_IWM=0.8057, w_CORN=0.1943 | 0.8057 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000135 - 0.000012) / (0.000042 + 0.000135 - 0.000024)
Unconstrained: w_IWM=0.8062
After long-only clamp: w_IWM=0.8062, w_CORN=0.1938. | {
"weights": {
"IWM": 0.8057000000000001,
"CORN": 0.1943
},
"sigma_1": 0.006448,
"sigma_2": 0.011602,
"covariance": 0.000012,
"correlation": 0.1623,
"has_text": true,
"text_chars": 3020,
"mu_floor": 0.1073,
"constraint_binding": false
} |
T4_all_20160802_0040 | T4 | 2 | train | sideways | all | [
"BTC-USD",
"SOYB"
] | 2016-08-02T00:00:00 | BTC-USD σ=0.0381, SOYB σ=0.0147, ρ=0.079. Min-variance weights: BTC-USD=0.109, SOYB=0.891. | Assets: BTC-USD, SOYB
BTC-USD: annualized_mean_return=0.6804, daily_std=0.0381
SOYB: annualized_mean_return=-0.0252, daily_std=0.0147
Minimum required portfolio return (annualized): 0.3972
Market regime: sideways
Compute portfolio weights (w_BTC-USD, w_SOYB) that minimize portfolio variance while satisfying the minimu... | w_BTC-USD=0.5986, w_SOYB=0.4014 | 0.5986 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000216 - 0.000044) / (0.001451 + 0.000216 - 0.000088)
Unconstrained: w_BTC-USD=0.1087
After long-only clamp: w_BTC-USD=0.1087, w_SOYB=0.8913. | {
"weights": {
"BTC-USD": 0.5986,
"SOYB": 0.40140000000000003
},
"sigma_1": 0.038092999999999995,
"sigma_2": 0.014681999999999999,
"covariance": 0.000044,
"correlation": 0.0786,
"has_text": false,
"text_chars": 0,
"mu_floor": 0.3972,
"constraint_binding": true
} |
T4_all_20171107_0043 | T4 | 2 | train | sideways | all | [
"BTC-USD",
"LQD"
] | 2017-11-07T00:00:00 | BTC-USD σ=0.0450, LQD σ=0.0022, ρ=0.032. Min-variance weights: BTC-USD=0.001, LQD=0.999. | Assets: BTC-USD, LQD
BTC-USD: annualized_mean_return=2.0664, daily_std=0.0450
LQD: annualized_mean_return=0.0504, daily_std=0.0022
Minimum required portfolio return (annualized): 0.0511
Market regime: sideways
Compute portfolio weights (w_BTC-USD, w_LQD) that minimize portfolio variance while satisfying the minimum re... | w_BTC-USD=0.0008, w_LQD=0.9992 | 0.0008 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000005 - 0.000003) / (0.002026 + 0.000005 - 0.000006)
Unconstrained: w_BTC-USD=0.0008
After long-only clamp: w_BTC-USD=0.0008, w_LQD=0.9992. | {
"weights": {
"BTC-USD": 0.0008,
"LQD": 0.9992000000000001
},
"sigma_1": 0.045014,
"sigma_2": 0.002156,
"covariance": 0.000003,
"correlation": 0.0317,
"has_text": false,
"text_chars": 0,
"mu_floor": 0.0511,
"constraint_binding": false
} |
T4_all_20211130_0046 | T4 | 2 | train | sideways | all | [
"BNB-USD",
"ICSH"
] | 2021-11-30T00:00:00 | BNB-USD σ=0.0397, ICSH σ=0.0002, ρ=0.241. Min-variance weights: BNB-USD=0.000, ICSH=1.000. | Assets: BNB-USD, ICSH
BNB-USD: annualized_mean_return=2.2176, daily_std=0.0397
ICSH: annualized_mean_return=-0.0000, daily_std=0.0002
Minimum required portfolio return (annualized): 0.8328
Market regime: sideways
Compute portfolio weights (w_BNB-USD, w_ICSH) that minimize portfolio variance while satisfying the minimu... | w_BNB-USD=0.3755, w_ICSH=0.6245 | 0.3755 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000000 - 0.000002) / (0.001578 + 0.000000 - 0.000004)
Unconstrained: w_BNB-USD=-0.0012
After long-only clamp: w_BNB-USD=0.0000, w_ICSH=1.0000. | {
"weights": {
"BNB-USD": 0.3755,
"ICSH": 0.6245
},
"sigma_1": 0.039717999999999996,
"sigma_2": 0.000194,
"covariance": 0.000002,
"correlation": 0.2409,
"has_text": true,
"text_chars": 20,
"mu_floor": 0.8328,
"constraint_binding": true
} |
T4_all_20221006_0049 | T4 | 2 | train | sideways | all | [
"DOT-USD",
"USMV"
] | 2022-10-06T00:00:00 | DOT-USD σ=0.0363, USMV σ=0.0107, ρ=-0.103. Min-variance weights: DOT-USD=0.101, USMV=0.899. | Assets: DOT-USD, USMV
DOT-USD: annualized_mean_return=-1.0080, daily_std=0.0363
USMV: annualized_mean_return=-0.0504, daily_std=0.0107
Minimum required portfolio return (annualized): -0.5071
Market regime: sideways
Compute portfolio weights (w_DOT-USD, w_USMV) that minimize portfolio variance while satisfying the mini... | w_DOT-USD=0.1014, w_USMV=0.8986 | 0.1014 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000114 - -0.000040) / (0.001321 + 0.000114 - -0.000079)
Unconstrained: w_DOT-USD=0.1013
After long-only clamp: w_DOT-USD=0.1013, w_USMV=0.8987. | {
"weights": {
"DOT-USD": 0.1014,
"USMV": 0.8986000000000001
},
"sigma_1": 0.036349,
"sigma_2": 0.010659,
"covariance": -0.00004,
"correlation": -0.10260000000000001,
"has_text": true,
"text_chars": 20,
"mu_floor": -0.5071,
"constraint_binding": false
} |
T4_all_20210503_0052 | T4 | 2 | train | sideways | all | [
"MATIC-USD",
"GLD"
] | 2021-05-03T00:00:00 | MATIC-USD σ=0.1198, GLD σ=0.0086, ρ=-0.098. Min-variance weights: MATIC-USD=0.012, GLD=0.988. | Assets: MATIC-USD, GLD
MATIC-USD: annualized_mean_return=6.6780, daily_std=0.1198
GLD: annualized_mean_return=-0.1260, daily_std=0.0086
Minimum required portfolio return (annualized): 4.8021
Market regime: sideways
Compute portfolio weights (w_MATIC-USD, w_GLD) that minimize portfolio variance while satisfying the min... | w_MATIC-USD=0.7243, w_GLD=0.2757 | 0.7243 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000075 - -0.000101) / (0.014347 + 0.000075 - -0.000203)
Unconstrained: w_MATIC-USD=0.0120
After long-only clamp: w_MATIC-USD=0.0120, w_GLD=0.9880. | {
"weights": {
"MATIC-USD": 0.7243,
"GLD": 0.2757
},
"sigma_1": 0.119781,
"sigma_2": 0.008636,
"covariance": -0.000101,
"correlation": -0.0981,
"has_text": false,
"text_chars": 0,
"mu_floor": 4.8021,
"constraint_binding": true
} |
T4_all_20180129_0055 | T4 | 2 | train | sideways | all | [
"BNB-USD",
"CORN"
] | 2018-01-29T00:00:00 | BNB-USD σ=0.1088, CORN σ=0.0055, ρ=-0.229. Min-variance weights: BNB-USD=0.014, CORN=0.986. | Assets: BNB-USD, CORN
BNB-USD: annualized_mean_return=8.5932, daily_std=0.1088
CORN: annualized_mean_return=-0.1008, daily_std=0.0055
Minimum required portfolio return (annualized): -0.0306
Market regime: sideways
Compute portfolio weights (w_BNB-USD, w_CORN) that minimize portfolio variance while satisfying the minim... | w_BNB-USD=0.0139, w_CORN=0.9861 | 0.0139 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000030 - -0.000138) / (0.011839 + 0.000030 - -0.000275)
Unconstrained: w_BNB-USD=0.0138
After long-only clamp: w_BNB-USD=0.0138, w_CORN=0.9862. | {
"weights": {
"BNB-USD": 0.013900000000000001,
"CORN": 0.9861000000000001
},
"sigma_1": 0.108808,
"sigma_2": 0.005510999999999999,
"covariance": -0.000138,
"correlation": -0.2293,
"has_text": false,
"text_chars": 0,
"mu_floor": -0.030600000000000002,
"constraint_binding": false
} |
T4_all_20181008_0058 | T4 | 2 | train | sideways | all | [
"EWJ",
"VCIT"
] | 2018-10-08T00:00:00 | EWJ σ=0.0070, VCIT σ=0.0016, ρ=0.104. Min-variance weights: EWJ=0.027, VCIT=0.973. | Assets: EWJ, VCIT
EWJ: annualized_mean_return=0.1512, daily_std=0.0070
VCIT: annualized_mean_return=-0.0252, daily_std=0.0016
Minimum required portfolio return (annualized): 0.0473
Market regime: sideways
Compute portfolio weights (w_EWJ, w_VCIT) that minimize portfolio variance while satisfying the minimum return con... | w_EWJ=0.4110, w_VCIT=0.5890 | 0.411 | Analytic min-variance formula:
w1* = (σ2² - σ12) / (σ1² + σ2² - 2σ12)
= (0.000002 - 0.000001) / (0.000049 + 0.000002 - 0.000002)
Unconstrained: w_EWJ=0.0271
After long-only clamp: w_EWJ=0.0271, w_VCIT=0.9729. | {
"weights": {
"EWJ": 0.41100000000000003,
"VCIT": 0.589
},
"sigma_1": 0.006973999999999999,
"sigma_2": 0.001571,
"covariance": 0.000001,
"correlation": 0.1044,
"has_text": true,
"text_chars": 3020,
"mu_floor": 0.0473,
"constraint_binding": true
} |
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