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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T2_all_20201130_0882 | T2 | 1 | train | sideways | all | [
"ETH-USD"
] | 2020-11-30T00:00:00 | ETH-USD: 60-day return history, mean=0.0085, std=0.0365. | Asset: ETH-USD
Daily returns (past 60 days): mean=0.0085, std=0.0365, min=-0.0909, max=0.0982
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for ETH-USD. Express as a decimal (e.g., -0.02). | -0.0382 | -0.038177 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0382 (i.e., on a bad day with 5% probability, the loss exceeds 3.82%). CVaR(95%) = -0.0626. | {
"var": -0.038176999999999996,
"cvar": -0.06257,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20210924_0884 | T2 | 1 | train | sideways | all | [
"USMV"
] | 2021-09-24T00:00:00 | USMV: 60-day return history, mean=0.0005, std=0.0049. | Asset: USMV
Daily returns (past 60 days): mean=0.0005, std=0.0049, min=-0.0115, max=0.0103
Market regime: sideways
Recent filing/news:
[Kaggle 2021-09-23] Interesting ADBE Put And Call Options For November 5th Investors in Adobe Inc (Symbol: ADBE) saw new options become available today, for the November 5th expiration.... | -0.0094 | -0.009352 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0094 (i.e., on a bad day with 5% probability, the loss exceeds 0.94%). CVaR(95%) = -0.0106. | {
"var": -0.009352,
"cvar": -0.010621,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20160908_0886 | T2 | 1 | train | sideways | all | [
"VNQI"
] | 2016-09-08T00:00:00 | VNQI: 60-day return history, mean=0.0019, std=0.0085. | Asset: VNQI
Daily returns (past 60 days): mean=0.0019, std=0.0085, min=-0.0251, max=0.0246
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for VNQI. Express as a decimal (e.g., -0.02). | -0.0095 | -0.009484 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0095 (i.e., on a bad day with 5% probability, the loss exceeds 0.95%). CVaR(95%) = -0.0172. | {
"var": -0.009484,
"cvar": -0.017169,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20170116_0887 | T2 | 1 | train | sideways | all | [
"EWJ"
] | 2017-01-16T00:00:00 | EWJ: 60-day return history, mean=0.0006, std=0.0071. | Asset: EWJ
Daily returns (past 60 days): mean=0.0006, std=0.0071, min=-0.0155, max=0.0199
Market regime: sideways
Recent filing/news:
[Kaggle 2017-01-13] ["Nintendo, Sony Investors Can Expect Gains of 50% Nintendo\u2019s Switch gaming console and a turnaround at Sony can send shares of both companies surging.", "Why th... | -0.0103 | -0.010271 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0103 (i.e., on a bad day with 5% probability, the loss exceeds 1.03%). CVaR(95%) = -0.0142. | {
"var": -0.010270999999999999,
"cvar": -0.014206,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20190912_0889 | T2 | 1 | train | sideways | all | [
"XLF"
] | 2019-09-12T00:00:00 | XLF: 60-day return history, mean=0.0008, std=0.0120. | Asset: XLF
Daily returns (past 60 days): mean=0.0008, std=0.0120, min=-0.0372, max=0.0202
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 in early trading Wednesday, ahead of e... | -0.0231 | -0.023056 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0231 (i.e., on a bad day with 5% probability, the loss exceeds 2.31%). CVaR(95%) = -0.0323. | {
"var": -0.023056,
"cvar": -0.032278,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20200203_0892 | T2 | 1 | train | sideways | all | [
"XLU"
] | 2020-02-03T00:00:00 | XLU: 60-day return history, mean=0.0016, std=0.0057. | Asset: XLU
Daily returns (past 60 days): mean=0.0016, std=0.0057, min=-0.0136, max=0.0147
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 the list of the most despised companie... | -0.0094 | -0.009383 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0094 (i.e., on a bad day with 5% probability, the loss exceeds 0.94%). CVaR(95%) = -0.0122. | {
"var": -0.009382999999999999,
"cvar": -0.012185,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20180411_0894 | T2 | 1 | train | sideways | all | [
"LQD"
] | 2018-04-11T00:00:00 | LQD: 60-day return history, mean=-0.0003, std=0.0029. | Asset: LQD
Daily returns (past 60 days): mean=-0.0003, std=0.0029, min=-0.0055, max=0.0051
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for LQD. Express as a decimal (e.g., -0.02). | -0.0043 | -0.004349 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0043 (i.e., on a bad day with 5% probability, the loss exceeds 0.43%). CVaR(95%) = -0.0050. | {
"var": -0.004349,
"cvar": -0.004999,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20220531_0898 | T2 | 1 | train | sideways | all | [
"AVAX-USD"
] | 2022-05-31T00:00:00 | AVAX-USD: 60-day return history, mean=-0.0160, std=0.0662. | Asset: AVAX-USD
Daily returns (past 60 days): mean=-0.0160, std=0.0662, min=-0.1912, max=0.1245
Market regime: sideways
Recent filing/news:
[Kaggle 2022-05-30]
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for AVAX-USD. Express as a decimal (e.g., -0.02). | -0.1352 | -0.135212 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.1352 (i.e., on a bad day with 5% probability, the loss exceeds 13.52%). CVaR(95%) = -0.1719. | {
"var": -0.135212,
"cvar": -0.17194099999999998,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 20
} |
T2_all_20160525_0900 | T2 | 1 | train | sideways | all | [
"IWM"
] | 2016-05-25T00:00:00 | IWM: 60-day return history, mean=0.0016, std=0.0111. | Asset: IWM
Daily returns (past 60 days): mean=0.0016, std=0.0111, min=-0.0244, max=0.0274
Market regime: sideways
Recent filing/news:
[Kaggle 2016-05-24] ["Taiwan Market Seen To Stabilize As Tsai Takes Office Among Asian markets, Taiwan has been sold off the most in May. In the first three weeks, foreigners net sold $3... | -0.0160 | -0.016006 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0160 (i.e., on a bad day with 5% probability, the loss exceeds 1.60%). CVaR(95%) = -0.0199. | {
"var": -0.016006,
"cvar": -0.019878,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20151020_0902 | T2 | 1 | train | sideways | all | [
"XLI"
] | 2015-10-20T00:00:00 | XLI: 60-day return history, mean=0.0000, std=0.0138. | Asset: XLI
Daily returns (past 60 days): mean=0.0000, std=0.0138, min=-0.0336, max=0.0303
Market regime: sideways
Recent filing/news:
[Kaggle 2015-10-19] Equifax (EFX) to Report Q3 Earnings: What's in the Cards? Equifax Inc. EFX is scheduled to report third-quarter 2015 results on Oct 21. Last quarter, the company post... | -0.0226 | -0.022567 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0226 (i.e., on a bad day with 5% probability, the loss exceeds 2.26%). CVaR(95%) = -0.0295. | {
"var": -0.022567,
"cvar": -0.029473,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20200427_0904 | T2 | 1 | train | sideways | all | [
"ICSH"
] | 2020-04-27T00:00:00 | ICSH: 60-day return history, mean=0.0003, std=0.0034. | Asset: ICSH
Daily returns (past 60 days): mean=0.0003, std=0.0034, min=-0.0147, max=0.0092
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for ICSH. Express as a decimal (e.g., -0.02). | -0.0057 | -0.005695 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0057 (i.e., on a bad day with 5% probability, the loss exceeds 0.57%). CVaR(95%) = -0.0105. | {
"var": -0.0056949999999999995,
"cvar": -0.010498,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20220215_0906 | T2 | 1 | train | sideways | all | [
"ETH-USD"
] | 2022-02-15T00:00:00 | ETH-USD: 60-day return history, mean=-0.0042, std=0.0387. | Asset: ETH-USD
Daily returns (past 60 days): mean=-0.0042, std=0.0387, min=-0.1477, max=0.1136
Market regime: sideways
Recent filing/news:
[Kaggle 2022-02-14]
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for ETH-USD. Express as a decimal (e.g., -0.02). | -0.0599 | -0.059942 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0599 (i.e., on a bad day with 5% probability, the loss exceeds 5.99%). CVaR(95%) = -0.0926. | {
"var": -0.059941999999999995,
"cvar": -0.092592,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 20
} |
T2_all_20161228_0908 | T2 | 1 | train | sideways | all | [
"QQQ"
] | 2016-12-28T00:00:00 | QQQ: 60-day return history, mean=0.0003, std=0.0075. | Asset: QQQ
Daily returns (past 60 days): mean=0.0003, std=0.0075, min=-0.0174, max=0.0235
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.com announces the list of stocks fea... | -0.0123 | -0.01229 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0123 (i.e., on a bad day with 5% probability, the loss exceeds 1.23%). CVaR(95%) = -0.0160. | {
"var": -0.01229,
"cvar": -0.016024,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20150814_0910 | T2 | 1 | train | sideways | all | [
"XLF"
] | 2015-08-14T00:00:00 | XLF: 60-day return history, mean=0.0001, std=0.0082. | Asset: XLF
Daily returns (past 60 days): mean=0.0001, std=0.0082, min=-0.0244, max=0.0152
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 Call; 2000 Contracts @Ask @$0.32; Now $1... | -0.0116 | -0.011602 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0116 (i.e., on a bad day with 5% probability, the loss exceeds 1.16%). CVaR(95%) = -0.0196. | {
"var": -0.011602,
"cvar": -0.019625999999999998,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20200603_0912 | T2 | 1 | train | sideways | all | [
"IYR"
] | 2020-06-03T00:00:00 | IYR: 60-day return history, mean=0.0001, std=0.0239. | Asset: IYR
Daily returns (past 60 days): mean=0.0001, std=0.0239, min=-0.0339, max=0.0302
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for IYR. Express as a decimal (e.g., -0.02). | -0.0339 | -0.033943 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0339 (i.e., on a bad day with 5% probability, the loss exceeds 3.39%). CVaR(95%) = -0.0339. | {
"var": -0.033943,
"cvar": -0.033943,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20171120_0914 | T2 | 1 | train | sideways | all | [
"BTC-USD"
] | 2017-11-20T00:00:00 | BTC-USD: 60-day return history, mean=0.0127, std=0.0417. | Asset: BTC-USD
Daily returns (past 60 days): mean=0.0127, std=0.0417, min=-0.0736, max=0.1157
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for BTC-USD. Express as a decimal (e.g., -0.02). | -0.0643 | -0.0643 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0643 (i.e., on a bad day with 5% probability, the loss exceeds 6.43%). CVaR(95%) = -0.0707. | {
"var": -0.06430000000000001,
"cvar": -0.070669,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20180807_0916 | T2 | 1 | train | sideways | all | [
"EWJ"
] | 2018-08-07T00:00:00 | EWJ: 60-day return history, mean=-0.0006, std=0.0062. | Asset: EWJ
Daily returns (past 60 days): mean=-0.0006, std=0.0062, min=-0.0160, max=0.0137
Market regime: sideways
Recent filing/news:
[Kaggle 2018-08-06] ["Zacks.com highlights: Mellanox Technologies, Fortinet, Commvault Systems and Adobe Systems For Immediate Release Chicago, IL - August 6, 2018 - Stocks in this week... | -0.0108 | -0.01081 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0108 (i.e., on a bad day with 5% probability, the loss exceeds 1.08%). CVaR(95%) = -0.0145. | {
"var": -0.01081,
"cvar": -0.014546,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20220606_0918 | T2 | 1 | train | sideways | all | [
"EFA"
] | 2022-06-06T00:00:00 | EFA: 60-day return history, mean=-0.0002, std=0.0131. | Asset: EFA
Daily returns (past 60 days): mean=-0.0002, std=0.0131, min=-0.0289, max=0.0259
Market regime: sideways
Recent filing/news:
[Kaggle 2022-06-03] ["Apple Was the Worst Stock in the Dow Friday The Dow Jones Industrial Average dropped close to 350 points on Friday, despite a better-than-expected jobs report. For... | -0.0244 | -0.024364 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0244 (i.e., on a bad day with 5% probability, the loss exceeds 2.44%). CVaR(95%) = -0.0287. | {
"var": -0.024364,
"cvar": -0.028716,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20191211_0920 | T2 | 1 | train | sideways | all | [
"IWM"
] | 2019-12-11T00:00:00 | IWM: 60-day return history, mean=0.0005, std=0.0078. | Asset: IWM
Daily returns (past 60 days): mean=0.0005, std=0.0078, min=-0.0198, max=0.0210
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 earnings in early December. In response, DO... | -0.0114 | -0.011424 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0114 (i.e., on a bad day with 5% probability, the loss exceeds 1.14%). CVaR(95%) = -0.0171. | {
"var": -0.011424,
"cvar": -0.017138999999999998,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20161012_0922 | T2 | 1 | train | sideways | all | [
"DBB"
] | 2016-10-12T00:00:00 | DBB: 60-day return history, mean=0.0001, std=0.0077. | Asset: DBB
Daily returns (past 60 days): mean=0.0001, std=0.0077, min=-0.0165, max=0.0204
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for DBB. Express as a decimal (e.g., -0.02). | -0.0138 | -0.013809 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0138 (i.e., on a bad day with 5% probability, the loss exceeds 1.38%). CVaR(95%) = -0.0150. | {
"var": -0.013809,
"cvar": -0.015021999999999999,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20181106_0924 | T2 | 1 | train | sideways | all | [
"XRP-USD"
] | 2018-11-06T00:00:00 | XRP-USD: 60-day return history, mean=0.0100, std=0.0727. | Asset: XRP-USD
Daily returns (past 60 days): mean=0.0100, std=0.0727, min=-0.1714, max=0.3257
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for XRP-USD. Express as a decimal (e.g., -0.02). | -0.0615 | -0.061463 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0615 (i.e., on a bad day with 5% probability, the loss exceeds 6.15%). CVaR(95%) = -0.1337. | {
"var": -0.061463,
"cvar": -0.133678,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20180330_0926 | T2 | 1 | train | sideways | all | [
"IAU"
] | 2018-03-30T00:00:00 | IAU: 60-day return history, mean=0.0002, std=0.0072. | Asset: IAU
Daily returns (past 60 days): mean=0.0002, std=0.0072, min=-0.0139, max=0.0175
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for IAU. Express as a decimal (e.g., -0.02). | -0.0125 | -0.012484 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0125 (i.e., on a bad day with 5% probability, the loss exceeds 1.25%). CVaR(95%) = -0.0136. | {
"var": -0.012483999999999999,
"cvar": -0.01365,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20160426_0929 | T2 | 1 | train | sideways | all | [
"FXI"
] | 2016-04-26T00:00:00 | FXI: 60-day return history, mean=0.0019, std=0.0169. | Asset: FXI
Daily returns (past 60 days): mean=0.0019, std=0.0169, min=-0.0259, max=0.0411
Market regime: sideways
Recent filing/news:
[Kaggle 2016-04-25] ["3 Things Not To Like About Sony Apple (AAPL) camera components supplier Sony Corp. (6758.Japan/SNE) tumbled 6.3% today after the electronics maker said it would pos... | -0.0229 | -0.02289 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0229 (i.e., on a bad day with 5% probability, the loss exceeds 2.29%). CVaR(95%) = -0.0248. | {
"var": -0.02289,
"cvar": -0.024789,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20211027_0931 | T2 | 1 | train | sideways | all | [
"BNB-USD"
] | 2021-10-27T00:00:00 | BNB-USD: 60-day return history, mean=0.0004, std=0.0448. | Asset: BNB-USD
Daily returns (past 60 days): mean=0.0004, std=0.0448, min=-0.1581, max=0.1050
Market regime: sideways
Recent filing/news:
[Kaggle 2021-10-19]
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for BNB-USD. Express as a decimal (e.g., -0.02). | -0.0601 | -0.060149 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0601 (i.e., on a bad day with 5% probability, the loss exceeds 6.01%). CVaR(95%) = -0.1154. | {
"var": -0.060148999999999994,
"cvar": -0.115413,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 20
} |
T2_all_20180517_0933 | T2 | 1 | train | sideways | all | [
"XLB"
] | 2018-05-17T00:00:00 | XLB: 60-day return history, mean=-0.0001, std=0.0123. | Asset: XLB
Daily returns (past 60 days): mean=-0.0001, std=0.0123, min=-0.0303, max=0.0204
Market regime: sideways
Recent filing/news:
[Kaggle 2018-05-16] ["Q1 13F Roundup: How Buffett, Einhorn, Ackman And Others Adjusted Their Portfolio", "Q1 13F Roundup: How Buffett, Einhorn, Ackman And Others Adjusted Their Portfoli... | -0.0221 | -0.022149 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0221 (i.e., on a bad day with 5% probability, the loss exceeds 2.21%). CVaR(95%) = -0.0275. | {
"var": -0.022149,
"cvar": -0.027460000000000002,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20180709_0935 | T2 | 1 | train | sideways | all | [
"ETH-USD"
] | 2018-07-09T00:00:00 | ETH-USD: 60-day return history, mean=-0.0061, std=0.0450. | Asset: ETH-USD
Daily returns (past 60 days): mean=-0.0061, std=0.0450, min=-0.1190, max=0.0956
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for ETH-USD. Express as a decimal (e.g., -0.02). | -0.0989 | -0.098899 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0989 (i.e., on a bad day with 5% probability, the loss exceeds 9.89%). CVaR(95%) = -0.1116. | {
"var": -0.098899,
"cvar": -0.11157099999999999,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20181205_0937 | T2 | 1 | train | sideways | all | [
"BIL"
] | 2018-12-05T00:00:00 | BIL: 60-day return history, mean=0.0001, std=0.0001. | Asset: BIL
Daily returns (past 60 days): mean=0.0001, std=0.0001, min=-0.0001, max=0.0003
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for BIL. Express as a decimal (e.g., -0.02). | -0.0001 | -0.000109 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0001 (i.e., on a bad day with 5% probability, the loss exceeds 0.01%). CVaR(95%) = -0.0001. | {
"var": -0.00010899999999999999,
"cvar": -0.00010899999999999999,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20210112_0940 | T2 | 1 | train | sideways | all | [
"TIP"
] | 2021-01-12T00:00:00 | TIP: 60-day return history, mean=0.0002, std=0.0006. | Asset: TIP
Daily returns (past 60 days): mean=0.0002, std=0.0006, min=-0.0013, max=0.0018
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for TIP. Express as a decimal (e.g., -0.02). | -0.0008 | -0.00077 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0008 (i.e., on a bad day with 5% probability, the loss exceeds 0.08%). CVaR(95%) = -0.0011. | {
"var": -0.0007700000000000001,
"cvar": -0.001063,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20190705_0942 | T2 | 1 | train | sideways | all | [
"TLT"
] | 2019-07-05T00:00:00 | TLT: 60-day return history, mean=0.0016, std=0.0055. | Asset: TLT
Daily returns (past 60 days): mean=0.0016, std=0.0055, min=-0.0114, max=0.0125
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for TLT. Express as a decimal (e.g., -0.02). | -0.0072 | -0.007238 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0072 (i.e., on a bad day with 5% probability, the loss exceeds 0.72%). CVaR(95%) = -0.0106. | {
"var": -0.007238,
"cvar": -0.0106,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20150507_0944 | T2 | 1 | train | sideways | all | [
"PALL"
] | 2015-05-07T00:00:00 | PALL: 60-day return history, mean=0.0007, std=0.0136. | Asset: PALL
Daily returns (past 60 days): mean=0.0007, std=0.0136, min=-0.0382, max=0.0290
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for PALL. Express as a decimal (e.g., -0.02). | -0.0217 | -0.021682 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0217 (i.e., on a bad day with 5% probability, the loss exceeds 2.17%). CVaR(95%) = -0.0296. | {
"var": -0.021682,
"cvar": -0.029604,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20180214_0946 | T2 | 1 | train | sideways | all | [
"VTI"
] | 2018-02-14T00:00:00 | VTI: 60-day return history, mean=0.0008, std=0.0088. | Asset: VTI
Daily returns (past 60 days): mean=0.0008, std=0.0088, min=-0.0335, max=0.0162
Market regime: sideways
Recent filing/news:
[Kaggle 2018-02-13] ["Google\u2019s new AMP mobile story telling models Snapchat and Instagram Format doesn\u2019t support advertising yet, which could slow its adoption among publishers... | -0.0110 | -0.011024 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0110 (i.e., on a bad day with 5% probability, the loss exceeds 1.10%). CVaR(95%) = -0.0295. | {
"var": -0.011023999999999999,
"cvar": -0.029469,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20191028_0948 | T2 | 1 | train | sideways | all | [
"VNQI"
] | 2019-10-28T00:00:00 | VNQI: 60-day return history, mean=0.0010, std=0.0071. | Asset: VNQI
Daily returns (past 60 days): mean=0.0010, std=0.0071, min=-0.0251, max=0.0199
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for VNQI. Express as a decimal (e.g., -0.02). | -0.0087 | -0.00869 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0087 (i.e., on a bad day with 5% probability, the loss exceeds 0.87%). CVaR(95%) = -0.0171. | {
"var": -0.008690000000000002,
"cvar": -0.017082,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20190204_0950 | T2 | 1 | train | sideways | all | [
"SHV"
] | 2019-02-04T00:00:00 | SHV: 60-day return history, mean=0.0001, std=0.0001. | Asset: SHV
Daily returns (past 60 days): mean=0.0001, std=0.0001, min=-0.0002, max=0.0005
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for SHV. Express as a decimal (e.g., -0.02). | -0.0001 | -0.000091 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0001 (i.e., on a bad day with 5% probability, the loss exceeds 0.01%). CVaR(95%) = -0.0002. | {
"var": -0.00009099999999999999,
"cvar": -0.00015099999999999998,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20210416_0953 | T2 | 1 | train | sideways | all | [
"BNO"
] | 2021-04-16T00:00:00 | BNO: 60-day return history, mean=0.0043, std=0.0229. | Asset: BNO
Daily returns (past 60 days): mean=0.0043, std=0.0229, min=-0.0619, max=0.0542
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for BNO. Express as a decimal (e.g., -0.02). | -0.0354 | -0.03539 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0354 (i.e., on a bad day with 5% probability, the loss exceeds 3.54%). CVaR(95%) = -0.0518. | {
"var": -0.035390000000000005,
"cvar": -0.051802,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20191216_0955 | T2 | 1 | train | sideways | all | [
"XLU"
] | 2019-12-16T00:00:00 | XLU: 60-day return history, mean=-0.0001, std=0.0063. | Asset: XLU
Daily returns (past 60 days): mean=-0.0001, std=0.0063, min=-0.0138, max=0.0147
Market regime: sideways
Recent filing/news:
[Kaggle 2019-12-13] ["5 Stocks To Watch For December 13, 2019", "A Peek Into The Markets: US Stock Futures Climb Ahead Of Economic Reports", "20 Stocks Moving in Friday's Pre-Market Ses... | -0.0129 | -0.012903 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0129 (i.e., on a bad day with 5% probability, the loss exceeds 1.29%). CVaR(95%) = -0.0136. | {
"var": -0.012903,
"cvar": -0.01361,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20210726_0957 | T2 | 1 | train | sideways | all | [
"SLV"
] | 2021-07-26T00:00:00 | SLV: 60-day return history, mean=0.0000, std=0.0143. | Asset: SLV
Daily returns (past 60 days): mean=0.0000, std=0.0143, min=-0.0452, max=0.0396
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for SLV. Express as a decimal (e.g., -0.02). | -0.0227 | -0.022716 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0227 (i.e., on a bad day with 5% probability, the loss exceeds 2.27%). CVaR(95%) = -0.0322. | {
"var": -0.022716,
"cvar": -0.032213,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20220524_0959 | T2 | 1 | train | sideways | all | [
"LQD"
] | 2022-05-24T00:00:00 | LQD: 60-day return history, mean=-0.0017, std=0.0073. | Asset: LQD
Daily returns (past 60 days): mean=-0.0017, std=0.0073, min=-0.0133, max=0.0111
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for LQD. Express as a decimal (e.g., -0.02). | -0.0133 | -0.013325 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0133 (i.e., on a bad day with 5% probability, the loss exceeds 1.33%). CVaR(95%) = -0.0133. | {
"var": -0.013325,
"cvar": -0.013325,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20180202_0961 | T2 | 1 | train | sideways | all | [
"USMV"
] | 2018-02-02T00:00:00 | USMV: 60-day return history, mean=0.0010, std=0.0038. | Asset: USMV
Daily returns (past 60 days): mean=0.0010, std=0.0038, min=-0.0076, max=0.0093
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 supplier Qualcomm Inc. reported that a large... | -0.0056 | -0.005648 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0056 (i.e., on a bad day with 5% probability, the loss exceeds 0.56%). CVaR(95%) = -0.0067. | {
"var": -0.005647999999999999,
"cvar": -0.0067469999999999995,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20170517_0963 | T2 | 1 | train | sideways | all | [
"XLU"
] | 2017-05-17T00:00:00 | XLU: 60-day return history, mean=0.0008, std=0.0063. | Asset: XLU
Daily returns (past 60 days): mean=0.0008, std=0.0063, min=-0.0145, max=0.0159
Market regime: sideways
Recent filing/news:
[Kaggle 2017-05-16] ["Tech Today: Buffett Buys AAPL, Salesforce On Tap, AMD\u2019s Prospects Warren Buffett bought more Apple stock, but others were loading up on Snapchat maker Snap, wh... | -0.0079 | -0.007852 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0079 (i.e., on a bad day with 5% probability, the loss exceeds 0.79%). CVaR(95%) = -0.0115. | {
"var": -0.007852,
"cvar": -0.011491999999999999,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20180706_0965 | T2 | 1 | train | sideways | all | [
"XLRE"
] | 2018-07-06T00:00:00 | XLRE: 60-day return history, mean=0.0014, std=0.0078. | Asset: XLRE
Daily returns (past 60 days): mean=0.0014, std=0.0078, min=-0.0226, max=0.0133
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 5: Trading The Range In The S&P 500; ... | -0.0120 | -0.012037 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0120 (i.e., on a bad day with 5% probability, the loss exceeds 1.20%). CVaR(95%) = -0.0189. | {
"var": -0.012036999999999999,
"cvar": -0.018859,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20221209_0967 | T2 | 1 | train | sideways | all | [
"CORN"
] | 2022-12-09T00:00:00 | CORN: 60-day return history, mean=-0.0006, std=0.0081. | Asset: CORN
Daily returns (past 60 days): mean=-0.0006, std=0.0081, min=-0.0186, max=0.0192
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for CORN. Express as a decimal (e.g., -0.02). | -0.0127 | -0.01266 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0127 (i.e., on a bad day with 5% probability, the loss exceeds 1.27%). CVaR(95%) = -0.0154. | {
"var": -0.012660000000000001,
"cvar": -0.015410000000000002,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20210128_0969 | T2 | 1 | train | sideways | all | [
"ICSH"
] | 2021-01-28T00:00:00 | ICSH: 60-day return history, mean=-0.0000, std=0.0002. | Asset: ICSH
Daily returns (past 60 days): mean=-0.0000, std=0.0002, min=-0.0004, max=0.0006
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for ICSH. Express as a decimal (e.g., -0.02). | -0.0004 | -0.000396 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0004 (i.e., on a bad day with 5% probability, the loss exceeds 0.04%). CVaR(95%) = -0.0004. | {
"var": -0.000396,
"cvar": -0.000396,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20210813_0971 | T2 | 1 | train | sideways | all | [
"IYR"
] | 2021-08-13T00:00:00 | IYR: 60-day return history, mean=0.0016, std=0.0081. | Asset: IYR
Daily returns (past 60 days): mean=0.0016, std=0.0081, min=-0.0175, max=0.0225
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for IYR. Express as a decimal (e.g., -0.02). | -0.0107 | -0.010682 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0107 (i.e., on a bad day with 5% probability, the loss exceeds 1.07%). CVaR(95%) = -0.0159. | {
"var": -0.010681999999999999,
"cvar": -0.015907,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20220715_0973 | T2 | 1 | train | sideways | all | [
"XRP-USD"
] | 2022-07-15T00:00:00 | XRP-USD: 60-day return history, mean=-0.0041, std=0.0391. | Asset: XRP-USD
Daily returns (past 60 days): mean=-0.0041, std=0.0391, min=-0.1004, max=0.0955
Market regime: sideways
Recent filing/news:
[Kaggle 2022-07-14]
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for XRP-USD. Express as a decimal (e.g., -0.02). | -0.0601 | -0.060113 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0601 (i.e., on a bad day with 5% probability, the loss exceeds 6.01%). CVaR(95%) = -0.0881. | {
"var": -0.060113,
"cvar": -0.08812,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 20
} |
T2_all_20190524_0975 | T2 | 1 | train | sideways | all | [
"BIL"
] | 2019-05-24T00:00:00 | BIL: 60-day return history, mean=0.0001, std=0.0001. | Asset: BIL
Daily returns (past 60 days): mean=0.0001, std=0.0001, min=-0.0001, max=0.0003
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for BIL. Express as a decimal (e.g., -0.02). | -0.0001 | -0.00011 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0001 (i.e., on a bad day with 5% probability, the loss exceeds 0.01%). CVaR(95%) = -0.0001. | {
"var": -0.00011,
"cvar": -0.00011,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20180702_0977 | T2 | 1 | train | sideways | all | [
"REZ"
] | 2018-07-02T00:00:00 | REZ: 60-day return history, mean=0.0016, std=0.0085. | Asset: REZ
Daily returns (past 60 days): mean=0.0016, std=0.0085, min=-0.0192, max=0.0256
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for REZ. Express as a decimal (e.g., -0.02). | -0.0161 | -0.016139 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0161 (i.e., on a bad day with 5% probability, the loss exceeds 1.61%). CVaR(95%) = -0.0177. | {
"var": -0.016139,
"cvar": -0.017744,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20151013_0979 | T2 | 1 | train | sideways | all | [
"QUAL"
] | 2015-10-13T00:00:00 | QUAL: 60-day return history, mean=-0.0008, std=0.0126. | Asset: QUAL
Daily returns (past 60 days): mean=-0.0008, std=0.0126, min=-0.0339, max=0.0266
Market regime: sideways
Recent filing/news:
[Kaggle 2015-10-12] Ameren Corp. (AEE) Raises Fourth Quarter Dividend by 3.7% Ameren CorporationAEE announced a 3.7% hike in its quarterly cash dividend, bringing the annualized payout... | -0.0225 | -0.022481 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0225 (i.e., on a bad day with 5% probability, the loss exceeds 2.25%). CVaR(95%) = -0.0318. | {
"var": -0.022480999999999998,
"cvar": -0.031820999999999995,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20190717_0981 | T2 | 1 | train | sideways | all | [
"VLUE"
] | 2019-07-17T00:00:00 | VLUE: 60-day return history, mean=0.0001, std=0.0089. | Asset: VLUE
Daily returns (past 60 days): mean=0.0001, std=0.0089, min=-0.0282, max=0.0288
Market regime: sideways
Recent filing/news:
[Kaggle 2019-07-16] ["Notable Tuesday Option Activity: UVE, AMD, WDC Among the underlying components of the Russell 3000 index, we saw noteworthy options trading volume today in Univers... | -0.0137 | -0.013701 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0137 (i.e., on a bad day with 5% probability, the loss exceeds 1.37%). CVaR(95%) = -0.0215. | {
"var": -0.013701,
"cvar": -0.021547999999999998,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": true,
"text_chars": 3020
} |
T2_all_20220912_0983 | T2 | 1 | train | sideways | all | [
"EMB"
] | 2022-09-12T00:00:00 | EMB: 60-day return history, mean=0.0006, std=0.0092. | Asset: EMB
Daily returns (past 60 days): mean=0.0006, std=0.0092, min=-0.0155, max=0.0165
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for EMB. Express as a decimal (e.g., -0.02). | -0.0152 | -0.015218 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0152 (i.e., on a bad day with 5% probability, the loss exceeds 1.52%). CVaR(95%) = -0.0155. | {
"var": -0.015217999999999999,
"cvar": -0.015501,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20180913_0985 | T2 | 1 | train | sideways | all | [
"HYG"
] | 2018-09-13T00:00:00 | HYG: 60-day return history, mean=0.0002, std=0.0014. | Asset: HYG
Daily returns (past 60 days): mean=0.0002, std=0.0014, min=-0.0028, max=0.0038
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for HYG. Express as a decimal (e.g., -0.02). | -0.0023 | -0.002329 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0023 (i.e., on a bad day with 5% probability, the loss exceeds 0.23%). CVaR(95%) = -0.0025. | {
"var": -0.002329,
"cvar": -0.002499,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20181010_0987 | T2 | 1 | train | sideways | all | [
"SOYB"
] | 2018-10-10T00:00:00 | SOYB: 60-day return history, mean=0.0011, std=0.0112. | Asset: SOYB
Daily returns (past 60 days): mean=0.0011, std=0.0112, min=-0.0265, max=0.0280
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for SOYB. Express as a decimal (e.g., -0.02). | -0.0148 | -0.014763 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0148 (i.e., on a bad day with 5% probability, the loss exceeds 1.48%). CVaR(95%) = -0.0213. | {
"var": -0.014763,
"cvar": -0.021342999999999997,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20211012_0990 | T2 | 1 | train | sideways | all | [
"CORN"
] | 2021-10-12T00:00:00 | CORN: 60-day return history, mean=-0.0006, std=0.0117. | Asset: CORN
Daily returns (past 60 days): mean=-0.0006, std=0.0117, min=-0.0268, max=0.0253
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for CORN. Express as a decimal (e.g., -0.02). | -0.0178 | -0.01784 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0178 (i.e., on a bad day with 5% probability, the loss exceeds 1.78%). CVaR(95%) = -0.0235. | {
"var": -0.01784,
"cvar": -0.023499,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20210118_0992 | T2 | 1 | train | sideways | all | [
"XHB"
] | 2021-01-18T00:00:00 | XHB: 60-day return history, mean=0.0015, std=0.0153. | Asset: XHB
Daily returns (past 60 days): mean=0.0015, std=0.0153, min=-0.0411, max=0.0350
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for XHB. Express as a decimal (e.g., -0.02). | -0.0188 | -0.018786 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0188 (i.e., on a bad day with 5% probability, the loss exceeds 1.88%). CVaR(95%) = -0.0310. | {
"var": -0.018786,
"cvar": -0.031010000000000003,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20210223_0994 | T2 | 1 | train | sideways | all | [
"MORT"
] | 2021-02-23T00:00:00 | MORT: 60-day return history, mean=0.0021, std=0.0142. | Asset: MORT
Daily returns (past 60 days): mean=0.0021, std=0.0142, min=-0.0297, max=0.0315
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for MORT. Express as a decimal (e.g., -0.02). | -0.0245 | -0.024535 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0245 (i.e., on a bad day with 5% probability, the loss exceeds 2.45%). CVaR(95%) = -0.0293. | {
"var": -0.024534999999999998,
"cvar": -0.029317,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20170203_0996 | T2 | 1 | train | sideways | all | [
"IYR"
] | 2017-02-03T00:00:00 | IYR: 60-day return history, mean=0.0009, std=0.0093. | Asset: IYR
Daily returns (past 60 days): mean=0.0009, std=0.0093, min=-0.0185, max=0.0195
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for IYR. Express as a decimal (e.g., -0.02). | -0.0159 | -0.015886 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0159 (i.e., on a bad day with 5% probability, the loss exceeds 1.59%). CVaR(95%) = -0.0184. | {
"var": -0.015886,
"cvar": -0.018404,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T2_all_20201026_0999 | T2 | 1 | train | sideways | all | [
"MATIC-USD"
] | 2020-10-26T00:00:00 | MATIC-USD: 60-day return history, mean=-0.0062, std=0.0490. | Asset: MATIC-USD
Daily returns (past 60 days): mean=-0.0062, std=0.0490, min=-0.1751, max=0.1042
Market regime: sideways
Using the historical simulation method, compute the 1-day VaR at 95% confidence level for MATIC-USD. Express as a decimal (e.g., -0.02). | -0.0894 | -0.089439 | Historical simulation VaR at 95%: sort the 60 daily returns and take the 5th percentile. VaR(95%) = -0.0894 (i.e., on a bad day with 5% probability, the loss exceeds 8.94%). CVaR(95%) = -0.1370. | {
"var": -0.08943899999999999,
"cvar": -0.136987,
"confidence": 0.9500000000000001,
"n_returns": 60,
"has_text": false,
"text_chars": 0
} |
T3_all_20171211_0000 | T3 | 1 | train | sideways | all | [
"VCIT"
] | 2017-12-11T00:00:00 | VCIT: 60-day history, VaR(99%)=-0.0034, max drawdown threshold=10%. | Asset: VCIT
Daily returns (past 60 days): mean=0.0001, std=0.0015, worst_day=-0.0043
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to VCIT, 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 = 0.0034 (i.e., a 0.34% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0034 = 28.9860, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.0034500000000000004,
"expected_loss": 0.0034500000000000004,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20160523_0003 | T3 | 1 | train | sideways | all | [
"XLE"
] | 2016-05-23T00:00:00 | XLE: 60-day history, VaR(99%)=-0.0317, max drawdown threshold=10%. | Asset: XLE
Daily returns (past 60 days): mean=0.0026, std=0.0142, worst_day=-0.0429
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-05-20] ["Apple\u2019s iPhone 7 Will Likely Have Dual Cameras: Who Will Benefit? Citi Research believes that all new 5.5\" Apple (AAPL) i... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0317 (i.e., a 3.17% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0317 = 3.1577, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.031668999999999996,
"expected_loss": 0.031668999999999996,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180613_0006 | T3 | 1 | train | sideways | all | [
"ITB"
] | 2018-06-13T00:00:00 | ITB: 60-day history, VaR(99%)=-0.0357, max drawdown threshold=10%. | Asset: ITB
Daily returns (past 60 days): mean=0.0005, std=0.0154, worst_day=-0.0379
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.g... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0357 (i.e., a 3.57% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0357 = 2.7990, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.035727,
"expected_loss": 0.035727,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20210326_0011 | T3 | 1 | train | sideways | all | [
"XHB"
] | 2021-03-26T00:00:00 | XHB: 60-day history, VaR(99%)=-0.0313, max drawdown threshold=10%. | Asset: XHB
Daily returns (past 60 days): mean=0.0028, std=0.0157, worst_day=-0.0404
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 = 0.0313 (i.e., a 3.13% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0313 = 3.1986, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.031264,
"expected_loss": 0.031264,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20210520_0014 | T3 | 1 | train | sideways | all | [
"QUAL"
] | 2021-05-20T00:00:00 | QUAL: 60-day history, VaR(99%)=-0.0233, max drawdown threshold=10%. | Asset: QUAL
Daily returns (past 60 days): mean=0.0011, std=0.0098, worst_day=-0.0247
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2021-05-19] ["Analog Devices Guides Q3 In Line With Estimates, Declares Dividend - Quick Facts (RTTNews) - While reporting financial results... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0233 (i.e., a 2.33% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0233 = 4.2898, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.023311,
"expected_loss": 0.023311,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20220127_0017 | T3 | 1 | train | sideways | all | [
"UNG"
] | 2022-01-27T00:00:00 | UNG: 60-day history, VaR(99%)=-0.0858, max drawdown threshold=10%. | Asset: UNG
Daily returns (past 60 days): mean=-0.0039, std=0.0414, worst_day=-0.0858
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to UNG, 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 = 0.0858 (i.e., a 8.58% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0858 = 1.1659, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.085769,
"expected_loss": 0.085769,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20210218_0020 | T3 | 1 | train | sideways | all | [
"ADA-USD"
] | 2021-02-18T00:00:00 | ADA-USD: 60-day history, VaR(99%)=-0.1459, max drawdown threshold=10%. | Asset: ADA-USD
Daily returns (past 60 days): mean=0.0298, std=0.0861, worst_day=-0.1736
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... | 0.6852 | 0.6852 | Step 1: Compute |VaR(99%)| from historical returns = 0.1459 (i.e., a 14.59% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1459 = 0.6852, capped at 1.0.
Maximum position size = 0.6852 (68.5% of portfolio). | {
"var_99": -0.14593599999999998,
"expected_loss": 0.14593599999999998,
"max_drawdown_threshold": 0.1,
"position_size": 0.6852,
"has_text": false,
"text_chars": 0
} |
T3_all_20200715_0023 | T3 | 1 | train | sideways | all | [
"XLU"
] | 2020-07-15T00:00:00 | XLU: 60-day history, VaR(99%)=-0.0339, max drawdown threshold=10%. | Asset: XLU
Daily returns (past 60 days): mean=-0.0006, std=0.0157, worst_day=-0.0339
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-07-14] ["S&P 500, Dow rise after mixed bank earnings; tech-heavy Nasdaq falls By Medha Singh and Devik Jain July 14 (Reuters) - The S&P... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0339 (i.e., a 3.39% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0339 = 2.9516, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.03388,
"expected_loss": 0.03388,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20200624_0025 | T3 | 1 | train | sideways | all | [
"VLUE"
] | 2020-06-24T00:00:00 | VLUE: 60-day history, VaR(99%)=-0.0356, max drawdown threshold=10%. | Asset: VLUE
Daily returns (past 60 days): mean=0.0021, std=0.0213, worst_day=-0.0356
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-06-23] ["9 Things Apple Announced at WWDC\u2014and 5 Things It Didn\u2019t iOS14, Car Play, and Apple Maps all got great upgrades, but ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0356 (i.e., a 3.56% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0356 = 2.8129, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.035551,
"expected_loss": 0.035551,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20170817_0028 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2017-08-17T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.0812, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=0.0069, std=0.0455, 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... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0812 (i.e., a 8.12% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0812 = 1.2308, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.081249,
"expected_loss": 0.081249,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20200901_0031 | T3 | 1 | train | sideways | all | [
"XLY"
] | 2020-09-01T00:00:00 | XLY: 60-day history, VaR(99%)=-0.0299, max drawdown threshold=10%. | Asset: XLY
Daily returns (past 60 days): mean=0.0025, std=0.0114, worst_day=-0.0388
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-08-31] ["Apple's stock rallies as 4-for-1 split set to take effect Shares of Apple Inc. rallied 1.4% in premarket trading Monday, paring... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0299 (i.e., a 2.99% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0299 = 3.3437, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.029907,
"expected_loss": 0.029907,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20171123_0034 | T3 | 1 | train | sideways | all | [
"XLV"
] | 2017-11-23T00:00:00 | XLV: 60-day history, VaR(99%)=-0.0103, max drawdown threshold=10%. | Asset: XLV
Daily returns (past 60 days): mean=0.0005, std=0.0055, worst_day=-0.0108
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2017-11-22] ["Apple Could Offer \u2018iPhone 8s\u2019 With Large, non-OLED Screen, Says Rosenblatt Apple may offer an \"iPhone 8s\" next year... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0103 (i.e., a 1.03% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0103 = 9.6652, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.010346,
"expected_loss": 0.010346,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180727_0037 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2018-07-27T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.0985, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=-0.0043, std=0.0387, worst_day=-0.1086
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 ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0985 (i.e., a 9.85% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0985 = 1.0156, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.09846,
"expected_loss": 0.09846,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20191016_0042 | T3 | 1 | train | sideways | all | [
"EWJ"
] | 2019-10-16T00:00:00 | EWJ: 60-day history, VaR(99%)=-0.0248, max drawdown threshold=10%. | Asset: EWJ
Daily returns (past 60 days): mean=0.0009, std=0.0085, worst_day=-0.0251
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-10-15] 2 Cheap Dividend Stocks You Can Buy Right Now The technology sector has become a great place to look for dividend stocks, as the ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0248 (i.e., a 2.48% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0248 = 4.0387, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.02476,
"expected_loss": 0.02476,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20160802_0045 | T3 | 1 | train | sideways | all | [
"QUAL"
] | 2016-08-02T00:00:00 | QUAL: 60-day history, VaR(99%)=-0.0230, max drawdown threshold=10%. | Asset: QUAL
Daily returns (past 60 days): mean=0.0008, std=0.0080, worst_day=-0.0339
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-08-01] ["Tech Turbocharged By Earnings, Chart Breakouts Last week\u2019s killer earnings from Facebook, Alphabet and Apple paced an alr... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0230 (i.e., a 2.30% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0230 = 4.3464, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.023008,
"expected_loss": 0.023008,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20171107_0047 | T3 | 1 | train | sideways | all | [
"REZ"
] | 2017-11-07T00:00:00 | REZ: 60-day history, VaR(99%)=-0.0138, max drawdown threshold=10%. | Asset: REZ
Daily returns (past 60 days): mean=0.0003, std=0.0060, worst_day=-0.0169
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to REZ, 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 = 0.0138 (i.e., a 1.38% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0138 = 7.2646, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.013765,
"expected_loss": 0.013765,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20211130_0050 | T3 | 1 | train | sideways | all | [
"XLF"
] | 2021-11-30T00:00:00 | XLF: 60-day history, VaR(99%)=-0.0277, max drawdown threshold=10%. | Asset: XLF
Daily returns (past 60 days): mean=0.0003, std=0.0110, worst_day=-0.0337
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2021-11-29] ["Is Autodesk Stock a Buy? Autodesk's (NASDAQ: ADSK) stock plunged 15% on Nov. 24 after the design software maker posted its thir... | 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 = 3.6131, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.027677,
"expected_loss": 0.027677,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20221006_0053 | T3 | 1 | train | sideways | all | [
"ACWI"
] | 2022-10-06T00:00:00 | ACWI: 60-day history, VaR(99%)=-0.0305, max drawdown threshold=10%. | Asset: ACWI
Daily returns (past 60 days): mean=-0.0003, std=0.0131, worst_day=-0.0309
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-10-05] ["After Hours Most Active for Oct 5, 2022 : DKNG, BTRS, X, AAPL, TQQQ, FDX, QQQ, SHY, STOR, C, TMX, T The NASDAQ 100 After Hour... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0305 (i.e., a 3.05% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0305 = 3.2746, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.030538,
"expected_loss": 0.030538,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20210503_0056 | T3 | 1 | train | sideways | all | [
"MORT"
] | 2021-05-03T00:00:00 | MORT: 60-day history, VaR(99%)=-0.0305, max drawdown threshold=10%. | Asset: MORT
Daily returns (past 60 days): mean=0.0026, std=0.0134, worst_day=-0.0363
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to MORT, 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 = 0.0305 (i.e., a 3.05% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0305 = 3.2783, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.030504,
"expected_loss": 0.030504,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20180129_0059 | T3 | 1 | train | sideways | all | [
"VLUE"
] | 2018-01-29T00:00:00 | VLUE: 60-day history, VaR(99%)=-0.0064, max drawdown threshold=10%. | Asset: VLUE
Daily returns (past 60 days): mean=0.0021, std=0.0053, worst_day=-0.0069
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-01-26] ["Qualcomm Fined by EC, Signs MoU With 4 Handset Developers", "Semiconductors finish week strong on Intel earnings", "Semiconduc... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0064 (i.e., a 0.64% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0064 = 15.6269, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.006398999999999999,
"expected_loss": 0.006398999999999999,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20181008_0062 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2018-10-08T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.1380, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=0.0085, std=0.0810, worst_day=-0.1449
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... | 0.7249 | 0.7249 | Step 1: Compute |VaR(99%)| from historical returns = 0.1380 (i.e., a 13.80% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1380 = 0.7249, capped at 1.0.
Maximum position size = 0.7249 (72.5% of portfolio). | {
"var_99": -0.13795000000000002,
"expected_loss": 0.13795000000000002,
"max_drawdown_threshold": 0.1,
"position_size": 0.7249,
"has_text": false,
"text_chars": 0
} |
T3_all_20211029_0065 | T3 | 1 | train | sideways | all | [
"BNB-USD"
] | 2021-10-29T00:00:00 | BNB-USD: 60-day history, VaR(99%)=-0.1320, max drawdown threshold=10%. | Asset: BNB-USD
Daily returns (past 60 days): mean=0.0015, std=0.0471, worst_day=-0.1581
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2021-10-27]
Determine the maximum fraction of total portfolio capital that should be allocated to BNB-USD, given the drawdown constrain... | 0.7576 | 0.7576 | Step 1: Compute |VaR(99%)| from historical returns = 0.1320 (i.e., a 13.20% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1320 = 0.7576, capped at 1.0.
Maximum position size = 0.7576 (75.8% of portfolio). | {
"var_99": -0.13200299999999998,
"expected_loss": 0.13200299999999998,
"max_drawdown_threshold": 0.1,
"position_size": 0.7576,
"has_text": true,
"text_chars": 20
} |
T3_all_20211008_0068 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2021-10-08T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.1005, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=0.0042, std=0.0385, worst_day=-0.1106
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... | 0.9951 | 0.9951 | Step 1: Compute |VaR(99%)| from historical returns = 0.1005 (i.e., a 10.05% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1005 = 0.9951, capped at 1.0.
Maximum position size = 0.9951 (99.5% of portfolio). | {
"var_99": -0.10049000000000001,
"expected_loss": 0.10049000000000001,
"max_drawdown_threshold": 0.1,
"position_size": 0.9951000000000001,
"has_text": false,
"text_chars": 0
} |
T3_all_20160804_0071 | T3 | 1 | train | sideways | all | [
"BNDX"
] | 2016-08-04T00:00:00 | BNDX: 60-day history, VaR(99%)=-0.0040, max drawdown threshold=10%. | Asset: BNDX
Daily returns (past 60 days): mean=0.0004, std=0.0022, worst_day=-0.0041
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to BNDX, 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 = 0.0040 (i.e., a 0.40% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0040 = 25.0127, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.003998,
"expected_loss": 0.003998,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20220915_0076 | T3 | 1 | train | sideways | all | [
"MATIC-USD"
] | 2022-09-15T00:00:00 | MATIC-USD: 60-day history, VaR(99%)=-0.1092, max drawdown threshold=10%. | Asset: MATIC-USD
Daily returns (past 60 days): mean=0.0046, std=0.0598, worst_day=-0.1198
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2022-09-14]
Determine the maximum fraction of total portfolio capital that should be allocated to MATIC-USD, given the drawdown const... | 0.9160 | 0.916 | Step 1: Compute |VaR(99%)| from historical returns = 0.1092 (i.e., a 10.92% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1092 = 0.9160, capped at 1.0.
Maximum position size = 0.9160 (91.6% of portfolio). | {
"var_99": -0.109165,
"expected_loss": 0.109165,
"max_drawdown_threshold": 0.1,
"position_size": 0.916,
"has_text": true,
"text_chars": 20
} |
T3_all_20200106_0079 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2020-01-06T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.0801, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=-0.0073, std=0.0271, worst_day=-0.1135
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 ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0801 (i.e., a 8.01% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0801 = 1.2478, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.080139,
"expected_loss": 0.080139,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20161101_0082 | T3 | 1 | train | sideways | all | [
"EEM"
] | 2016-11-01T00:00:00 | EEM: 60-day history, VaR(99%)=-0.0290, max drawdown threshold=10%. | Asset: EEM
Daily returns (past 60 days): mean=0.0002, std=0.0114, worst_day=-0.0341
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-10-31] ["Music industry still plagued by pirated CDs Even in the digital era there are plenty of music fans who still buy old-fashioned ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0290 (i.e., a 2.90% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0290 = 3.4456, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.029023,
"expected_loss": 0.029023,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20190619_0085 | T3 | 1 | train | sideways | all | [
"BTC-USD"
] | 2019-06-19T00:00:00 | BTC-USD: 60-day history, VaR(99%)=-0.0642, max drawdown threshold=10%. | Asset: BTC-USD
Daily returns (past 60 days): mean=0.0092, std=0.0378, worst_day=-0.0686
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 = 0.0642 (i.e., a 6.42% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0642 = 1.5585, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.064165,
"expected_loss": 0.064165,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20190729_0090 | T3 | 1 | train | sideways | all | [
"EWJ"
] | 2019-07-29T00:00:00 | EWJ: 60-day history, VaR(99%)=-0.0202, max drawdown threshold=10%. | Asset: EWJ
Daily returns (past 60 days): mean=0.0000, std=0.0079, worst_day=-0.0228
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-07-26] ["SoftBank launches another tech megafund, backed by Apple, Microsoft Second Vision Fund, with about $108 billion secured, will i... | 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_20160303_0093 | T3 | 1 | train | sideways | all | [
"^VIX"
] | 2016-03-03T00:00:00 | ^VIX: 60-day history, VaR(99%)=-0.1798, max drawdown threshold=10%. | Asset: ^VIX
Daily returns (past 60 days): mean=-0.0007, std=0.0903, worst_day=-0.1825
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2016-03-02] ["Why Are Stocks Rising? Bond Market Has Answer Equities jumped 9% in two weeks, thanks to credit bounce. But debt can backfire... | 0.5562 | 0.5562 | Step 1: Compute |VaR(99%)| from historical returns = 0.1798 (i.e., a 17.98% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1798 = 0.5562, capped at 1.0.
Maximum position size = 0.5562 (55.6% of portfolio). | {
"var_99": -0.17978000000000002,
"expected_loss": 0.17978000000000002,
"max_drawdown_threshold": 0.1,
"position_size": 0.5562,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180703_0104 | T3 | 1 | train | sideways | all | [
"VTI"
] | 2018-07-03T00:00:00 | VTI: 60-day history, VaR(99%)=-0.0129, max drawdown threshold=10%. | Asset: VTI
Daily returns (past 60 days): mean=0.0009, std=0.0064, worst_day=-0.0141
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-07-02] ["Why there may never be a Netflix of videogames Years into parallel efforts to deliver videogame streams from the cloud, latency... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0129 (i.e., a 1.29% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0129 = 7.7385, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.012922,
"expected_loss": 0.012922,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20180607_0111 | T3 | 1 | train | sideways | all | [
"TLH"
] | 2018-06-07T00:00:00 | TLH: 60-day history, VaR(99%)=-0.0065, max drawdown threshold=10%. | Asset: TLH
Daily returns (past 60 days): mean=0.0000, std=0.0040, worst_day=-0.0078
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Determine the maximum fraction of total portfolio capital that should be allocated to TLH, 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 = 0.0065 (i.e., a 0.65% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0065 = 15.4273, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.006482,
"expected_loss": 0.006482,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20171219_0114 | T3 | 1 | train | sideways | all | [
"LINK-USD"
] | 2017-12-19T00:00:00 | LINK-USD: 39-day history, VaR(99%)=-0.1712, max drawdown threshold=10%. | Asset: LINK-USD
Daily returns (past 39 days): mean=0.0168, std=0.0964, worst_day=-0.1767
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... | 0.5840 | 0.584 | Step 1: Compute |VaR(99%)| from historical returns = 0.1712 (i.e., a 17.12% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1712 = 0.5840, capped at 1.0.
Maximum position size = 0.5840 (58.4% of portfolio). | {
"var_99": -0.171239,
"expected_loss": 0.171239,
"max_drawdown_threshold": 0.1,
"position_size": 0.584,
"has_text": false,
"text_chars": 0
} |
T3_all_20170414_0117 | T3 | 1 | train | sideways | all | [
"VTI"
] | 2017-04-14T00:00:00 | VTI: 60-day history, VaR(99%)=-0.0098, max drawdown threshold=10%. | Asset: VTI
Daily returns (past 60 days): mean=0.0004, std=0.0045, worst_day=-0.0141
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2017-04-13] ["CEO average pay climbed more than $1 million in 2016 Warren Buffett was the lowest-earning CEO on the list, though his company ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0098 (i.e., a 0.98% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0098 = 10.1968, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.009807,
"expected_loss": 0.009807,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20191126_0122 | T3 | 1 | train | sideways | all | [
"^VIX"
] | 2019-11-26T00:00:00 | ^VIX: 60-day history, VaR(99%)=-0.1226, max drawdown threshold=10%. | Asset: ^VIX
Daily returns (past 60 days): mean=-0.0078, std=0.0615, worst_day=-0.1261
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-11-25] ["Noteworthy ETF Outflows: TQQQ, CMCSA, CSCO, ADBE Looking today at week-over-week shares outstanding changes among the univers... | 0.8154 | 0.8154 | Step 1: Compute |VaR(99%)| from historical returns = 0.1226 (i.e., a 12.26% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1226 = 0.8154, capped at 1.0.
Maximum position size = 0.8154 (81.5% of portfolio). | {
"var_99": -0.122641,
"expected_loss": 0.122641,
"max_drawdown_threshold": 0.1,
"position_size": 0.8154,
"has_text": true,
"text_chars": 3020
} |
T3_all_20181121_0132 | T3 | 1 | train | sideways | all | [
"XLK"
] | 2018-11-21T00:00:00 | XLK: 60-day history, VaR(99%)=-0.0427, max drawdown threshold=10%. | Asset: XLK
Daily returns (past 60 days): mean=-0.0024, std=0.0165, worst_day=-0.0427
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2018-11-20] ["Asian stocks drop as tech pullback, Nissan CEO\u2019s arrest take toll Auto, electronics sectors weigh on Nikkei; tech stocks ... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0427 (i.e., a 4.27% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0427 = 2.3410, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.042717,
"expected_loss": 0.042717,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20201021_0135 | T3 | 1 | train | sideways | all | [
"ADA-USD"
] | 2020-10-21T00:00:00 | ADA-USD: 60-day history, VaR(99%)=-0.1411, max drawdown threshold=10%. | Asset: ADA-USD
Daily returns (past 60 days): mean=-0.0018, std=0.0516, 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_20200911_0138 | T3 | 1 | train | sideways | all | [
"MTUM"
] | 2020-09-11T00:00:00 | MTUM: 60-day history, VaR(99%)=-0.0383, max drawdown threshold=10%. | Asset: MTUM
Daily returns (past 60 days): mean=0.0020, std=0.0141, worst_day=-0.0383
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2020-09-10] ["$718 mln options unwind signals more caution on tech stocks By April Joyner NEW YORK, Sept 10 (Reuters) - A large options play... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0383 (i.e., a 3.83% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0383 = 2.6134, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.038264,
"expected_loss": 0.038264,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20211022_0141 | T3 | 1 | train | sideways | all | [
"VTI"
] | 2021-10-22T00:00:00 | VTI: 60-day history, VaR(99%)=-0.0190, max drawdown threshold=10%. | Asset: VTI
Daily returns (past 60 days): mean=0.0006, std=0.0074, worst_day=-0.0212
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2021-10-21] ["Customer engagement platform Batch raises $23 million after years of bootstrapping If you\u2019ve been working in the French te... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0190 (i.e., a 1.90% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0190 = 5.2642, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.018996,
"expected_loss": 0.018996,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": true,
"text_chars": 3020
} |
T3_all_20201110_0146 | T3 | 1 | train | sideways | all | [
"XRP-USD"
] | 2020-11-10T00:00:00 | XRP-USD: 60-day history, VaR(99%)=-0.0551, max drawdown threshold=10%. | Asset: XRP-USD
Daily returns (past 60 days): mean=0.0007, std=0.0226, worst_day=-0.0592
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... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0551 (i.e., a 5.51% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0551 = 1.8150, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.055096,
"expected_loss": 0.055096,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20201229_0149 | T3 | 1 | train | sideways | all | [
"ETH-USD"
] | 2020-12-29T00:00:00 | ETH-USD: 60-day history, VaR(99%)=-0.0848, max drawdown threshold=10%. | Asset: ETH-USD
Daily returns (past 60 days): mean=0.0116, std=0.0437, worst_day=-0.0909
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... | 1.0000 | 1 | Step 1: Compute |VaR(99%)| from historical returns = 0.0848 (i.e., a 8.48% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.0848 = 1.1792, capped at 1.0.
Maximum position size = 1.0000 (100.0% of portfolio). | {
"var_99": -0.08480299999999999,
"expected_loss": 0.08480299999999999,
"max_drawdown_threshold": 0.1,
"position_size": 1,
"has_text": false,
"text_chars": 0
} |
T3_all_20201117_0153 | T3 | 1 | train | sideways | all | [
"MATIC-USD"
] | 2020-11-17T00:00:00 | MATIC-USD: 60-day history, VaR(99%)=-0.1145, max drawdown threshold=10%. | Asset: MATIC-USD
Daily returns (past 60 days): mean=-0.0021, std=0.0521, worst_day=-0.1372
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 ... | 0.8737 | 0.8737 | Step 1: Compute |VaR(99%)| from historical returns = 0.1145 (i.e., a 11.45% loss in the worst 1% of days).
Step 2: Fixed-fractional formula: f* = 10% / 0.1145 = 0.8737, capped at 1.0.
Maximum position size = 0.8737 (87.4% of portfolio). | {
"var_99": -0.114452,
"expected_loss": 0.114452,
"max_drawdown_threshold": 0.1,
"position_size": 0.8737,
"has_text": false,
"text_chars": 0
} |
T3_all_20150916_0156 | T3 | 1 | train | sideways | all | [
"^VIX"
] | 2015-09-16T00:00:00 | ^VIX: 60-day history, VaR(99%)=-0.1825, max drawdown threshold=10%. | Asset: ^VIX
Daily returns (past 60 days): mean=0.0032, std=0.1055, worst_day=-0.1825
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2015-09-15] ["Chip Stocks Up Despite Falling Semiconductor Billings", "Chip Stocks Up Despite Falling Semiconductor Billings", "Is Apple, In... | 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_20190819_0164 | T3 | 1 | train | sideways | all | [
"XLU"
] | 2019-08-19T00:00:00 | XLU: 60-day history, VaR(99%)=-0.0188, max drawdown threshold=10%. | Asset: XLU
Daily returns (past 60 days): mean=0.0007, std=0.0081, worst_day=-0.0220
Maximum acceptable portfolio drawdown: 10%
Market regime: sideways
Recent filing/news:
[Kaggle 2019-08-16] ["7 Stocks To Watch For August 16, 2019", "KeyBanc Maintains Overweight on Applied Materials, Lowers Price Target to $54", "A Pee... | 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
} |
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