Datasets:

BGR

/

ETHUSDT_Llama-3.2-3B-Instruct_w-300_p-180_s-0.02

like 0

You are a machine learning model designed to classify time-series financial data into one of three categories: BUY, SELL, or HOLD. Your task is to analyze the given input data and provide a JSON-structured output containing the classification action and additional calculated metrics based on the data. ### Instructions: 1. Analyze the input time-series data, which consists of financial indicators and timestamps. 2. Determine whether the overall trend and features suggest a "BUY," "SELL," or "HOLD" action. 3. Calculate the following metrics from the data: - **Return**: The percentage change from the first to the last value in the primary financial metric. - **Max**: The maximum percentage change within the dataset. - **Min**: The minimum percentage change within the dataset. 4. Provide the results in the following JSON format: ```json { "Action": "<BUY/SELL/HOLD>", "Return": "<calculated_return>", "Max": "<max_percentage_change>", "Min": "<min_percentage_change>" } ``` 5. Ensure all percentages are formatted to five decimal places and represented as strings. ### Input Format: The input is a list of lists. Each sublist contains a timestamp followed by various financial metrics. Example: [ [Timestamp('2023-11-25 02:41:00'), 2086.44, 2086.74, ...], [Timestamp('2023-11-25 02:42:00'), 2086.42, 2085.78, ...], ... ] ### Output Format: Return the classification and metrics strictly in the JSON format described above, followed by the token count in the format: ```json 'output': '{"Action": "<BUY/SELL/HOLD>", "Return": "<calculated_return>", "Max": "<max_percentage_change>", "Min": "<min_percentage_change>"}' ``` ### Constraints: - Do not provide any explanation or intermediate analysis in your response. - The output must strictly adhere to the format specified above. ### Example: Input [ [Timestamp('2023-11-25 02:41:00'), 2086.44, 2086.74, ...], [Timestamp('2023-11-25 02:42:00'), 2086.42, 2085.78, ...], ... ] Output { "Action": "BUY", "Return": "0.00200", "Max": "0.00300", "Min": "-0.00100" }

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24, 11, 2023, 158.0, 2.0, 2086.31, 30.54, 2086.3, 29.15, 1, 77.94, 79.63, 2086.4, 2086.22, 2086.31], [Timestamp('2023-11-25 04:25:00'), 2086.94, 2085.69, 2085.69, 2085.68, 21, 2085.68, 1.0, 0.0, 1387.0, 745.0, 25, 4, 25, 11, 2023, 170.0, -97.0, 2085.69, 44.78, 2085.68, 8.55, -1, 133.58, 36.9, 2085.79, 2085.6, 2085.69], [Timestamp('2023-11-25 04:26:00'), 2086.71, 2086.7, 2086.7, 2086.69, 25, 2086.69, 1.0, 1.0, 1455.0, 1072.0, 25, 4, 26, 11, 2023, 192.0, 130.0, 2086.7, 14.44, 2086.69, 81.2, 1, 30.67, 160.97, 2086.78, 2086.62, 2086.7], [Timestamp('2023-11-25 04:27:00'), 2086.59, 2087.07, 2087.08, 2087.07, 27, 2087.07, 1.0, 0.0, 1607.0, 724.0, 25, 4, 27, 11, 2023, 203.0, 111.0, 2087.09, 10.1, 2087.08, 72.08, 1, 46.04, 156.88, 2087.2, 2087.02, 2087.09], [Timestamp('2023-11-25 04:28:00'), 2086.56, 2086.65, 2086.66, 2086.65, 31, 2086.66, 1.0, 0.0, 1757.0, 885.0, 25, 4, 28, 11, 2023, 164.0, -66.0, 2086.66, 70.24, 2086.65, 12.19, -1, 115.01, 49.05, 2086.7, 2086.52, 2086.66], [Timestamp('2023-11-25 04:29:00'), 2086.53, 2086.4, 2086.4, 2086.39, 32, 2086.39, 1.0, 0.0, 1671.0, 851.0, 25, 4, 29, 11, 2023, 252.0, -179.0, 2086.63, 72.12, 2086.39, 10.25, -1, 215.85, 36.57, 2086.53, 2086.27, 2086.4], [Timestamp('2023-11-25 04:30:00'), 2086.62, 2087.0, 2087.0, 2086.39, 150, 2086.39, 96.0, 95.0, 199931.0, 198661.0, 25, 4, 30, 11, 2023, 161.0, 46.0, 2087.0, 27.29, 2086.99, 50.45, 1, 57.46, 103.91, 2087.06, 2086.93, 2087.0], [Timestamp('2023-11-25 04:31:00'), 2086.81, 2086.9, 2086.91, 2086.9, 40, 2086.91, 1.0, 1.0, 2331.0, 1375.0, 25, 4, 31, 11, 2023, 163.0, 61.0, 2086.91, 10.99, 2086.9, 82.68, 1, 50.69, 112.18, 2087.02, 2086.86, 2086.91], [Timestamp('2023-11-25 04:32:00'), 2086.96, 2087.62, 2087.92, 2086.9, 258, 2086.91, 76.0, 48.0, 158519.0, 100461.0, 25, 4, 32, 11, 2023, 140.0, 69.0, 2087.8, 13.53, 2087.62, 72.15, 1, 35.46, 104.76, 2087.76, 2087.59, 2087.63], [Timestamp('2023-11-25 04:33:00'), 2087.01, 2087.15, 2087.8, 2087.15, 158, 2087.62, 49.0, 26.0, 101888.0, 54056.0, 25, 4, 33, 11, 2023, 197.0, -132.0, 2087.02, 92.08, 2086.8, 7.95, -1, 164.31, 32.76, 2087.07, 2086.87, 2087.02], [Timestamp('2023-11-25 04:34:00'), 2087.01, 2086.77, 2086.77, 2086.76, 30, 2086.76, 11.0, 10.0, 22005.0, 19996.0, 25, 4, 34, 11, 2023, 181.0, -75.0, 2086.79, 98.28, 2086.78, 23.17, -1, 128.28, 53.06, 2086.81, 2086.68, 2086.79], [Timestamp('2023-11-25 04:35:00'), 2087.06, 2086.78, 2086.79, 2086.76, 155, 2086.76, 103.0, 91.0, 215035.0, 190849.0, 25, 4, 35, 11, 2023, 198.0, -157.0, 2086.65, 104.8, 2086.5, 5.01, -1, 177.45, 20.83, 2086.71, 2086.46, 2086.65], [Timestamp('2023-11-25 04:36:00'), 2086.9, 2085.89, 2086.79, 2085.89, 212, 2086.78, 46.0, 12.0, 96251.0, 24660.0, 25, 4, 36, 11, 2023, 263.0, 170.0, 2085.92, 13.8, 2085.91, 116.59, 1, 46.12, 216.49, 2086.08, 2085.86, 2085.92], [Timestamp('2023-11-25 04:37:00'), 2086.46, 2086.2, 2086.21, 2086.2, 30, 2086.2, 3.0, 1.0, 5867.0, 1129.0, 25, 4, 37, 11, 2023, 104.0, -25.0, 2086.21, 30.89, 2086.2, 20.82, -1, 64.63, 39.37, 2086.27, 2086.14, 2086.21], [Timestamp('2023-11-25 04:38:00'), 2086.34, 2085.95, 2085.95, 2085.94, 28, 2085.94, 1.0, 0.0, 1776.0, 937.0, 25, 4, 38, 11, 2023, 234.0, 164.0, 2086.1, 12.08, 2086.09, 94.46, 1, 35.01, 199.12, 2086.17, 2086.04, 2086.1], [Timestamp('2023-11-25 04:39:00'), 2086.27, 2086.78, 2086.79, 2086.78, 35, 2086.78, 2.0, 0.0, 4633.0, 935.0, 25, 4, 39, 11, 2023, 249.0, 172.0, 2087.15, 9.69, 2087.01, 115.91, 1, 38.58, 210.76, 2087.17, 2086.95, 2087.02], [Timestamp('2023-11-25 04:40:00'), 2086.18, 2086.6, 2086.6, 2086.59, 35, 2086.6, 6.0, 1.0, 12583.0, 1404.0, 25, 4, 40, 11, 2023, 172.0, 111.0, 2086.8, 11.93, 2086.59, 69.47, 1, 30.5, 141.89, 2086.78, 2086.55, 2086.6], [Timestamp('2023-11-25 04:41:00'), 2086.14, 2085.62, 2085.63, 2085.62, 33, 2085.63, 1.0, 1.0, 1993.0, 1158.0, 25, 4, 41, 11, 2023, 180.0, -66.0, 2085.21, 49.57, 2085.2, 36.38, -1, 122.7, 56.93, 2085.3, 2085.13, 2085.21], [Timestamp('2023-11-25 04:42:00'), 2086.0, 2084.7, 2084.71, 2084.7, 27, 2084.71, 1.0, 0.0, 1677.0, 826.0, 25, 4, 42, 11, 2023, 140.0, 81.0, 2084.71, 8.93, 2084.7, 79.35, 1, 29.5, 110.23, 2084.85, 2084.67, 2084.71], [Timestamp('2023-11-25 04:43:00'), 2085.64, 2084.42, 2084.43, 2084.42, 32, 2084.42, 1.0, 0.0, 1916.0, 982.0, 25, 4, 43, 11, 2023, 133.0, 55.0, 2084.43, 16.15, 2084.42, 62.78, 1, 39.14, 94.09, 2084.51, 2084.38, 2084.43], [Timestamp('2023-11-25 04:44:00'), 2085.18, 2084.39, 2084.4, 2084.39, 20, 2084.4, 1.0, 0.0, 1065.0, 654.0, 25, 4, 44, 11, 2023, 227.0, 97.0, 2084.6, 16.62, 2084.59, 83.19, 1, 64.81, 162.27, 2084.72, 2084.53, 2084.6], [Timestamp('2023-11-25 04:45:00'), 2084.89, 2084.97, 2084.97, 2084.96, 36, 2084.97, 1.0, 0.0, 1799.0, 846.0, 25, 4, 45, 11, 2023, 252.0, 152.0, 2084.97, 15.95, 2084.96, 97.54, 1, 50.19, 202.12, 2085.11, 2084.91, 2084.97], [Timestamp('2023-11-25 04:46:00'), 2084.58, 2084.49, 2084.49, 2084.48, 35, 2084.48, 1.0, 1.0, 2692.0, 1373.0, 25, 4, 46, 11, 2023, 261.0, 121.0, 2084.59, 23.17, 2084.48, 98.03, 1, 70.18, 191.19, 2084.56, 2084.44, 2084.49], [Timestamp('2023-11-25 04:47:00'), 2084.66, 2084.68, 2084.69, 2084.68, 28, 2084.69, 1.0, 0.0, 1685.0, 781.0, 25, 4, 47, 11, 2023, 176.0, -65.0, 2084.69, 84.02, 2084.68, 26.23, -1, 120.36, 55.18, 2084.73, 2084.64, 2084.69], [Timestamp('2023-11-25 04:48:00'), 2084.58, 2083.8, 2083.8, 2083.79, 32, 2083.79, 1.0, 1.0, 3022.0, 1833.0, 25, 4, 48, 11, 2023, 170.0, 74.0, 2083.8, 12.48, 2083.79, 97.03, 1, 48.18, 121.86, 2083.91, 2083.76, 2083.8], [Timestamp('2023-11-25 04:49:00'), 2084.49, 2083.2, 2083.21, 2083.2, 27, 2083.21, 1.0, 0.0, 1645.0, 1038.0, 25, 4, 49, 11, 2023, 166.0, -37.0, 2083.21, 63.74, 2083.2, 40.08, -1, 101.73, 64.44, 2083.29, 2083.15, 2083.21], [Timestamp('2023-11-25 04:50:00'), 2084.35, 2083.2, 2083.2, 2083.19, 32, 2083.2, 1.0, 0.0, 1956.0, 979.0, 25, 4, 50, 11, 2023, 155.0, -44.0, 2083.2, 62.7, 2083.19, 38.81, -1, 99.33, 55.62, 2083.25, 2083.14, 2083.2], [Timestamp('2023-11-25 04:51:00'), 2084.27, 2083.2, 2083.2, 2083.19, 29, 2083.19, 1.0, 1.0, 2084.0, 1126.0, 25, 4, 51, 11, 2023, 173.0, -83.0, 2083.2, 91.36, 2083.19, 15.68, -1, 128.06, 44.73, 2083.24, 2083.1, 2083.2], [Timestamp('2023-11-25 04:52:00'), 2083.62, 2084.64, 2084.65, 2084.64, 30, 2084.65, 1.0, 0.0, 1675.0, 775.0, 25, 4, 52, 11, 2023, 188.0, 13.0, 2084.65, 61.54, 2084.64, 63.48, 1, 87.68, 100.36, 2084.68, 2084.61, 2084.65], [Timestamp('2023-11-25 04:53:00'), 2083.72, 2084.2, 2084.2, 2084.19, 23, 2084.2, 1.0, 0.0, 1359.0, 980.0, 25, 4, 53, 11, 2023, 152.0, -63.0, 2084.2, 74.75, 2084.19, 12.18, -1, 107.59, 44.83, 2084.23, 2084.14, 2084.2], [Timestamp('2023-11-25 04:54:00'), 2083.78, 2084.05, 2084.06, 2084.05, 32, 2084.05, 1.0, 0.0, 1904.0, 1041.0, 25, 4, 54, 11, 2023, 165.0, 68.0, 2084.2, 17.25, 2084.05, 73.17, 1, 48.62, 116.79, 2084.18, 2084.03, 2084.06], [Timestamp('2023-11-25 04:55:00'), 2083.95, 2084.48, 2084.49, 2084.05, 202, 2084.05, 77.0, 65.0, 161141.0, 134981.0, 25, 4, 55, 11, 2023, 184.0, 46.0, 2084.49, 20.21, 2084.48, 74.19, 1, 68.64, 114.9, 2084.6, 2084.44, 2084.49], 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2086.28], [Timestamp('2023-11-25 05:05:00'), 2085.97, 2085.66, 2085.66, 2085.65, 22, 2085.65, 1.0, 0.0, 1160.0, 429.0, 25, 5, 5, 11, 2023, 191.0, -90.0, 2085.66, 77.43, 2085.65, 19.16, -1, 140.86, 50.57, 2085.72, 2085.57, 2085.66], [Timestamp('2023-11-25 05:06:00'), 2085.9, 2085.02, 2085.02, 2085.01, 35, 2085.01, 1.0, 0.0, 1746.0, 916.0, 25, 5, 6, 11, 2023, 193.0, -63.0, 2085.02, 88.98, 2085.01, 10.48, -1, 127.87, 65.08, 2085.05, 2084.85, 2085.02], [Timestamp('2023-11-25 05:07:00'), 2085.78, 2084.26, 2084.26, 2084.25, 26, 2084.26, 1.0, 0.0, 1667.0, 912.0, 25, 5, 7, 11, 2023, 303.0, -239.0, 2084.26, 180.29, 2084.25, 6.91, -1, 271.07, 32.3, 2084.29, 2084.14, 2084.26], [Timestamp('2023-11-25 05:08:00'), 2085.45, 2084.63, 2084.63, 2084.5, 51, 2084.51, 2.0, 1.0, 4287.0, 2948.0, 25, 5, 8, 11, 2023, 271.0, 161.0, 2084.65, 14.55, 2084.64, 92.0, 1, 55.19, 216.08, 2084.74, 2084.57, 2084.65], [Timestamp('2023-11-25 05:09:00'), 2085.16, 2084.45, 2084.46, 2084.45, 27, 2084.46, 2.0, 0.0, 3246.0, 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{"Action": "HOLD","Return": "-0.00315","Max": "0.00092","Min": "-0.00746"}

45422

You are a machine learning model designed to classify time-series financial data into one of three categories: BUY, SELL, or HOLD. Your task is to analyze the given input data and provide a JSON-structured output containing the classification action and additional calculated metrics based on the data. ### Instructions: 1. Analyze the input time-series data, which consists of financial indicators and timestamps. 2. Determine whether the overall trend and features suggest a "BUY," "SELL," or "HOLD" action. 3. Calculate the following metrics from the data: - **Return**: The percentage change from the first to the last value in the primary financial metric. - **Max**: The maximum percentage change within the dataset. - **Min**: The minimum percentage change within the dataset. 4. Provide the results in the following JSON format: ```json { "Action": "<BUY/SELL/HOLD>", "Return": "<calculated_return>", "Max": "<max_percentage_change>", "Min": "<min_percentage_change>" } ``` 5. Ensure all percentages are formatted to five decimal places and represented as strings. ### Input Format: The input is a list of lists. Each sublist contains a timestamp followed by various financial metrics. Example: [ [Timestamp('2023-11-25 02:41:00'), 2086.44, 2086.74, ...], [Timestamp('2023-11-25 02:42:00'), 2086.42, 2085.78, ...], ... ] ### Output Format: Return the classification and metrics strictly in the JSON format described above, followed by the token count in the format: ```json 'output': '{"Action": "<BUY/SELL/HOLD>", "Return": "<calculated_return>", "Max": "<max_percentage_change>", "Min": "<min_percentage_change>"}' ``` ### Constraints: - Do not provide any explanation or intermediate analysis in your response. - The output must strictly adhere to the format specified above. ### Example: Input [ [Timestamp('2023-11-25 02:41:00'), 2086.44, 2086.74, ...], [Timestamp('2023-11-25 02:42:00'), 2086.42, 2085.78, ...], ... ] Output { "Action": "BUY", "Return": "0.00200", "Max": "0.00300", "Min": "-0.00100" }

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{"Action": "HOLD","Return": "0.00115","Max": "0.00115","Min": "-0.00301"}

45423

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-25 00:54:00'), 2078.17, 2076.95, 2076.96, 2076.95, 16, 2076.95, 1.0, 0.0, 1061(...TRUNCATED)

{"Action": "HOLD","Return": "0.00153","Max": "0.00369","Min": "-0.0004"}

45430

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-25 02:16:00'), 2081.04, 2080.52, 2080.53, 2080.52, 35, 2080.52, 1.0, 0.0, 2269(...TRUNCATED)

{"Action": "HOLD","Return": "-0.00151","Max": "0.00297","Min": "-0.00205"}

45436

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-24 22:03:00'), 2082.61, 2083.65, 2083.65, 2083.64, 24, 2083.65, 1.0, 0.0, 1439(...TRUNCATED)

{"Action": "HOLD","Return": "-0.00095","Max": "0.00143","Min": "-0.00275"}

45415

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-25 06:20:00'), 2083.05, 2083.49, 2083.49, 2083.44, 120, 2083.44, 15.0, 8.0, 30(...TRUNCATED)

{"Action": "HOLD","Return": "0.00039","Max": "0.00244","Min": "-0.00396"}

45427

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-24 23:54:00'), 2079.29, 2079.87, 2079.87, 2079.86, 30, 2079.87, 1.0, 1.0, 2343(...TRUNCATED)

{"Action": "HOLD","Return": "0.00196","Max": "0.00253","Min": "-0.00163"}

45427

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-25 03:54:00'), 2087.62, 2086.88, 2086.89, 2086.88, 35, 2086.88, 1.0, 1.0, 2182(...TRUNCATED)

{"Action": "HOLD","Return": "-0.00796","Max": "0.0","Min": "-0.0094"}

45427

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-25 06:11:00'), 2084.11, 2083.23, 2083.24, 2083.23, 24, 2083.23, 2.0, 0.0, 4819(...TRUNCATED)

{"Action": "HOLD","Return": "-4e-05","Max": "0.00143","Min": "-0.00497"}

45431

"\n\nYou are a machine learning model designed to classify time-series financial data into one of th(...TRUNCATED)

"[[Timestamp('2023-11-25 01:14:00'), 2080.94, 2081.31, 2081.32, 2081.31, 24, 2081.32, 2.0, 0.0, 4350(...TRUNCATED)

{"Action": "HOLD","Return": "-0.00036","Max": "0.00386","Min": "-0.00108"}

45428