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Datasets:
nagarhimanshu37
/
nifty-session-logs

Tasks:
Tabular Classification
Reinforcement Learning
Size:
n<1K
Tags:
finance
trading
nifty
algorithmic-trading
ai-agent
session-logs
License:
Dataset card Data Studio Files
xet
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ValueError
Message:      Expected object or value
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
                  examples = [ujson_loads(line) for line in batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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text
list
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[ "\"2026-09-01T00:00:00+05:30\"", "24077.55", "24143.15", "23952.55", "24055.8", "0", "0" ]
null
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[ "\"2026-10-01T15:13:00+05:30\"", "22524.9", "22525.0", "22514.6", "22520.5", "9360", "19627920" ]
[ "\"2026-10-01T15:12:00+05:30\"", "22523.0", "22527.9", "22520.3", "22520.8", "13455", "19625515" ]
[ "\"2026-10-01T15:11:00+05:30\"", "22505.5", "22522.8", "22505.5", "22520.0", "52130", "19606405" ]
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[ "\"2026-10-01T15:09:00+05:30\"", "22502.3", "22509.0", "22491.6", "22500.1", "7735", "19591910" ]
[ "\"2026-10-01T15:08:00+05:30\"", "22507.0", "22515.9", "22496.3", "22502.3", "59150", "19585345" ]
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[ "\"2026-10-01T15:06:00+05:30\"", "22522.0", "22531.0", "22510.8", "22510.8", "30680", "19542575" ]
[ "\"2026-10-01T15:05:00+05:30\"", "22496.0", "22524.0", "22494.7", "22516.6", "20150", "19541600" ]
[ "\"2026-10-01T15:04:00+05:30\"", "22484.9", "22504.1", "22484.9", "22503.4", "14430", "19548685" ]
[ "\"2026-10-01T15:03:00+05:30\"", "22480.0", "22488.0", "22477.7", "22484.7", "12610", "19547125" ]
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[ "\"2026-10-01T14:54:00+05:30\"", "22495.0", "22505.0", "22493.3", "22500.0", "27040", "19578520" ]
[ "\"2026-10-01T14:53:00+05:30\"", "22481.1", "22495.0", "22481.1", "22495.0", "21775", "19579170" ]
[ "\"2026-10-01T14:52:00+05:30\"", "22479.0", "22483.0", "22473.3", "22482.3", "14430", "19570915" ]
[ "\"2026-10-01T14:51:00+05:30\"", "22477.5", "22485.0", "22460.0", "22477.2", "12805", "19566105" ]
[ "\"2026-10-01T14:50:00+05:30\"", "22452.7", "22479.0", "22451.1", "22479.0", "20215", "19568770" ]
[ "\"2026-10-01T14:49:00+05:30\"", "22450.0", "22458.9", "22446.1", "22453.3", "10790", "19562205" ]
[ "\"2026-10-01T14:48:00+05:30\"", "22454.1", "22465.0", "22447.3", "22455.4", "14365", "19556550" ]
[ "\"2026-10-01T14:47:00+05:30\"", "22440.0", "22460.7", "22438.7", "22453.9", "23140", "19549075" ]
[ "\"2026-10-01T14:46:00+05:30\"", "22448.7", "22453.2", "22440.0", "22440.0", "13325", "19542705" ]
[ "\"2026-10-01T14:45:00+05:30\"", "22460.0", "22464.4", "22445.1", "22448.4", "9165", "19538155" ]
[ "\"2026-10-01T14:44:00+05:30\"", "22453.6", "22459.5", "22446.2", "22451.8", "8970", "19533670" ]
[ "\"2026-10-01T14:43:00+05:30\"", "22458.9", "22463.3", "22450.0", "22454.4", "7475", "19528990" ]
[ "\"2026-10-01T14:42:00+05:30\"", "22463.9", "22463.9", "22442.7", "22453.7", "25350", "19525415" ]
[ "\"2026-10-01T14:41:00+05:30\"", "22466.5", "22466.7", "22457.6", "22461.2", "8125", "19522945" ]
[ "\"2026-10-01T14:40:00+05:30\"", "22461.1", "22468.3", "22456.0", "22463.4", "10660", "19519890" ]
[ "\"2026-10-01T14:39:00+05:30\"", "22474.5", "22475.0", "22449.4", "22460.7", "10985", "19515145" ]
[ "\"2026-10-01T14:38:00+05:30\"", "22484.1", "22488.3", "22465.6", "22473.9", "19110", "19512870" ]
[ "\"2026-10-01T14:37:00+05:30\"", "22484.0", "22491.8", "22480.0", "22482.7", "29705", "19506305" ]
[ "\"2026-10-01T14:36:00+05:30\"", "22472.1", "22484.3", "22468.9", "22483.0", "23010", "19498960" ]
[ "\"2026-10-01T14:35:00+05:30\"", "22470.5", "22481.0", "22453.8", "22478.9", "43875", "19479200" ]
[ "\"2026-10-01T14:34:00+05:30\"", "22469.1", "22480.0", "22465.0", "22470.5", "54275", "19470230" ]
[ "\"2026-10-01T14:33:00+05:30\"", "22438.6", "22472.0", "22422.6", "22467.8", "43160", "19440980" ]
[ "\"2026-10-01T14:32:00+05:30\"", "22452.4", "22455.0", "22428.8", "22438.6", "28990", "19428565" ]
[ "\"2026-10-01T14:31:00+05:30\"", "22447.9", "22456.0", "22440.3", "22455.0", "34385", "19411925" ]
[ "\"2026-10-01T14:30:00+05:30\"", "22416.5", "22448.0", "22411.3", "22447.4", "61295", "19379035" ]
[ "\"2026-10-01T14:29:00+05:30\"", "22396.0", "22416.6", "22393.9", "22413.1", "26780", "19359015" ]
[ "\"2026-10-01T14:28:00+05:30\"", "22406.0", "22407.3", "22386.7", "22393.3", "17355", "19342765" ]
[ "\"2026-10-01T14:27:00+05:30\"", "22407.7", "22424.0", "22403.5", "22404.9", "40885", "19330415" ]
[ "\"2026-10-01T14:26:00+05:30\"", "22385.0", "22409.4", "22385.0", "22407.7", "20410", "19306885" ]
[ "\"2026-10-01T14:25:00+05:30\"", "22373.7", "22385.0", "22373.7", "22385.0", "5005", "19304675" ]
[ "\"2026-10-01T14:24:00+05:30\"", "22380.0", "22382.2", "22370.0", "22372.5", "7995", "19299085" ]
[ "\"2026-10-01T14:23:00+05:30\"", "22387.9", "22389.4", "22376.6", "22381.0", "9685", "19296030" ]
[ "\"2026-10-01T14:22:00+05:30\"", "22383.7", "22390.0", "22380.0", "22385.0", "7865", "19289270" ]
End of preview.

🚀 Nifty 50 AI Agent — Kaggle Session Logs & Accuracy Telemetry

This dataset repository contains structured session telemetry, real-time trade signals, risk gate evaluations, agent decisions, and accuracy improvement reports generated by the Nifty AI Brain Agent running autonomously on Kaggle dual T4 GPU environment.

📂 Repository Structure 

nagarhimanshu37/nifty-session-logs
├── README.md                      ← Telemetry overview & session log index
├── latest_report.json             ← Latest session accuracy & weakness report
└── sessions/
    └── <session_id>/
        ├── session_meta.jsonl     ← Session start/end timestamps & runtime metadata
        ├── session_summary_raw.json ← Quick session totals
        ├── accuracy_report.json   ← Computed accuracy, win rates & weakness analysis
        ├── improvement_notes.md   ← Human-readable action items for next prompt/code edit
        ├── signals.jsonl          ← Every generated AlphaSignal before risk gate
        ├── trades.jsonl           ← Every approved trade + execution/outcome
        ├── vetoed_trades.jsonl    ← Every trade rejected by QuantitativeRiskAgent
        ├── agent_decisions.jsonl  ← Sub-agent cycle steps and market scans
        ├── heartbeats.jsonl       ← 24/7 supervisor health snapshots
        └── errors.jsonl           ← Anomaly reports & runtime exceptions

🎯 Purpose

  1. Continuous Learning: Track which setups (CPR Breakout, VWAP Rejection, Trap Hunter) perform best.
  2. Defect & Weakness Isolation: Automatically identify why signals fail (e.g. low VIX chop, opening 15-min noise, wrong delta).
  3. Agent Tuning: Use improvement_notes.md to refine strategy thresholds, risk weights, and LLM reasoning prompts.

Automatically synced from Kaggle dual T4 runners via HFSessionPusher.
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