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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 Files
xet
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
trade_id: string
signal_id: string
mode: string
real_order_id: null
real_exit_order_id: null
symbol: string
instrument_key: string
option_type: string
direction: string
lots: int64
lot_size: int64
quantity: int64
entry_time: timestamp[s]
exit_time: timestamp[s]
entry_opt_price: double
exit_opt_price: double
opt_points_pnl: double
entry_spot: double
exit_spot: double
spot_points_pnl: double
margin_invested: double
exit_turnover: double
gross_pnl: double
fees_paid: double
fees_breakdown: struct<brokerage: double, stt: double, exchange_charges: double, sebi_charges: double, stamp_duty: d (... 39 chars omitted)
  child 0, brokerage: double
  child 1, stt: double
  child 2, exchange_charges: double
  child 3, sebi_charges: double
  child 4, stamp_duty: double
  child 5, gst: double
  child 6, total_fees: double
net_pnl: double
roi_pct: double
exit_reason: string
t1_hit: bool
t2_hit: bool
target_trailed: bool
mfe_pts: double
mae_pts: double
details: null
opt_target_1: null
opt_target_2: null
opt_sl: null
opt_t1_hit: bool
opt_t2_hit: bool
opt_peak_price: double
opt_peak_roi_pct: double
moneyness: string
strike_category: string
entry_type: string
amount: double
timestamp: timestamp[s]
total_equity: double
cash_balance_after: double
used_margin: double
to
{'timestamp': Value('timestamp[s]'), 'entry_type': Value('string'), 'amount': Value('float64'), 'cash_balance_after': Value('float64'), 'used_margin': Value('float64'), 'total_equity': Value('float64'), 'details': Json(decode=True)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              trade_id: string
              signal_id: string
              mode: string
              real_order_id: null
              real_exit_order_id: null
              symbol: string
              instrument_key: string
              option_type: string
              direction: string
              lots: int64
              lot_size: int64
              quantity: int64
              entry_time: timestamp[s]
              exit_time: timestamp[s]
              entry_opt_price: double
              exit_opt_price: double
              opt_points_pnl: double
              entry_spot: double
              exit_spot: double
              spot_points_pnl: double
              margin_invested: double
              exit_turnover: double
              gross_pnl: double
              fees_paid: double
              fees_breakdown: struct<brokerage: double, stt: double, exchange_charges: double, sebi_charges: double, stamp_duty: d (... 39 chars omitted)
                child 0, brokerage: double
                child 1, stt: double
                child 2, exchange_charges: double
                child 3, sebi_charges: double
                child 4, stamp_duty: double
                child 5, gst: double
                child 6, total_fees: double
              net_pnl: double
              roi_pct: double
              exit_reason: string
              t1_hit: bool
              t2_hit: bool
              target_trailed: bool
              mfe_pts: double
              mae_pts: double
              details: null
              opt_target_1: null
              opt_target_2: null
              opt_sl: null
              opt_t1_hit: bool
              opt_t2_hit: bool
              opt_peak_price: double
              opt_peak_roi_pct: double
              moneyness: string
              strike_category: string
              entry_type: string
              amount: double
              timestamp: timestamp[s]
              total_equity: double
              cash_balance_after: double
              used_margin: double
              to
              {'timestamp': Value('timestamp[s]'), 'entry_type': Value('string'), 'amount': Value('float64'), 'cash_balance_after': Value('float64'), 'used_margin': Value('float64'), 'total_equity': Value('float64'), 'details': Json(decode=True)}
              because column names don't match

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🚀 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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