Datasets:

nagarhimanshu37
/

nifty-session-logs

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
timestamp: timestamp[s]
entry_type: string
amount: double
cash_balance_after: double
used_margin: double
total_equity: double
details: struct<note: string, trade_id: string, symbol: string, action: string, margin_released: double, gros (... 76 chars omitted)
  child 0, note: string
  child 1, trade_id: string
  child 2, symbol: string
  child 3, action: string
  child 4, margin_released: double
  child 5, gross_pnl: double
  child 6, fees_paid: double
  child 7, net_pnl: double
  child 8, trade_date: timestamp[s]
entry_spot: double
trade_bias: string
target_1_spot: double
opt_t1_hit: bool
is_open: bool
trough_spot: double
mfe_pts: double
t1_hit_time: null
current_spot: double
is_expiry_day: bool
entry_opt_price: double
strike_price: double
lots: int64
current_sl_spot: double
runner_miles_pts: double
opt_target_1: double
real_exit_order_id: null
strike_category: string
opt_sl: double
real_order_id: null
signal_id: string
margin_invested: double
original_target_2_spot: double
t1_hit: bool
symbol: string
moneyness: string
entry_time: timestamp[s]
trailed_to_cost: bool
lot_size: int64
peak_spot: double
opt_peak_price: double
trade_id: string
target_2_spot: double
invalidation_sl_spot: double
quantity: int64
t2_hit: bool
target_trailed: bool
current_opt_price: double
opt_t2_hit: bool
mae_pts: double
current_sl_opt: double
instrument_key: string
mode: string
trailed_to_t1: bool
entry_clock: string
direction: string
unrealized_pnl: double
is_overnight: bool
opt_peak_roi_pct: double
option_type: string
opt_target_2: double
to
{'is_open': Value('bool'), 'mode': Value('string'), 'real_order_id': Value('null'), 'real_exit_order_id': Value('null'), 'trade_id': Value('string'), 'signal_id': Value('string'), 'symbol': Value('string'), 'instrument_key': Value('string'), 'option_type': Value('string'), 'strike_price': Value('float64'), 'trade_bias': Value('string'), 'direction': Value('string'), 'is_overnight': Value('bool'), 'is_expiry_day': Value('bool'), 'quantity': Value('int64'), 'lots': Value('int64'), 'lot_size': Value('int64'), 'entry_opt_price': Value('float64'), 'entry_spot': Value('float64'), 'margin_invested': Value('float64'), 'invalidation_sl_spot': Value('float64'), 'target_1_spot': Value('float64'), 'target_2_spot': Value('float64'), 'original_target_2_spot': Value('float64'), 'current_sl_spot': Value('float64'), 't1_hit': Value('bool'), 't1_hit_time': Value('null'), 't2_hit': Value('bool'), 'trailed_to_cost': Value('bool'), 'trailed_to_t1': Value('bool'), 'target_trailed': Value('bool'), 'peak_spot': Value('float64'), 'trough_spot': Value('float64'), 'runner_miles_pts': Value('float64'), 'current_opt_price': Value('float64'), 'current_spot': Value('float64'), 'unrealized_pnl': Value('float64'), 'mfe_pts': Value('float64'), 'mae_pts': Value('float64'), 'opt_target_1': Value('float64'), 'opt_target_2': Value('float64'), 'opt_sl': Value('float64'), 'current_sl_opt': Value('float64'), 'opt_t1_hit': Value('bool'), 'opt_t2_hit': Value('bool'), 'opt_peak_price': Value('float64'), 'opt_peak_roi_pct': Value('float64'), 'moneyness': Value('string'), 'strike_category': Value('string'), 'entry_time': Value('timestamp[s]'), 'entry_clock': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              timestamp: timestamp[s]
              entry_type: string
              amount: double
              cash_balance_after: double
              used_margin: double
              total_equity: double
              details: struct<note: string, trade_id: string, symbol: string, action: string, margin_released: double, gros (... 76 chars omitted)
                child 0, note: string
                child 1, trade_id: string
                child 2, symbol: string
                child 3, action: string
                child 4, margin_released: double
                child 5, gross_pnl: double
                child 6, fees_paid: double
                child 7, net_pnl: double
                child 8, trade_date: timestamp[s]
              entry_spot: double
              trade_bias: string
              target_1_spot: double
              opt_t1_hit: bool
              is_open: bool
              trough_spot: double
              mfe_pts: double
              t1_hit_time: null
              current_spot: double
              is_expiry_day: bool
              entry_opt_price: double
              strike_price: double
              lots: int64
              current_sl_spot: double
              runner_miles_pts: double
              opt_target_1: double
              real_exit_order_id: null
              strike_category: string
              opt_sl: double
              real_order_id: null
              signal_id: string
              margin_invested: double
              original_target_2_spot: double
              t1_hit: bool
              symbol: string
              moneyness: string
              entry_time: timestamp[s]
              trailed_to_cost: bool
              lot_size: int64
              peak_spot: double
              opt_peak_price: double
              trade_id: string
              target_2_spot: double
              invalidation_sl_spot: double
              quantity: int64
              t2_hit: bool
              target_trailed: bool
              current_opt_price: double
              opt_t2_hit: bool
              mae_pts: double
              current_sl_opt: double
              instrument_key: string
              mode: string
              trailed_to_t1: bool
              entry_clock: string
              direction: string
              unrealized_pnl: double
              is_overnight: bool
              opt_peak_roi_pct: double
              option_type: string
              opt_target_2: double
              to
              {'is_open': Value('bool'), 'mode': Value('string'), 'real_order_id': Value('null'), 'real_exit_order_id': Value('null'), 'trade_id': Value('string'), 'signal_id': Value('string'), 'symbol': Value('string'), 'instrument_key': Value('string'), 'option_type': Value('string'), 'strike_price': Value('float64'), 'trade_bias': Value('string'), 'direction': Value('string'), 'is_overnight': Value('bool'), 'is_expiry_day': Value('bool'), 'quantity': Value('int64'), 'lots': Value('int64'), 'lot_size': Value('int64'), 'entry_opt_price': Value('float64'), 'entry_spot': Value('float64'), 'margin_invested': Value('float64'), 'invalidation_sl_spot': Value('float64'), 'target_1_spot': Value('float64'), 'target_2_spot': Value('float64'), 'original_target_2_spot': Value('float64'), 'current_sl_spot': Value('float64'), 't1_hit': Value('bool'), 't1_hit_time': Value('null'), 't2_hit': Value('bool'), 'trailed_to_cost': Value('bool'), 'trailed_to_t1': Value('bool'), 'target_trailed': Value('bool'), 'peak_spot': Value('float64'), 'trough_spot': Value('float64'), 'runner_miles_pts': Value('float64'), 'current_opt_price': Value('float64'), 'current_spot': Value('float64'), 'unrealized_pnl': Value('float64'), 'mfe_pts': Value('float64'), 'mae_pts': Value('float64'), 'opt_target_1': Value('float64'), 'opt_target_2': Value('float64'), 'opt_sl': Value('float64'), 'current_sl_opt': Value('float64'), 'opt_t1_hit': Value('bool'), 'opt_t2_hit': Value('bool'), 'opt_peak_price': Value('float64'), 'opt_peak_roi_pct': Value('float64'), 'moneyness': Value('string'), 'strike_category': Value('string'), 'entry_time': Value('timestamp[s]'), 'entry_clock': Value('string')}
              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

Continuous Learning: Track which setups (CPR Breakout, VWAP Rejection, Trap Hunter) perform best.
Defect & Weakness Isolation: Automatically identify why signals fail (e.g. low VIX chop, opening 15-min noise, wrong delta).
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.

Downloads last month: 3,348

Total file size:

2.27 GB