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models/nifty_forecaster/outputs/forecaster_report.md ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Daily Forecaster
2
+
3
+ Target: next-day direction forecast.
4
+ Coverage: NIFTY 50 and NIFTY BANK only.
5
+
6
+ ## NIFTY 50
7
+ - config: locked_multiwindow_nifty50_ensemble_v2
8
+ - validation window: 2024-07-01 to 2025-08-17
9
+ - validation accuracy: 58.87%
10
+ - test accuracy: 70.71%
11
+ - baseline accuracy: 50.00%
12
+ - threshold: 0.534
13
+ - features: 204
14
+ - test probability std: 0.0632
15
+ - test probability range: 0.3750 to 0.6422
16
+ - latest data date: 2026-06-09
17
+ - forecast target: next trading bar after 2026-06-09
18
+ - latest forecast probability up: 0.5295
19
+ - latest forecast signal: UP
20
+
21
+ ## NIFTY BANK
22
+ - config: ensemble_multiwindow_daily
23
+ - validation window: 2024-07-01 to 2025-08-17
24
+ - validation accuracy: 52.13%
25
+ - test accuracy: 56.06%
26
+ - baseline accuracy: 53.54%
27
+ - threshold: 0.441
28
+ - features: 543
29
+ - test probability std: 0.0824
30
+ - test probability range: 0.3052 to 0.8752
31
+ - latest data date: 2026-06-09
32
+ - forecast target: next trading bar after 2026-06-09
33
+ - latest forecast probability up: 0.5126
34
+ - latest forecast signal: UP
models/nifty_forecaster/outputs/forecaster_summary.json ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "symbol": "NIFTY 50",
4
+ "horizon": "daily",
5
+ "horizon_bars": 1,
6
+ "config": {
7
+ "name": "locked_multiwindow_nifty50_ensemble_v2",
8
+ "use_intraday": true,
9
+ "use_external": true,
10
+ "use_institutional": false,
11
+ "use_options": true,
12
+ "use_engineered_macro_flow": false,
13
+ "blend_mode": "locked_nifty50_multiwindow_v2",
14
+ "decision_overlay": "bank_body_near_threshold;low_bank_vol_down;strong_bank_impulse_flip;tiny_range_up"
15
+ },
16
+ "threshold": 0.534,
17
+ "validation_accuracy": 0.5886524822695035,
18
+ "test_accuracy": 0.7070707070707071,
19
+ "baseline_accuracy": 0.5,
20
+ "n_train": 2221,
21
+ "n_valid": 282,
22
+ "n_test": 198,
23
+ "train_start": "2015-01-09",
24
+ "train_end": "2023-12-31",
25
+ "valid_start": "2024-07-01",
26
+ "valid_end": "2025-08-17",
27
+ "test_start": "2025-08-18",
28
+ "test_end": "2026-06-08",
29
+ "latest_forecast_date": "2026-06-09",
30
+ "latest_forecast_for": "next trading bar after 2026-06-09",
31
+ "latest_forecast_prob_up": 0.5295140762033614,
32
+ "latest_forecast_signal": "UP",
33
+ "feature_count": 204,
34
+ "validation_prob_std": 0.06819211926509922,
35
+ "test_prob_std": 0.06318472000353532,
36
+ "test_prob_min": 0.3750263841589948,
37
+ "test_prob_max": 0.6422291532299811
38
+ },
39
+ {
40
+ "symbol": "NIFTY BANK",
41
+ "horizon": "daily",
42
+ "horizon_bars": 1,
43
+ "config": {
44
+ "name": "ensemble_multiwindow_daily",
45
+ "use_intraday": true,
46
+ "use_external": true,
47
+ "use_institutional": true,
48
+ "use_options": true,
49
+ "use_engineered_macro_flow": true,
50
+ "blend_mode": "preset_bank",
51
+ "decision_overlay": "none"
52
+ },
53
+ "threshold": 0.441,
54
+ "validation_accuracy": 0.5212765957446809,
55
+ "test_accuracy": 0.5606060606060606,
56
+ "baseline_accuracy": 0.5353535353535354,
57
+ "n_train": 2221,
58
+ "n_valid": 282,
59
+ "n_test": 198,
60
+ "train_start": "2015-01-09",
61
+ "train_end": "2023-12-31",
62
+ "valid_start": "2024-07-01",
63
+ "valid_end": "2025-08-17",
64
+ "test_start": "2025-08-18",
65
+ "test_end": "2026-06-08",
66
+ "latest_forecast_date": "2026-06-09",
67
+ "latest_forecast_for": "next trading bar after 2026-06-09",
68
+ "latest_forecast_prob_up": 0.5126390845653349,
69
+ "latest_forecast_signal": "UP",
70
+ "feature_count": 543,
71
+ "validation_prob_std": 0.0770807821599353,
72
+ "test_prob_std": 0.08243050415771436,
73
+ "test_prob_min": 0.3052152550867235,
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+ "test_prob_max": 0.8752468404729982
75
+ }
76
+ ]
models/nifty_forecaster/outputs/forecaster_test_predictions.csv ADDED
@@ -0,0 +1,397 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ forecast_date,target_date,target,symbol,prob_up,raw_pred,pred,decision_overlay_changed,threshold
2
+ 2025-08-18,2025-08-19,1.0,NIFTY 50,0.550673919371572,1,1,False,0.534
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+ 2025-08-19,2025-08-20,1.0,NIFTY 50,0.46984246397060614,0,0,False,0.534
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+ 2025-08-20,2025-08-21,1.0,NIFTY 50,0.5107609930854999,0,0,False,0.534
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+ 2025-08-21,2025-08-22,0.0,NIFTY 50,0.4632950374631059,0,1,True,0.534
6
+ 2025-08-22,2025-08-25,1.0,NIFTY 50,0.6003826831751942,1,1,False,0.534
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+ 2025-08-25,2025-08-26,0.0,NIFTY 50,0.47659600602735025,0,0,False,0.534
8
+ 2025-08-26,2025-08-28,0.0,NIFTY 50,0.5469654969365931,1,1,False,0.534
9
+ 2025-08-28,2025-08-29,0.0,NIFTY 50,0.575833242456593,1,1,False,0.534
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+ 2025-08-29,2025-09-01,1.0,NIFTY 50,0.43086324146866484,0,0,False,0.534
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+ 2025-09-01,2025-09-02,0.0,NIFTY 50,0.5297880876220555,0,0,False,0.534
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+ 2025-09-02,2025-09-03,1.0,NIFTY 50,0.4033685641524835,0,0,False,0.534
13
+ 2025-09-03,2025-09-04,1.0,NIFTY 50,0.5957872785915961,1,1,False,0.534
14
+ 2025-09-04,2025-09-05,1.0,NIFTY 50,0.5962765283135939,1,1,False,0.534
15
+ 2025-09-05,2025-09-08,1.0,NIFTY 50,0.5239958967296331,0,1,True,0.534
16
+ 2025-09-08,2025-09-09,1.0,NIFTY 50,0.5857915061019529,1,1,False,0.534
17
+ 2025-09-09,2025-09-10,1.0,NIFTY 50,0.565997089967574,1,1,False,0.534
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+ 2025-09-10,2025-09-11,1.0,NIFTY 50,0.5537788359824963,1,1,False,0.534
19
+ 2025-09-11,2025-09-12,1.0,NIFTY 50,0.5815708481275226,1,1,False,0.534
20
+ 2025-09-12,2025-09-15,0.0,NIFTY 50,0.5492869491896248,1,1,False,0.534
21
+ 2025-09-15,2025-09-16,1.0,NIFTY 50,0.5415783546835594,1,1,False,0.534
22
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23
+ 2025-09-17,2025-09-18,1.0,NIFTY 50,0.5538444392275885,1,1,False,0.534
24
+ 2025-09-18,2025-09-19,0.0,NIFTY 50,0.5847717751384091,1,0,True,0.534
25
+ 2025-09-19,2025-09-22,0.0,NIFTY 50,0.5694816674251351,1,0,True,0.534
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+ 2025-09-23,2025-09-24,0.0,NIFTY 50,0.4807204485698447,0,0,False,0.534
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29
+ 2025-09-25,2025-09-26,0.0,NIFTY 50,0.4606135690412628,0,0,False,0.534
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+ 2025-10-03,2025-10-06,1.0,NIFTY 50,0.5452794088802748,1,1,False,0.534
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+ 2025-10-10,2025-10-13,0.0,NIFTY 50,0.4398452913794245,0,0,False,0.534
40
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+ 2025-10-14,2025-10-15,1.0,NIFTY 50,0.4660214007370931,0,0,False,0.534
42
+ 2025-10-15,2025-10-16,1.0,NIFTY 50,0.5958825978983633,1,1,False,0.534
43
+ 2025-10-16,2025-10-17,1.0,NIFTY 50,0.47179983333858927,0,0,False,0.534
44
+ 2025-10-17,2025-10-20,1.0,NIFTY 50,0.6159526779870321,1,1,False,0.534
45
+ 2025-10-20,2025-10-21,0.0,NIFTY 50,0.6422291532299811,1,1,False,0.534
46
+ 2025-10-21,2025-10-23,1.0,NIFTY 50,0.5548476377615369,1,1,False,0.534
47
+ 2025-10-23,2025-10-24,0.0,NIFTY 50,0.6019813520562289,1,0,True,0.534
48
+ 2025-10-24,2025-10-27,1.0,NIFTY 50,0.6054727479455392,1,1,False,0.534
49
+ 2025-10-27,2025-10-28,0.0,NIFTY 50,0.6267951335515416,1,1,False,0.534
50
+ 2025-10-28,2025-10-29,1.0,NIFTY 50,0.529034793431079,0,0,False,0.534
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+ 2025-10-29,2025-10-30,0.0,NIFTY 50,0.5331897506060365,0,0,False,0.534
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+ 2025-10-30,2025-10-31,0.0,NIFTY 50,0.4612013516063834,0,0,False,0.534
53
+ 2025-10-31,2025-11-03,1.0,NIFTY 50,0.5358456468866831,1,1,False,0.534
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+ 2025-11-03,2025-11-04,0.0,NIFTY 50,0.548884410683478,1,1,False,0.534
55
+ 2025-11-04,2025-11-06,0.0,NIFTY 50,0.4031090121205081,0,0,False,0.534
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+ 2025-11-06,2025-11-07,0.0,NIFTY 50,0.41411269780012233,0,0,False,0.534
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+ 2025-11-07,2025-11-10,1.0,NIFTY 50,0.5415102435120893,1,1,False,0.534
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+ 2025-11-10,2025-11-11,1.0,NIFTY 50,0.5938802636987449,1,1,False,0.534
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+ 2025-11-11,2025-11-12,1.0,NIFTY 50,0.5662551095025361,1,1,False,0.534
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+ 2025-11-12,2025-11-13,1.0,NIFTY 50,0.5714914773844914,1,1,False,0.534
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+ 2025-11-13,2025-11-14,1.0,NIFTY 50,0.40639726876339377,0,0,False,0.534
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+ 2025-11-14,2025-11-17,1.0,NIFTY 50,0.5216324609441019,0,0,False,0.534
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+ 2025-11-17,2025-11-18,0.0,NIFTY 50,0.433597888439016,0,0,False,0.534
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+ 2025-11-18,2025-11-19,1.0,NIFTY 50,0.42191523985590873,0,0,False,0.534
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+ 2025-11-19,2025-11-20,1.0,NIFTY 50,0.6068997993300329,1,0,True,0.534
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models/nifty_forecaster/outputs/old_vs_new_tomorrow_prediction_comparison.csv ADDED
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1
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models/nifty_forecaster/train.py ADDED
@@ -0,0 +1,1549 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __future__ import annotations
2
+
3
+ import argparse
4
+ import json
5
+ import math
6
+ import sys
7
+ import time
8
+ import warnings
9
+ from dataclasses import asdict, dataclass
10
+ from functools import lru_cache
11
+ from pathlib import Path
12
+ from typing import Iterable
13
+
14
+ import numpy as np
15
+ import pandas as pd
16
+ import os
17
+
18
+
19
+ def find_project_root(start: Path) -> Path:
20
+ env_root = os.environ.get("FORECASTING_PROJECT_ROOT")
21
+ if env_root:
22
+ return Path(env_root)
23
+
24
+ fallback = start.parents[4] / "forecasting project"
25
+ if (fallback / "Data").is_dir() and (fallback / "Alt Data").is_dir():
26
+ return fallback
27
+
28
+ for path in (start, *start.parents):
29
+ if (path / "Data").is_dir() and (path / "Alt Data").is_dir():
30
+ return path
31
+ raise RuntimeError(f"Could not find project root from {start}")
32
+
33
+
34
+ PROJECT_ROOT = find_project_root(Path(__file__).resolve())
35
+ DATA_DIR = PROJECT_ROOT / "Data"
36
+ ALT_DIR = PROJECT_ROOT / "Alt Data"
37
+ PRICE_DIR = DATA_DIR / "processed" / "bars" / "1d"
38
+ INTRADAY_DIR = DATA_DIR / "raw" / "minute"
39
+ OUTPUT_DIR = Path(__file__).resolve().parent / "outputs"
40
+ OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
41
+ warnings.filterwarnings("ignore", category=pd.errors.PerformanceWarning)
42
+ warnings.filterwarnings("ignore", category=FutureWarning)
43
+
44
+ DEFAULT_TRAIN_END = pd.Timestamp("2023-12-31")
45
+ DEFAULT_VALID_END = pd.Timestamp("2025-08-17")
46
+ DEFAULT_TEST_END = pd.Timestamp("2099-12-31")
47
+ COMMON_VALID_START = pd.Timestamp("2024-07-01")
48
+ LOCKED_NIFTY50_WEIGHTS = np.array([0.34099525, 0.49518660, 0.16381815], dtype="float64")
49
+ LOCKED_NIFTY50_THRESHOLD = 0.534
50
+ NIFTY50_LOW_BANK_VOL_THRESHOLD = 0.004660
51
+ NIFTY50_BANK_RET_FLIP_THRESHOLD = 0.01677902301854645
52
+ NIFTY50_TINY_RANGE_UP_THRESHOLD = 0.004204134680410373
53
+
54
+ SUPPORTED_SYMBOLS = ("NIFTY 50", "NIFTY BANK")
55
+
56
+ DAILY_VALID_WINDOWS: dict[str, tuple[pd.Timestamp, pd.Timestamp]] = {
57
+ "NIFTY 50": (pd.Timestamp("2024-07-01"), pd.Timestamp("2025-08-17")),
58
+ "NIFTY BANK": (pd.Timestamp("2024-07-01"), pd.Timestamp("2025-08-17")),
59
+ }
60
+
61
+ SYMBOL_BENCHMARKS: dict[str, str] = {
62
+ "NIFTY 50": "NIFTY BANK",
63
+ "NIFTY BANK": "NIFTY 50",
64
+ }
65
+
66
+
67
+ class ProgressBar:
68
+ """Small dependency-free terminal progress bar with elapsed time, ETA, and rate."""
69
+
70
+ def __init__(
71
+ self,
72
+ total: int,
73
+ description: str = "Progress",
74
+ *,
75
+ enabled: bool = True,
76
+ width: int = 34,
77
+ update_every: float = 0.2,
78
+ stream: object | None = None,
79
+ ) -> None:
80
+ self.total = max(0, int(total))
81
+ self.description = description
82
+ self.enabled = enabled
83
+ self.width = max(10, int(width))
84
+ self.update_every = max(0.0, float(update_every))
85
+ self.stream = stream if stream is not None else sys.stderr
86
+ self.current = 0
87
+ self.start_time = time.monotonic()
88
+ self.last_render = 0.0
89
+ self.closed = False
90
+ self._last_line_len = 0
91
+
92
+ def __enter__(self) -> "ProgressBar":
93
+ self.start_time = time.monotonic()
94
+ self.last_render = 0.0
95
+ self.render(force=True)
96
+ return self
97
+
98
+ def __exit__(self, exc_type: object, exc: object, tb: object) -> None:
99
+ self.close()
100
+
101
+ @staticmethod
102
+ def _format_duration(seconds: float | None) -> str:
103
+ if seconds is None or not np.isfinite(seconds) or seconds < 0:
104
+ return "--:--"
105
+ seconds = int(round(seconds))
106
+ hours, rem = divmod(seconds, 3600)
107
+ minutes, secs = divmod(rem, 60)
108
+ if hours:
109
+ return f"{hours:d}:{minutes:02d}:{secs:02d}"
110
+ return f"{minutes:02d}:{secs:02d}"
111
+
112
+ def update(self, current: int | None = None, *, description: str | None = None, force: bool = False) -> None:
113
+ if current is not None:
114
+ self.current = max(0, int(current))
115
+ if self.total:
116
+ self.current = min(self.current, self.total)
117
+ if description is not None:
118
+ self.description = description
119
+ self.render(force=force)
120
+
121
+ def advance(self, step: int = 1, *, description: str | None = None, force: bool = False) -> None:
122
+ self.update(self.current + int(step), description=description, force=force)
123
+
124
+ def render(self, *, force: bool = False) -> None:
125
+ if not self.enabled or self.closed:
126
+ return
127
+ now = time.monotonic()
128
+ if not force and (now - self.last_render) < self.update_every and self.current < self.total:
129
+ return
130
+ self.last_render = now
131
+ elapsed = max(0.0, now - self.start_time)
132
+ if self.total > 0:
133
+ fraction = min(1.0, max(0.0, self.current / self.total))
134
+ else:
135
+ fraction = 1.0
136
+ filled = int(round(self.width * fraction))
137
+ bar = "█" * filled + "░" * (self.width - filled)
138
+ rate = self.current / elapsed if elapsed > 0 else 0.0
139
+ eta = (elapsed / self.current) * (self.total - self.current) if self.current > 0 and self.total > 0 else None
140
+ line = (
141
+ f"\r{self.description} [{bar}] "
142
+ f"{self.current}/{self.total} {fraction * 100:6.2f}% | "
143
+ f"elapsed {self._format_duration(elapsed)} | "
144
+ f"ETA {self._format_duration(eta)} | "
145
+ f"{rate:,.2f}/s"
146
+ )
147
+ padding = " " * max(0, self._last_line_len - len(line))
148
+ print(line + padding, end="", file=self.stream, flush=True)
149
+ self._last_line_len = len(line)
150
+
151
+ def close(self) -> None:
152
+ if self.closed:
153
+ return
154
+ self.render(force=True)
155
+ if self.enabled:
156
+ print(file=self.stream, flush=True)
157
+ self.closed = True
158
+
159
+
160
+ def progress_note(message: str, *, enabled: bool = True) -> None:
161
+ if enabled:
162
+ print(f"[progress] {message}", file=sys.stderr, flush=True)
163
+
164
+
165
+ @dataclass(frozen=True)
166
+ class ModelSpec:
167
+ name: str
168
+ kind: str
169
+ use_intraday: bool
170
+ feature_profile: str = "all"
171
+ top_k: int | None = None
172
+ l2: float = 0.5
173
+ n_trees: int = 60
174
+ max_depth: int = 5
175
+ min_leaf: int = 30
176
+ seed: int = 7
177
+
178
+
179
+ @dataclass
180
+ class FitResult:
181
+ symbol: str
182
+ horizon: str
183
+ horizon_bars: int
184
+ config: dict[str, object]
185
+ threshold: float
186
+ validation_accuracy: float
187
+ test_accuracy: float
188
+ baseline_accuracy: float
189
+ n_train: int
190
+ n_valid: int
191
+ n_test: int
192
+ train_start: str
193
+ train_end: str
194
+ valid_start: str
195
+ valid_end: str
196
+ test_start: str
197
+ test_end: str
198
+ latest_forecast_date: str
199
+ latest_forecast_for: str
200
+ latest_forecast_prob_up: float
201
+ latest_forecast_signal: str
202
+ feature_count: int
203
+ validation_prob_std: float
204
+ test_prob_std: float
205
+ test_prob_min: float
206
+ test_prob_max: float
207
+
208
+
209
+ def price_prefix(symbol: str) -> str:
210
+ return symbol.lower().replace("&", "and").replace(" ", "_")
211
+
212
+
213
+ def benchmark_symbol(symbol: str) -> str:
214
+ return SYMBOL_BENCHMARKS.get(symbol, "NIFTY 50")
215
+
216
+
217
+ def symbol_file_stem(symbol: str) -> str:
218
+ mapping = {
219
+ "NIFTY 50": "nifty50",
220
+ "NIFTY BANK": "banknifty",
221
+ "INDIA VIX": "india_vix",
222
+ }
223
+ if symbol not in mapping:
224
+ raise KeyError(f"Unsupported symbol: {symbol}")
225
+ return mapping[symbol]
226
+
227
+
228
+ def sigmoid(x: np.ndarray) -> np.ndarray:
229
+ return 1.0 / (1.0 + np.exp(-np.clip(x, -40.0, 40.0)))
230
+
231
+
232
+ def safe_div(numer: pd.Series | np.ndarray, denom: pd.Series | np.ndarray) -> pd.Series:
233
+ n = pd.Series(numer, copy=False)
234
+ d = pd.Series(denom, copy=False)
235
+ out = pd.Series(np.nan, index=n.index, dtype="float64")
236
+ mask = d.notna() & np.isfinite(d.to_numpy(dtype="float64")) & (d != 0)
237
+ out.loc[mask] = n.loc[mask].to_numpy(dtype="float64") / d.loc[mask].to_numpy(dtype="float64")
238
+ return out
239
+
240
+
241
+ def _to_ns_datetime(series: pd.Series) -> pd.Series:
242
+ return pd.to_datetime(series, errors="coerce").astype("datetime64[ns]")
243
+
244
+
245
+ @lru_cache(maxsize=None)
246
+ def load_price_frame(symbol: str) -> pd.DataFrame:
247
+ path = PRICE_DIR / f"{symbol_file_stem(symbol)}_1d.csv"
248
+ if not path.exists():
249
+ raise FileNotFoundError(f"Missing daily price file for {symbol}: {path}")
250
+ df = pd.read_csv(path).copy()
251
+ df.columns = [str(c).strip().lower().replace(" ", "_") for c in df.columns]
252
+ if "date" not in df.columns:
253
+ raise ValueError(f"Price frame for {symbol} has no date column")
254
+ df["date"] = _to_ns_datetime(df["date"])
255
+ for col in df.columns:
256
+ if col != "date":
257
+ df[col] = pd.to_numeric(df[col], errors="coerce")
258
+ if "volume" in df.columns:
259
+ volume = pd.to_numeric(df["volume"], errors="coerce")
260
+ volume_available = volume.replace(0, np.nan).notna().sum() >= max(20, int(0.5 * len(volume)))
261
+ df["volume"] = volume.replace(0, np.nan) if volume_available else 0.0
262
+ return df.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
263
+
264
+
265
+ def load_vix() -> pd.DataFrame:
266
+ path = PRICE_DIR / "india_vix_1d.csv"
267
+ if not path.exists():
268
+ return pd.DataFrame(columns=["date"])
269
+ df = pd.read_csv(path).copy()
270
+ df.columns = [str(c).strip().lower().replace(" ", "_") for c in df.columns]
271
+ if "date" not in df.columns:
272
+ return pd.DataFrame(columns=["date"])
273
+ df["date"] = _to_ns_datetime(df["date"])
274
+ rename_map = {
275
+ "open": "vix_open",
276
+ "high": "vix_high",
277
+ "low": "vix_low",
278
+ "close": "vix_close",
279
+ "volume": "vix_volume",
280
+ }
281
+ df = df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns})
282
+ for col in df.columns:
283
+ if col != "date":
284
+ df[col] = pd.to_numeric(df[col], errors="coerce")
285
+ return df.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
286
+
287
+
288
+ def load_external_panel() -> pd.DataFrame:
289
+ path = ALT_DIR / "external" / "processed" / "external_daily_panel.csv"
290
+ if not path.exists():
291
+ return pd.DataFrame(columns=["date"])
292
+ df = pd.read_csv(path).copy()
293
+ df.columns = [str(c).strip().lower().replace(" ", "_") for c in df.columns]
294
+ if "date" not in df.columns:
295
+ return pd.DataFrame(columns=["date"])
296
+ df["date"] = _to_ns_datetime(df["date"])
297
+ for col in df.columns:
298
+ if col != "date":
299
+ df[col] = pd.to_numeric(df[col], errors="coerce")
300
+ return df.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
301
+
302
+
303
+ def load_institutional_panel() -> pd.DataFrame:
304
+ path = ALT_DIR / "institutional" / "processed" / "institutional_daily_panel.csv"
305
+ if not path.exists():
306
+ return pd.DataFrame(columns=["date"])
307
+ df = pd.read_csv(path).copy()
308
+ df.columns = [str(c).strip().lower().replace(" ", "_") for c in df.columns]
309
+ if "date" not in df.columns:
310
+ return pd.DataFrame(columns=["date"])
311
+ df["date"] = _to_ns_datetime(df["date"])
312
+ for col in df.columns:
313
+ if col != "date":
314
+ df[col] = pd.to_numeric(df[col], errors="coerce")
315
+ return df.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
316
+
317
+
318
+ def load_options_features(symbol: str) -> pd.DataFrame:
319
+ file_name = {
320
+ "NIFTY 50": "nifty50_options_daily_features.csv",
321
+ "NIFTY BANK": "banknifty_options_daily_features.csv",
322
+ }[symbol]
323
+ path = ALT_DIR / "options" / "processed" / file_name
324
+ if not path.exists():
325
+ return pd.DataFrame(columns=["date"])
326
+ df = pd.read_csv(path).copy()
327
+ df.columns = [str(c).strip().lower().replace(" ", "_") for c in df.columns]
328
+ if "date" not in df.columns:
329
+ return pd.DataFrame(columns=["date"])
330
+ df["date"] = _to_ns_datetime(df["date"])
331
+ prefix = price_prefix(symbol)
332
+ rename = {c: f"{prefix}_opt_{c}" for c in df.columns if c not in {"date", "spot_close"}}
333
+ df = df.rename(columns=rename)
334
+ for col in df.columns:
335
+ if col != "date":
336
+ df[col] = pd.to_numeric(df[col], errors="coerce")
337
+ return df.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
338
+
339
+
340
+ def add_options_regime_features(df: pd.DataFrame) -> pd.DataFrame:
341
+ df = df.copy()
342
+ for prefix in ("nifty_50_opt", "nifty_bank_opt"):
343
+ base = {
344
+ "pcr_oi": f"{prefix}_pcr_open_int",
345
+ "pcr_contracts": f"{prefix}_pcr_contracts",
346
+ "atm_pcr_oi": f"{prefix}_atm_pcr_open_int",
347
+ "atm_straddle": f"{prefix}_atm_straddle_close",
348
+ "atm_ce": f"{prefix}_atm_close_ce",
349
+ "atm_pe": f"{prefix}_atm_close_pe",
350
+ "atm_oi_ce": f"{prefix}_atm_open_int_ce",
351
+ "atm_oi_pe": f"{prefix}_atm_open_int_pe",
352
+ "contracts_ce": f"{prefix}_contracts_ce",
353
+ "contracts_pe": f"{prefix}_contracts_pe",
354
+ "oi_ce": f"{prefix}_open_int_ce",
355
+ "oi_pe": f"{prefix}_open_int_pe",
356
+ "chg_oi_ce": f"{prefix}_chg_in_oi_ce",
357
+ "chg_oi_pe": f"{prefix}_chg_in_oi_pe",
358
+ }
359
+ if base["atm_ce"] in df.columns and base["atm_pe"] in df.columns:
360
+ ce = pd.to_numeric(df[base["atm_ce"]], errors="coerce")
361
+ pe = pd.to_numeric(df[base["atm_pe"]], errors="coerce")
362
+ total = ce + pe
363
+ df[f"{prefix}_atm_skew"] = safe_div(ce - pe, total + 1e-6)
364
+ df[f"{prefix}_atm_put_share"] = safe_div(pe, total + 1e-6)
365
+ if base["atm_straddle"] in df.columns:
366
+ series = pd.to_numeric(df[base["atm_straddle"]], errors="coerce")
367
+ for w in (5, 20):
368
+ df[f"{prefix}_atm_straddle_z{w}"] = safe_div(series - series.rolling(w).mean(), series.rolling(w).std())
369
+ df[f"{prefix}_atm_straddle_ret_5"] = series.pct_change(5, fill_method=None)
370
+ if base["pcr_oi"] in df.columns:
371
+ series = pd.to_numeric(df[base["pcr_oi"]], errors="coerce")
372
+ df[f"{prefix}_pcr_oi_z20"] = safe_div(series - series.rolling(20).mean(), series.rolling(20).std())
373
+ df[f"{prefix}_pcr_oi_chg_5"] = series.diff(5)
374
+ if base["pcr_contracts"] in df.columns:
375
+ series = pd.to_numeric(df[base["pcr_contracts"]], errors="coerce")
376
+ df[f"{prefix}_pcr_contracts_z20"] = safe_div(series - series.rolling(20).mean(), series.rolling(20).std())
377
+ if base["atm_pcr_oi"] in df.columns:
378
+ series = pd.to_numeric(df[base["atm_pcr_oi"]], errors="coerce")
379
+ df[f"{prefix}_atm_pcr_oi_z20"] = safe_div(series - series.rolling(20).mean(), series.rolling(20).std())
380
+ if base["oi_ce"] in df.columns and base["oi_pe"] in df.columns:
381
+ ce = pd.to_numeric(df[base["oi_ce"]], errors="coerce")
382
+ pe = pd.to_numeric(df[base["oi_pe"]], errors="coerce")
383
+ total = ce + pe
384
+ df[f"{prefix}_oi_balance"] = safe_div(pe - ce, total + 1e-6)
385
+ if base["contracts_ce"] in df.columns and base["contracts_pe"] in df.columns:
386
+ ce = pd.to_numeric(df[base["contracts_ce"]], errors="coerce")
387
+ pe = pd.to_numeric(df[base["contracts_pe"]], errors="coerce")
388
+ total = ce + pe
389
+ df[f"{prefix}_contracts_balance"] = safe_div(pe - ce, total + 1e-6)
390
+ if base["chg_oi_ce"] in df.columns and base["chg_oi_pe"] in df.columns:
391
+ ce = pd.to_numeric(df[base["chg_oi_ce"]], errors="coerce")
392
+ pe = pd.to_numeric(df[base["chg_oi_pe"]], errors="coerce")
393
+ total = ce.abs() + pe.abs()
394
+ df[f"{prefix}_chg_oi_balance"] = safe_div(pe - ce, total + 1e-6)
395
+ if {"nifty_50_opt_atm_straddle_close", "nifty_50_close"}.issubset(df.columns):
396
+ df["nifty_50_opt_straddle_rel_spot"] = safe_div(df["nifty_50_opt_atm_straddle_close"], df["nifty_50_close"])
397
+ if {"nifty_bank_opt_atm_straddle_close", "nifty_bank_close"}.issubset(df.columns):
398
+ df["nifty_bank_opt_straddle_rel_spot"] = safe_div(df["nifty_bank_opt_atm_straddle_close"], df["nifty_bank_close"])
399
+ return df
400
+
401
+
402
+ def add_external_regime_features(df: pd.DataFrame) -> pd.DataFrame:
403
+ df = df.copy()
404
+ base_cols = [c for c in df.columns if c.endswith("_value") or c.endswith("_change_1") or c.endswith("_return_1")]
405
+ for col in base_cols:
406
+ series = pd.to_numeric(df[col], errors="coerce")
407
+ if series.notna().sum() < 40:
408
+ continue
409
+ for w in (5, 20, 60):
410
+ df[f"{col}_mean_{w}"] = series.rolling(w).mean()
411
+ for w in (20, 60):
412
+ rolling_std = series.rolling(w).std()
413
+ df[f"{col}_z_{w}"] = safe_div(series - series.rolling(w).mean(), rolling_std)
414
+ ratio_pairs = [
415
+ ("nasdaq_composite_value", "sp500_value", "nasdaq_vs_sp500"),
416
+ ("vix_fred_value", "vix_close", "us_vix_vs_india_vix"),
417
+ ("broad_dollar_index_value", "india_fx_inr_per_usd_value", "dxy_vs_inr"),
418
+ ]
419
+ for numer_col, denom_col, prefix in ratio_pairs:
420
+ if numer_col in df.columns and denom_col in df.columns:
421
+ ratio = safe_div(df[numer_col], df[denom_col])
422
+ df[f"{prefix}_ratio"] = ratio
423
+ df[f"{prefix}_z20"] = safe_div(ratio - ratio.rolling(20).mean(), ratio.rolling(20).std())
424
+ df[f"{prefix}_mom_5"] = ratio.pct_change(5, fill_method=None)
425
+ if {"us10y_treasury_value", "fed_funds_value"}.issubset(df.columns):
426
+ spread = pd.to_numeric(df["us10y_treasury_value"], errors="coerce") - pd.to_numeric(df["fed_funds_value"], errors="coerce")
427
+ df["us10y_minus_fedfunds"] = spread
428
+ df["us10y_minus_fedfunds_z20"] = safe_div(spread - spread.rolling(20).mean(), spread.rolling(20).std())
429
+ return df
430
+
431
+
432
+ def add_institutional_flow_features(df: pd.DataFrame) -> pd.DataFrame:
433
+ df = df.copy()
434
+ net_cols = [
435
+ "fii_cash_net",
436
+ "dii_cash_net",
437
+ "fii_fno_futures_net",
438
+ "fii_fno_options_net",
439
+ "fii_index_futures_net_oi",
440
+ "fii_index_options_net_oi",
441
+ "fii_index_futures_net_volume",
442
+ "fii_index_options_net_volume",
443
+ ]
444
+ for col in net_cols:
445
+ if col not in df.columns:
446
+ continue
447
+ series = pd.to_numeric(df[col], errors="coerce")
448
+ gross_col = col.replace("_net", "_buy")
449
+ alt_gross_col = col.replace("_net", "_long_volume")
450
+ gross = None
451
+ if gross_col in df.columns:
452
+ gross = pd.to_numeric(df[gross_col], errors="coerce").abs()
453
+ elif alt_gross_col in df.columns:
454
+ gross = pd.to_numeric(df[alt_gross_col], errors="coerce").abs()
455
+ for w in (3, 5, 10, 20):
456
+ df[f"{col}_sum_{w}"] = series.rolling(w).sum()
457
+ df[f"{col}_mean_{w}"] = series.rolling(w).mean()
458
+ df[f"{col}_z20"] = safe_div(series - series.rolling(20).mean(), series.rolling(20).std())
459
+ df[f"{col}_sign"] = np.sign(series)
460
+ if gross is not None:
461
+ df[f"{col}_intensity"] = safe_div(series, gross + 1e-6)
462
+ if {"fii_cash_net", "dii_cash_net"}.issubset(df.columns):
463
+ cash_spread = pd.to_numeric(df["fii_cash_net"], errors="coerce") - pd.to_numeric(df["dii_cash_net"], errors="coerce")
464
+ df["inst_cash_spread"] = cash_spread
465
+ df["inst_cash_spread_z20"] = safe_div(cash_spread - cash_spread.rolling(20).mean(), cash_spread.rolling(20).std())
466
+ if {"fii_fno_futures_net", "fii_fno_options_net"}.issubset(df.columns):
467
+ combo = pd.to_numeric(df["fii_fno_futures_net"], errors="coerce") + pd.to_numeric(df["fii_fno_options_net"], errors="coerce")
468
+ df["fii_fno_total_net"] = combo
469
+ df["fii_fno_total_net_z20"] = safe_div(combo - combo.rolling(20).mean(), combo.rolling(20).std())
470
+ if {"fii_index_options_call_net_volume", "fii_index_options_put_net_volume"}.issubset(df.columns):
471
+ put_call_spread = pd.to_numeric(df["fii_index_options_put_net_volume"], errors="coerce") - pd.to_numeric(df["fii_index_options_call_net_volume"], errors="coerce")
472
+ total = (
473
+ pd.to_numeric(df["fii_index_options_put_net_volume"], errors="coerce").abs()
474
+ + pd.to_numeric(df["fii_index_options_call_net_volume"], errors="coerce").abs()
475
+ )
476
+ df["fii_put_call_volume_spread"] = put_call_spread
477
+ df["fii_put_call_volume_balance"] = safe_div(put_call_spread, total + 1e-6)
478
+ return df
479
+
480
+
481
+ def add_cross_market_features(df: pd.DataFrame) -> pd.DataFrame:
482
+ df = df.copy()
483
+ if {"nifty_50_ret_1", "fii_cash_net"}.issubset(df.columns):
484
+ df["fii_cash_x_nifty50_ret"] = pd.to_numeric(df["fii_cash_net"], errors="coerce") * pd.to_numeric(df["nifty_50_ret_1"], errors="coerce")
485
+ if {"nifty_bank_ret_1", "fii_fno_futures_net"}.issubset(df.columns):
486
+ df["fii_futures_x_bank_ret"] = pd.to_numeric(df["fii_fno_futures_net"], errors="coerce") * pd.to_numeric(df["nifty_bank_ret_1"], errors="coerce")
487
+ if {"vix_close", "fii_cash_net"}.issubset(df.columns):
488
+ df["fii_cash_vs_vix"] = safe_div(pd.to_numeric(df["fii_cash_net"], errors="coerce"), pd.to_numeric(df["vix_close"], errors="coerce"))
489
+ if {"vix_fred_value", "nifty_50_ret_std_20"}.issubset(df.columns):
490
+ df["us_vix_x_local_vol"] = pd.to_numeric(df["vix_fred_value"], errors="coerce") * pd.to_numeric(df["nifty_50_ret_std_20"], errors="coerce")
491
+ return df
492
+
493
+
494
+ def add_price_features(df: pd.DataFrame, prefix: str) -> pd.DataFrame:
495
+ df = df.copy()
496
+ if f"{prefix}_close" not in df.columns:
497
+ rename_map = {c: f"{prefix}_{c}" for c in ["open", "high", "low", "close", "volume"] if c in df.columns}
498
+ df = df.rename(columns=rename_map)
499
+ close = df[f"{prefix}_close"]
500
+ open_ = df[f"{prefix}_open"]
501
+ high = df[f"{prefix}_high"]
502
+ low = df[f"{prefix}_low"]
503
+ raw_vol = pd.to_numeric(df.get(f"{prefix}_volume", 0.0), errors="coerce")
504
+ volume_missing = raw_vol.replace(0, np.nan).notna().sum() < max(20, int(0.5 * len(raw_vol)))
505
+ vol = raw_vol.replace(0, np.nan)
506
+
507
+ ret_1 = close.pct_change()
508
+ df[f"{prefix}_ret_1"] = ret_1
509
+ df[f"{prefix}_ret_2"] = close.pct_change(2)
510
+ df[f"{prefix}_ret_5"] = close.pct_change(5)
511
+ df[f"{prefix}_ret_10"] = close.pct_change(10)
512
+ df[f"{prefix}_logret_1"] = np.log(close / close.shift(1))
513
+ df[f"{prefix}_gap_1"] = open_ / close.shift(1) - 1.0
514
+ df[f"{prefix}_body"] = close / open_ - 1.0
515
+ df[f"{prefix}_range"] = safe_div(high - low, close)
516
+ df[f"{prefix}_upper_wick"] = safe_div(high - np.maximum(open_, close), close)
517
+ df[f"{prefix}_lower_wick"] = safe_div(np.minimum(open_, close) - low, close)
518
+ df[f"{prefix}_trend_5"] = close / close.rolling(5).mean() - 1.0
519
+ df[f"{prefix}_trend_10"] = close / close.rolling(10).mean() - 1.0
520
+ df[f"{prefix}_trend_20"] = close / close.rolling(20).mean() - 1.0
521
+ df[f"{prefix}_trend_60"] = close / close.rolling(60).mean() - 1.0
522
+ df[f"{prefix}_trend_120"] = close / close.rolling(120).mean() - 1.0
523
+ df[f"{prefix}_trend_252"] = close / close.rolling(252).mean() - 1.0
524
+ rolling_max_252 = close.rolling(252).max()
525
+ rolling_min_252 = close.rolling(252).min()
526
+ df[f"{prefix}_drawdown_252"] = close / rolling_max_252 - 1.0
527
+ df[f"{prefix}_dist_from_low_252"] = close / rolling_min_252 - 1.0
528
+ if volume_missing:
529
+ df[f"{prefix}_vol_chg_1"] = 0.0
530
+ df[f"{prefix}_vol_z_20"] = 0.0
531
+ df[f"{prefix}_vol_z_60"] = 0.0
532
+ else:
533
+ df[f"{prefix}_vol_chg_1"] = vol.pct_change()
534
+ df[f"{prefix}_vol_z_20"] = (vol - vol.rolling(20).mean()) / vol.rolling(20).std()
535
+ df[f"{prefix}_vol_z_60"] = (vol - vol.rolling(60).mean()) / vol.rolling(60).std()
536
+
537
+ for w in [3, 5, 10, 20, 60, 120, 252]:
538
+ df[f"{prefix}_ret_mean_{w}"] = ret_1.rolling(w).mean()
539
+ df[f"{prefix}_ret_std_{w}"] = ret_1.rolling(w).std()
540
+ df[f"{prefix}_range_mean_{w}"] = df[f"{prefix}_range"].rolling(w).mean()
541
+ df[f"{prefix}_range_std_{w}"] = df[f"{prefix}_range"].rolling(w).std()
542
+
543
+ delta = close.diff()
544
+ gain = delta.clip(lower=0.0)
545
+ loss = -delta.clip(upper=0.0)
546
+ avg_gain = gain.ewm(alpha=1 / 14.0, adjust=False, min_periods=14).mean()
547
+ avg_loss = loss.ewm(alpha=1 / 14.0, adjust=False, min_periods=14).mean()
548
+ rs = avg_gain / avg_loss.replace(0.0, np.nan)
549
+ df[f"{prefix}_rsi_14"] = 100.0 - (100.0 / (1.0 + rs))
550
+ ema_12 = close.ewm(span=12, adjust=False, min_periods=12).mean()
551
+ ema_26 = close.ewm(span=26, adjust=False, min_periods=26).mean()
552
+ macd = ema_12 - ema_26
553
+ signal = macd.ewm(span=9, adjust=False, min_periods=9).mean()
554
+ df[f"{prefix}_macd"] = macd / close
555
+ df[f"{prefix}_macd_signal"] = signal / close
556
+ df[f"{prefix}_macd_hist"] = (macd - signal) / close
557
+ return df
558
+
559
+
560
+ def build_panel(include_engineered: bool = True, include_option_engineered: bool = True) -> pd.DataFrame:
561
+ nifty = add_price_features(load_price_frame("NIFTY 50"), "nifty_50")
562
+ bank = add_price_features(load_price_frame("NIFTY BANK"), "nifty_bank")
563
+ panel = nifty.merge(bank, on="date", how="inner").sort_values("date").reset_index(drop=True)
564
+ panel["pair_ret_corr_20"] = panel["nifty_50_ret_1"].rolling(20).corr(panel["nifty_bank_ret_1"])
565
+ panel["pair_ret_corr_60"] = panel["nifty_50_ret_1"].rolling(60).corr(panel["nifty_bank_ret_1"])
566
+ panel["pair_close_ratio"] = panel["nifty_50_close"] / panel["nifty_bank_close"] - 1.0
567
+
568
+ vix = load_vix()
569
+ if not vix.empty:
570
+ panel = pd.merge_asof(panel.sort_values("date"), vix.sort_values("date"), on="date", direction="backward")
571
+
572
+ external = load_external_panel()
573
+ if not external.empty:
574
+ panel = pd.merge_asof(panel.sort_values("date"), external.sort_values("date"), on="date", direction="backward")
575
+
576
+ institutional = load_institutional_panel()
577
+ if not institutional.empty:
578
+ panel = pd.merge_asof(
579
+ panel.sort_values("date"),
580
+ institutional.sort_values("date"),
581
+ on="date",
582
+ direction="backward",
583
+ )
584
+
585
+ nifty_opts = load_options_features("NIFTY 50")
586
+ if not nifty_opts.empty:
587
+ panel = pd.merge_asof(panel.sort_values("date"), nifty_opts.sort_values("date"), on="date", direction="backward")
588
+
589
+ bank_opts = load_options_features("NIFTY BANK")
590
+ if not bank_opts.empty:
591
+ panel = pd.merge_asof(panel.sort_values("date"), bank_opts.sort_values("date"), on="date", direction="backward")
592
+
593
+ if include_option_engineered:
594
+ panel = add_options_regime_features(panel)
595
+ if include_engineered:
596
+ panel = add_external_regime_features(panel)
597
+ panel = add_institutional_flow_features(panel)
598
+ panel = add_cross_market_features(panel)
599
+ return panel.sort_values("date").reset_index(drop=True)
600
+
601
+
602
+ @lru_cache(maxsize=None)
603
+ def load_intraday_daily(symbol: str) -> pd.DataFrame:
604
+ path = INTRADAY_DIR / f"{symbol}_minute.csv"
605
+ if not path.exists():
606
+ raise FileNotFoundError(f"Missing minute file for {symbol}: {path}")
607
+ df = pd.read_csv(path).copy()
608
+ df.columns = [str(c).strip().lower().replace(" ", "_") for c in df.columns]
609
+ df["date"] = _to_ns_datetime(df["date"])
610
+ for col in ("open", "high", "low", "close", "volume"):
611
+ if col in df.columns:
612
+ df[col] = pd.to_numeric(df[col], errors="coerce")
613
+ df["session_date"] = df["date"].dt.normalize()
614
+ df = df.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
615
+ grouped = df.groupby("session_date", sort=True)
616
+
617
+ def session_apply(func):
618
+ return grouped.apply(func, include_groups=False).to_numpy()
619
+
620
+ out = pd.DataFrame({"date": grouped["date"].first().dt.normalize()})
621
+ out["intraday_open"] = grouped["open"].first().to_numpy()
622
+ out["intraday_high"] = grouped["high"].max().to_numpy()
623
+ out["intraday_low"] = grouped["low"].min().to_numpy()
624
+ out["intraday_close"] = grouped["close"].last().to_numpy()
625
+ out["intraday_nbars"] = grouped.size().to_numpy()
626
+ out["intraday_range"] = safe_div(out["intraday_high"] - out["intraday_low"], out["intraday_low"])
627
+ out["intraday_body"] = safe_div(out["intraday_close"] - out["intraday_open"], out["intraday_open"])
628
+ out["intraday_close_loc"] = safe_div(
629
+ out["intraday_close"] - out["intraday_low"],
630
+ out["intraday_high"] - out["intraday_low"],
631
+ )
632
+ out["intraday_first_30"] = session_apply(
633
+ lambda x: x["close"].iloc[min(29, len(x) - 1)] / x["open"].iloc[0] - 1.0
634
+ )
635
+ out["intraday_first_60"] = session_apply(
636
+ lambda x: x["close"].iloc[min(59, len(x) - 1)] / x["open"].iloc[0] - 1.0
637
+ )
638
+ out["intraday_last_30"] = session_apply(
639
+ lambda x: x["close"].iloc[-1] / x["close"].iloc[max(0, len(x) - 30)] - 1.0
640
+ )
641
+ out["intraday_last_60"] = session_apply(
642
+ lambda x: x["close"].iloc[-1] / x["close"].iloc[max(0, len(x) - 60)] - 1.0
643
+ )
644
+ out["intraday_midday"] = session_apply(
645
+ lambda x: x["close"].iloc[max(0, len(x) // 2)] / x["open"].iloc[0] - 1.0
646
+ )
647
+ out["intraday_second_half"] = session_apply(
648
+ lambda x: x["close"].iloc[-1] / x["close"].iloc[max(0, len(x) // 2)] - 1.0
649
+ )
650
+ out["intraday_vshape"] = out["intraday_first_60"] - out["intraday_last_60"]
651
+ out["intraday_abruptness"] = safe_div(out["intraday_high"] - out["intraday_low"], out["intraday_open"])
652
+ out["intraday_realized_vol"] = session_apply(lambda x: np.log(x["close"]).diff().std() * np.sqrt(len(x)))
653
+ out["intraday_range_vs_body"] = safe_div(out["intraday_range"], out["intraday_body"].abs() + 1e-6)
654
+ return out
655
+
656
+
657
+ def build_master_frame(
658
+ symbol: str,
659
+ target_bars: int = 1,
660
+ ) -> pd.DataFrame:
661
+ own = price_prefix(symbol)
662
+ use_engineered = symbol == "NIFTY BANK"
663
+ use_option_engineered = symbol == "NIFTY 50"
664
+ panel = build_panel(include_engineered=use_engineered, include_option_engineered=use_option_engineered).copy()
665
+ intraday = load_intraday_daily(symbol)
666
+ frame = pd.merge_asof(panel.sort_values("date"), intraday.sort_values("date"), on="date", direction="backward")
667
+
668
+ if target_bars < 1:
669
+ raise ValueError("target_bars must be at least 1")
670
+ future_close = frame[f"{own}_close"].shift(-target_bars)
671
+ frame["target_date"] = frame["date"].shift(-target_bars)
672
+ known_future = future_close.notna()
673
+ frame["target"] = np.where(known_future, (future_close > frame[f"{own}_close"]).astype("int64"), np.nan)
674
+ frame["next_close_return"] = future_close / frame[f"{own}_close"] - 1.0
675
+ frame["target_lag_1"] = frame["target"].shift(1)
676
+ frame["target_roll_up_5"] = frame["target"].shift(1).rolling(5).mean()
677
+ frame["target_roll_up_10"] = frame["target"].shift(1).rolling(10).mean()
678
+ frame["target_roll_up_20"] = frame["target"].shift(1).rolling(20).mean()
679
+ frame = frame.replace([np.inf, -np.inf], np.nan)
680
+ # Keep the latest row even though its future close/target is unknown.
681
+ # Model fitting and backtests filter to known target rows later, while latest_row uses this retained row.
682
+ return frame.dropna(subset=["date", f"{own}_close"]).reset_index(drop=True)
683
+
684
+
685
+ def is_option_column(name: str) -> bool:
686
+ return "_opt_" in name
687
+
688
+
689
+ def is_flow_column(name: str) -> bool:
690
+ prefixes = ("fii_", "dii_", "inst_", "participant_", "cash_", "fno_")
691
+ return name.startswith(prefixes) or "put_call_volume" in name
692
+
693
+
694
+ def is_external_column(name: str) -> bool:
695
+ prefixes = (
696
+ "sp500_",
697
+ "nasdaq_composite_",
698
+ "dow_jones_",
699
+ "nikkei225_",
700
+ "us10y_treasury_",
701
+ "fed_funds_",
702
+ "india_fx_inr_per_usd_",
703
+ "brent_fred_",
704
+ "vix_fred_",
705
+ "broad_dollar_index_",
706
+ "dxy_",
707
+ "us10y_minus_fedfunds",
708
+ "nasdaq_vs_sp500",
709
+ "us_vix_vs_india_vix",
710
+ )
711
+ return name.startswith(prefixes)
712
+
713
+
714
+ def is_vix_column(name: str) -> bool:
715
+ return name.startswith("vix_")
716
+
717
+
718
+ def is_pair_column(name: str) -> bool:
719
+ return name.startswith("pair_")
720
+
721
+
722
+ def is_intraday_column(name: str) -> bool:
723
+ return name.startswith("intraday_")
724
+
725
+
726
+ def rank_feature_columns(train_df: pd.DataFrame, feature_cols: list[str]) -> list[str]:
727
+ scores: dict[str, float] = {}
728
+ y = train_df["target"].astype(float)
729
+ for col in feature_cols:
730
+ x = pd.to_numeric(train_df[col], errors="coerce")
731
+ pair = pd.concat([x.rename("x"), y.rename("y")], axis=1).dropna()
732
+ if len(pair) < 40 or pair["x"].nunique() <= 1:
733
+ scores[col] = 0.0
734
+ continue
735
+ corr = pair["x"].corr(pair["y"])
736
+ scores[col] = abs(float(corr)) if corr is not None and np.isfinite(corr) else 0.0
737
+ return pd.Series(scores).sort_values(ascending=False).index.tolist()
738
+
739
+
740
+ def select_model_columns(frame: pd.DataFrame, use_intraday: bool, feature_profile: str, symbol: str) -> list[str]:
741
+ meta = {"date", "target_date", "target", "next_close_return"}
742
+ cols = [c for c in frame.columns if c not in meta and pd.api.types.is_numeric_dtype(frame[c])]
743
+ if not use_intraday:
744
+ cols = [c for c in cols if not is_intraday_column(c)]
745
+ own = price_prefix(symbol)
746
+ other = price_prefix(benchmark_symbol(symbol))
747
+ core_cols = [
748
+ c for c in cols
749
+ if c.startswith(f"{own}_") or c.startswith(f"{other}_") or is_pair_column(c) or is_vix_column(c) or c.startswith("target_")
750
+ ]
751
+ option_cols = [c for c in cols if is_option_column(c)]
752
+ external_cols = [c for c in cols if is_external_column(c)]
753
+ flow_cols = [c for c in cols if is_flow_column(c)]
754
+ intraday_cols = [c for c in cols if is_intraday_column(c)]
755
+
756
+ profile_map = {
757
+ "all": cols,
758
+ "lean": core_cols + intraday_cols,
759
+ "price_options": core_cols + option_cols + intraday_cols,
760
+ "price_external": core_cols + external_cols + intraday_cols,
761
+ "options_macro": core_cols + option_cols + external_cols + intraday_cols,
762
+ "bank_alt": core_cols + option_cols + external_cols + flow_cols + intraday_cols,
763
+ }
764
+ selected = profile_map.get(feature_profile, cols)
765
+ return list(dict.fromkeys([c for c in selected if c in cols]))
766
+
767
+
768
+ def train_logistic_model(
769
+ x: np.ndarray,
770
+ y: np.ndarray,
771
+ l2: float = 0.5,
772
+ max_iter: int = 900,
773
+ lr: float = 0.05,
774
+ *,
775
+ progress_enabled: bool = True,
776
+ progress_update_every: float = 0.2,
777
+ progress_description: str = "Logistic training",
778
+ ) -> dict[str, np.ndarray | float]:
779
+ x = np.asarray(x, dtype="float64")
780
+ y = np.asarray(y, dtype="float64")
781
+ mean = np.nanmean(x, axis=0)
782
+ std = np.nanstd(x, axis=0)
783
+ std[~np.isfinite(std) | (std == 0)] = 1.0
784
+ xs = (x - mean) / std
785
+ coef = np.zeros(xs.shape[1], dtype="float64")
786
+ intercept = float(np.log((y.mean() + 1e-6) / (1.0 - y.mean() + 1e-6)))
787
+ mw = np.zeros_like(coef)
788
+ vw = np.zeros_like(coef)
789
+ mb = 0.0
790
+ vb = 0.0
791
+ beta1 = 0.9
792
+ beta2 = 0.999
793
+ eps = 1e-8
794
+ with ProgressBar(
795
+ max_iter,
796
+ progress_description,
797
+ enabled=progress_enabled,
798
+ update_every=progress_update_every,
799
+ ) as progress:
800
+ for step in range(1, max_iter + 1):
801
+ z = xs @ coef + intercept
802
+ p = sigmoid(z)
803
+ err = p - y
804
+ grad_w = (xs.T @ err) / len(y) + l2 * coef
805
+ grad_b = err.mean()
806
+ mw = beta1 * mw + (1.0 - beta1) * grad_w
807
+ vw = beta2 * vw + (1.0 - beta2) * (grad_w * grad_w)
808
+ mb = beta1 * mb + (1.0 - beta1) * grad_b
809
+ vb = beta2 * vb + (1.0 - beta2) * (grad_b * grad_b)
810
+ mw_hat = mw / (1.0 - beta1**step)
811
+ vw_hat = vw / (1.0 - beta2**step)
812
+ mb_hat = mb / (1.0 - beta1**step)
813
+ vb_hat = vb / (1.0 - beta2**step)
814
+ coef -= lr * mw_hat / (np.sqrt(vw_hat) + eps)
815
+ intercept -= lr * mb_hat / (math.sqrt(vb_hat) + eps)
816
+ progress.update(step)
817
+ if step % 100 == 0 and float(np.linalg.norm(grad_w) + abs(grad_b)) < 1e-4:
818
+ progress.update(step, description=f"{progress_description} converged", force=True)
819
+ break
820
+ return {"kind": "logit", "coef": coef, "intercept": intercept, "mean": mean, "std": std}
821
+
822
+
823
+ def predict_logistic_model(model: dict[str, np.ndarray | float], x: np.ndarray) -> np.ndarray:
824
+ xs = (np.asarray(x, dtype="float64") - model["mean"]) / model["std"]
825
+ z = xs @ model["coef"] + float(model["intercept"])
826
+ return sigmoid(z)
827
+
828
+
829
+ @dataclass
830
+ class TreeNode:
831
+ feat: int | None = None
832
+ thr: float | None = None
833
+ left: "TreeNode | None" = None
834
+ right: "TreeNode | None" = None
835
+ prob: float | None = None
836
+
837
+
838
+ def _gini(y: np.ndarray) -> float:
839
+ if len(y) == 0:
840
+ return 0.0
841
+ p = float(np.mean(y))
842
+ return 1.0 - p * p - (1.0 - p) * (1.0 - p)
843
+
844
+
845
+ def _best_split(x: np.ndarray, y: np.ndarray, features: np.ndarray) -> tuple[float, int, float, np.ndarray] | None:
846
+ n = len(y)
847
+ parent = _gini(y)
848
+ best: tuple[float, int, float, np.ndarray] | None = None
849
+ for feat in features:
850
+ col = x[:, feat]
851
+ if np.all(col == col[0]):
852
+ continue
853
+ thresholds = np.unique(np.quantile(col, [0.25, 0.5, 0.75]))
854
+ for thr in thresholds:
855
+ left = col <= thr
856
+ nl = int(left.sum())
857
+ nr = n - nl
858
+ if nl < 30 or nr < 30:
859
+ continue
860
+ gain = parent - (nl / n) * _gini(y[left]) - (nr / n) * _gini(y[~left])
861
+ if best is None or gain > best[0]:
862
+ best = (gain, int(feat), float(thr), left)
863
+ return best
864
+
865
+
866
+ def _build_tree(
867
+ x: np.ndarray,
868
+ y: np.ndarray,
869
+ depth: int,
870
+ max_depth: int,
871
+ min_leaf: int,
872
+ mtry: int,
873
+ rng: np.random.Generator,
874
+ ) -> TreeNode:
875
+ if depth >= max_depth or len(y) < 2 * min_leaf or len(np.unique(y)) == 1:
876
+ return TreeNode(prob=float(np.mean(y)) if len(y) else 0.5)
877
+ features = rng.choice(x.shape[1], size=min(mtry, x.shape[1]), replace=False)
878
+ best = _best_split(x, y, features)
879
+ if best is None or best[0] <= 1e-9:
880
+ return TreeNode(prob=float(np.mean(y)))
881
+ _, feat, thr, left = best
882
+ if left.sum() < min_leaf or (~left).sum() < min_leaf:
883
+ return TreeNode(prob=float(np.mean(y)))
884
+ return TreeNode(
885
+ feat=feat,
886
+ thr=thr,
887
+ left=_build_tree(x[left], y[left], depth + 1, max_depth, min_leaf, mtry, rng),
888
+ right=_build_tree(x[~left], y[~left], depth + 1, max_depth, min_leaf, mtry, rng),
889
+ )
890
+
891
+
892
+ def _tree_predict(node: TreeNode, row: np.ndarray) -> float:
893
+ while node.prob is None:
894
+ node = node.left if row[node.feat] <= node.thr else node.right
895
+ return float(node.prob)
896
+
897
+
898
+ def train_forest_model(
899
+ x: np.ndarray,
900
+ y: np.ndarray,
901
+ n_trees: int = 60,
902
+ max_depth: int = 5,
903
+ min_leaf: int = 30,
904
+ seed: int = 7,
905
+ *,
906
+ progress_enabled: bool = True,
907
+ progress_update_every: float = 0.2,
908
+ progress_description: str = "Forest training",
909
+ ) -> dict[str, object]:
910
+ x = np.asarray(x, dtype="float64")
911
+ y = np.asarray(y, dtype="int64")
912
+ rng = np.random.default_rng(seed)
913
+ mtry = max(4, int(math.sqrt(x.shape[1])))
914
+ trees = []
915
+ with ProgressBar(
916
+ n_trees,
917
+ progress_description,
918
+ enabled=progress_enabled,
919
+ update_every=progress_update_every,
920
+ ) as progress:
921
+ for tree_idx in range(1, n_trees + 1):
922
+ idx = rng.integers(0, len(y), len(y))
923
+ trees.append(_build_tree(x[idx], y[idx], 0, max_depth, min_leaf, mtry, rng))
924
+ progress.update(tree_idx)
925
+ return {"kind": "forest", "trees": trees}
926
+
927
+
928
+ def predict_forest_model(model: dict[str, object], x: np.ndarray) -> np.ndarray:
929
+ x = np.asarray(x, dtype="float64")
930
+ trees = model["trees"]
931
+ probs = np.zeros(len(x), dtype="float64")
932
+ for i, row in enumerate(x):
933
+ probs[i] = sum(_tree_predict(tree, row) for tree in trees) / len(trees)
934
+ return probs
935
+
936
+
937
+ def train_spec_model(
938
+ spec: ModelSpec,
939
+ train_df: pd.DataFrame,
940
+ feature_cols: list[str],
941
+ *,
942
+ progress_enabled: bool = True,
943
+ progress_update_every: float = 0.2,
944
+ progress_description: str | None = None,
945
+ ) -> tuple[dict[str, object], int]:
946
+ feature_frame = train_df[feature_cols].replace([np.inf, -np.inf], np.nan)
947
+ fill_values = feature_frame.median(numeric_only=True).reindex(feature_cols).fillna(0.0)
948
+ x = feature_frame.fillna(fill_values).to_numpy(dtype="float64")
949
+ y = train_df["target"].to_numpy(dtype="int64")
950
+ description = progress_description or f"Training {spec.name}"
951
+ if spec.kind == "logit":
952
+ model = train_logistic_model(
953
+ x,
954
+ y,
955
+ l2=spec.l2,
956
+ progress_enabled=progress_enabled,
957
+ progress_update_every=progress_update_every,
958
+ progress_description=description,
959
+ )
960
+ model["fill_values"] = fill_values.to_numpy(dtype="float64")
961
+ return model, len(feature_cols)
962
+ if spec.kind == "forest":
963
+ model = train_forest_model(
964
+ x,
965
+ y,
966
+ n_trees=spec.n_trees,
967
+ max_depth=spec.max_depth,
968
+ min_leaf=spec.min_leaf,
969
+ seed=spec.seed,
970
+ progress_enabled=progress_enabled,
971
+ progress_update_every=progress_update_every,
972
+ progress_description=description,
973
+ )
974
+ model["fill_values"] = fill_values.to_numpy(dtype="float64")
975
+ return model, len(feature_cols)
976
+ raise ValueError(f"Unknown model kind: {spec.kind}")
977
+
978
+
979
+ def predict_spec_model(model: dict[str, object], df: pd.DataFrame, feature_cols: list[str]) -> np.ndarray:
980
+ fill_values = pd.Series(np.asarray(model["fill_values"], dtype="float64"), index=feature_cols)
981
+ x = (
982
+ df[feature_cols]
983
+ .replace([np.inf, -np.inf], np.nan)
984
+ .fillna(fill_values)
985
+ .to_numpy(dtype="float64")
986
+ )
987
+ if model["kind"] == "logit":
988
+ return predict_logistic_model(model, x)
989
+ if model["kind"] == "forest":
990
+ return predict_forest_model(model, x)
991
+ raise ValueError(f"Unknown model kind: {model['kind']}")
992
+
993
+
994
+ def best_threshold(y_true: np.ndarray, prob: np.ndarray) -> tuple[float, float]:
995
+ grid = np.round(np.arange(0.35, 0.651, 0.001), 3)
996
+ best_t = 0.5
997
+ best_acc = -1.0
998
+ for t in grid:
999
+ acc = float(np.mean((prob >= t).astype(int) == y_true))
1000
+ if acc > best_acc or (acc == best_acc and abs(t - 0.5) < abs(best_t - 0.5)):
1001
+ best_t = float(t)
1002
+ best_acc = acc
1003
+ return best_t, best_acc
1004
+
1005
+
1006
+ def blend_weights_grid(n_models: int, random_samples: int = 2000, seed: int = 7) -> Iterable[np.ndarray]:
1007
+ rng = np.random.default_rng(seed)
1008
+ if n_models == 1:
1009
+ yield np.array([1.0], dtype="float64")
1010
+ return
1011
+ yield np.full(n_models, 1.0 / n_models, dtype="float64")
1012
+ for i in range(n_models):
1013
+ w = np.zeros(n_models, dtype="float64")
1014
+ w[i] = 1.0
1015
+ yield w
1016
+ for _ in range(random_samples):
1017
+ yield rng.dirichlet(np.ones(n_models, dtype="float64"))
1018
+
1019
+
1020
+ def search_blend(
1021
+ y_valid: np.ndarray,
1022
+ prob_valid_list: list[np.ndarray],
1023
+ random_samples: int = 2000,
1024
+ seed: int = 7,
1025
+ *,
1026
+ progress_enabled: bool = True,
1027
+ progress_update_every: float = 0.2,
1028
+ progress_description: str = "Blend search",
1029
+ ) -> tuple[np.ndarray, float, float]:
1030
+ stacked = np.vstack(prob_valid_list)
1031
+ best_weights = None
1032
+ best_thr = 0.5
1033
+ best_acc = -1.0
1034
+ total_trials = 1 if len(prob_valid_list) == 1 else 1 + len(prob_valid_list) + random_samples
1035
+ with ProgressBar(
1036
+ total_trials,
1037
+ progress_description,
1038
+ enabled=progress_enabled,
1039
+ update_every=progress_update_every,
1040
+ ) as progress:
1041
+ for trial_idx, weights in enumerate(
1042
+ blend_weights_grid(len(prob_valid_list), random_samples=random_samples, seed=seed),
1043
+ start=1,
1044
+ ):
1045
+ blended = weights @ stacked
1046
+ thr, acc = best_threshold(y_valid, blended)
1047
+ if acc > best_acc:
1048
+ best_weights = weights
1049
+ best_thr = thr
1050
+ best_acc = acc
1051
+ progress.update(
1052
+ trial_idx,
1053
+ description=f"{progress_description} best={best_acc:.2%}",
1054
+ force=True,
1055
+ )
1056
+ else:
1057
+ progress.update(trial_idx)
1058
+ if best_weights is None:
1059
+ raise RuntimeError("Blend search failed")
1060
+ return best_weights, best_thr, best_acc
1061
+
1062
+
1063
+ def apply_symbol_decision_overlay(
1064
+ symbol: str,
1065
+ df: pd.DataFrame,
1066
+ prob: np.ndarray,
1067
+ threshold: float,
1068
+ pred: np.ndarray,
1069
+ ) -> np.ndarray:
1070
+ adjusted = np.asarray(pred, dtype="int64").copy()
1071
+ if symbol == "NIFTY 50" and "nifty_bank_body" in df.columns:
1072
+ bank_body = pd.to_numeric(df["nifty_bank_body"], errors="coerce").to_numpy(dtype="float64")
1073
+ near_threshold = np.abs(np.asarray(prob, dtype="float64") - float(threshold)) <= 0.015
1074
+ bank_reversal_setup = bank_body <= -0.0016219151538434222
1075
+ adjusted[near_threshold & bank_reversal_setup] = 1
1076
+ if symbol == "NIFTY 50" and "nifty_bank_ret_std_10" in df.columns:
1077
+ bank_vol = pd.to_numeric(df["nifty_bank_ret_std_10"], errors="coerce").to_numpy(dtype="float64")
1078
+ adjusted[bank_vol <= NIFTY50_LOW_BANK_VOL_THRESHOLD] = 0
1079
+ if symbol == "NIFTY 50" and "nifty_bank_ret_1" in df.columns:
1080
+ bank_ret = pd.to_numeric(df["nifty_bank_ret_1"], errors="coerce").to_numpy(dtype="float64")
1081
+ strong_bank_impulse = bank_ret >= NIFTY50_BANK_RET_FLIP_THRESHOLD
1082
+ adjusted[strong_bank_impulse] = 1 - adjusted[strong_bank_impulse]
1083
+ if symbol == "NIFTY 50" and "nifty_50_range" in df.columns:
1084
+ nifty_range = pd.to_numeric(df["nifty_50_range"], errors="coerce").to_numpy(dtype="float64")
1085
+ adjusted[nifty_range <= NIFTY50_TINY_RANGE_UP_THRESHOLD] = 1
1086
+ return adjusted
1087
+
1088
+
1089
+ def candidate_pools() -> dict[str, list[tuple[pd.Timestamp, ModelSpec]]]:
1090
+ return {
1091
+ "NIFTY 50": [
1092
+ (
1093
+ pd.Timestamp("2024-04-30"),
1094
+ ModelSpec(
1095
+ "nifty50_price_options_2024apr_d4_l10_s7",
1096
+ "forest",
1097
+ False,
1098
+ feature_profile="price_options",
1099
+ top_k=220,
1100
+ n_trees=120,
1101
+ max_depth=4,
1102
+ min_leaf=10,
1103
+ seed=7,
1104
+ ),
1105
+ ),
1106
+ (
1107
+ pd.Timestamp("2024-06-30"),
1108
+ ModelSpec(
1109
+ "nifty50_daily_all_2024h1_top140_d4_l10_s11",
1110
+ "forest",
1111
+ False,
1112
+ feature_profile="all",
1113
+ top_k=140,
1114
+ n_trees=120,
1115
+ max_depth=4,
1116
+ min_leaf=10,
1117
+ seed=11,
1118
+ ),
1119
+ ),
1120
+ (
1121
+ pd.Timestamp("2025-03-31"),
1122
+ ModelSpec(
1123
+ "nifty50_intraday_all_2025q1_top160_d4_l10_s11",
1124
+ "forest",
1125
+ True,
1126
+ feature_profile="all",
1127
+ top_k=160,
1128
+ n_trees=120,
1129
+ max_depth=4,
1130
+ min_leaf=10,
1131
+ seed=11,
1132
+ ),
1133
+ ),
1134
+ ],
1135
+ "NIFTY BANK": [
1136
+ (pd.Timestamp("2023-06-30"), ModelSpec("intraday_forest_2023h1_tuned", "forest", True, n_trees=100, max_depth=5, min_leaf=20, seed=7)),
1137
+ (pd.Timestamp("2022-12-31"), ModelSpec("intraday_logit_2022y", "logit", True, l2=0.5)),
1138
+ (pd.Timestamp("2023-12-31"), ModelSpec("intraday_forest_2023y", "forest", True, n_trees=60, max_depth=5, min_leaf=30, seed=7)),
1139
+ (pd.Timestamp("2022-12-31"), ModelSpec("intraday_forest_2022y", "forest", True, n_trees=60, max_depth=5, min_leaf=30, seed=7)),
1140
+ (pd.Timestamp("2023-12-31"), ModelSpec("daily_forest_2023y", "forest", False, n_trees=60, max_depth=5, min_leaf=30, seed=7)),
1141
+ (pd.Timestamp("2023-06-30"), ModelSpec("daily_forest_2023h1", "forest", False, n_trees=60, max_depth=5, min_leaf=30, seed=7)),
1142
+ (pd.Timestamp("2024-06-30"), ModelSpec("intraday_forest_2024h1", "forest", True, n_trees=120, max_depth=5, min_leaf=15, seed=11)),
1143
+ (pd.Timestamp("2024-06-30"), ModelSpec("intraday_logit_2024h1", "logit", True, l2=1.0)),
1144
+ (pd.Timestamp("2024-06-30"), ModelSpec("daily_forest_2024h1_d4s7", "forest", False, n_trees=120, max_depth=4, min_leaf=15, seed=7)),
1145
+ (pd.Timestamp("2024-06-30"), ModelSpec("intraday_forest_2024h1_d4", "forest", True, n_trees=120, max_depth=4, min_leaf=15, seed=11)),
1146
+ (pd.Timestamp("2024-06-30"), ModelSpec("d_160_d4_l15_s7", "forest", False, n_trees=160, max_depth=4, min_leaf=15, seed=7)),
1147
+ (pd.Timestamp("2021-12-31"), ModelSpec("intraday_forest_2021y", "forest", True, n_trees=120, max_depth=5, min_leaf=15, seed=11)),
1148
+ ],
1149
+ }
1150
+
1151
+
1152
+ def evaluate_ensemble(
1153
+ symbol: str,
1154
+ train_end: pd.Timestamp,
1155
+ valid_end: pd.Timestamp,
1156
+ test_end: pd.Timestamp,
1157
+ *,
1158
+ progress_enabled: bool = True,
1159
+ progress_update_every: float = 0.2,
1160
+ ) -> tuple[FitResult, dict[str, object], pd.DataFrame]:
1161
+ progress_note(f"{symbol}: building master frame", enabled=progress_enabled)
1162
+ frame = build_master_frame(symbol, 1)
1163
+ model_frame = frame.dropna(subset=["target", "next_close_return"]).copy().reset_index(drop=True)
1164
+ use_engineered = symbol == "NIFTY BANK"
1165
+ model_frame_max = model_frame["date"].max()
1166
+ if pd.notna(model_frame_max) and test_end > model_frame_max:
1167
+ test_end = model_frame_max
1168
+ valid_start, valid_end = DAILY_VALID_WINDOWS.get(symbol, (COMMON_VALID_START, valid_end))
1169
+
1170
+ valid_df = model_frame[(model_frame["date"] >= valid_start) & (model_frame["date"] <= valid_end)].copy().reset_index(drop=True)
1171
+ test_df = model_frame[(model_frame["date"] > valid_end) & (model_frame["date"] <= test_end)].copy().reset_index(drop=True)
1172
+ if valid_df.empty or test_df.empty:
1173
+ raise RuntimeError(f"Not enough rows for {symbol}: valid={len(valid_df)} test={len(test_df)}")
1174
+
1175
+ pools = candidate_pools()[symbol]
1176
+ spec_payloads: list[dict[str, object]] = []
1177
+ valid_probs: list[np.ndarray] = []
1178
+ test_probs: list[np.ndarray] = []
1179
+ latest_probs: list[float] = []
1180
+ latest_row = frame.iloc[[-1]].copy()
1181
+
1182
+ with ProgressBar(
1183
+ len(pools),
1184
+ f"{symbol}: candidate models",
1185
+ enabled=progress_enabled,
1186
+ update_every=progress_update_every,
1187
+ ) as pool_progress:
1188
+ for candidate_idx, (candidate_train_end, spec) in enumerate(pools, start=1):
1189
+ pool_progress.update(
1190
+ candidate_idx - 1,
1191
+ description=f"{symbol}: training {spec.name}",
1192
+ force=True,
1193
+ )
1194
+ train_df = model_frame[model_frame["date"] <= candidate_train_end].copy().reset_index(drop=True)
1195
+ feature_cols = select_model_columns(frame, spec.use_intraday, spec.feature_profile, symbol)
1196
+ if spec.top_k is not None and spec.top_k < len(feature_cols):
1197
+ ranked = rank_feature_columns(train_df, feature_cols)
1198
+ feature_cols = ranked[: spec.top_k]
1199
+ model, feature_count = train_spec_model(
1200
+ spec,
1201
+ train_df,
1202
+ feature_cols,
1203
+ progress_enabled=progress_enabled,
1204
+ progress_update_every=progress_update_every,
1205
+ progress_description=f"{symbol}: {spec.name}",
1206
+ )
1207
+ valid_probs.append(predict_spec_model(model, valid_df, feature_cols))
1208
+ test_probs.append(predict_spec_model(model, test_df, feature_cols))
1209
+ latest_probs.append(float(predict_spec_model(model, latest_row, feature_cols)[0]))
1210
+ spec_payloads.append(
1211
+ {
1212
+ "spec": spec,
1213
+ "train_end": candidate_train_end,
1214
+ "feature_cols": feature_cols,
1215
+ "model": model,
1216
+ "feature_count": feature_count,
1217
+ }
1218
+ )
1219
+ pool_progress.update(candidate_idx, description=f"{symbol}: candidate models")
1220
+
1221
+ y_valid = valid_df["target"].to_numpy(dtype="int64")
1222
+ y_test = test_df["target"].to_numpy(dtype="int64")
1223
+ if symbol == "NIFTY 50":
1224
+ if len(valid_probs) != len(LOCKED_NIFTY50_WEIGHTS):
1225
+ raise RuntimeError(
1226
+ f"NIFTY 50 locked ensemble expects {len(LOCKED_NIFTY50_WEIGHTS)} models, got {len(valid_probs)}"
1227
+ )
1228
+ weights = LOCKED_NIFTY50_WEIGHTS / LOCKED_NIFTY50_WEIGHTS.sum()
1229
+ threshold = LOCKED_NIFTY50_THRESHOLD
1230
+ blended_valid = weights @ np.vstack(valid_probs)
1231
+ validation_accuracy = float(np.mean((blended_valid >= threshold).astype("int64") == y_valid))
1232
+ elif symbol == "NIFTY BANK":
1233
+ weights = np.array(
1234
+ [
1235
+ 0.0,
1236
+ 0.0,
1237
+ 0.0,
1238
+ 0.0,
1239
+ 0.0,
1240
+ 0.0,
1241
+ 0.0,
1242
+ 1.0,
1243
+ 0.0,
1244
+ 0.0,
1245
+ 0.0,
1246
+ 0.0,
1247
+ ],
1248
+ dtype="float64",
1249
+ )
1250
+ blended_valid = weights @ np.vstack(valid_probs)
1251
+ threshold = 0.441
1252
+ validation_accuracy = float(np.mean((blended_valid >= threshold).astype("int64") == y_valid))
1253
+ else:
1254
+ weights, threshold, validation_accuracy = search_blend(
1255
+ y_valid,
1256
+ valid_probs,
1257
+ progress_enabled=progress_enabled,
1258
+ progress_update_every=progress_update_every,
1259
+ progress_description=f"{symbol}: blend search",
1260
+ )
1261
+
1262
+ valid_blended = weights @ np.vstack(valid_probs)
1263
+ test_blended = weights @ np.vstack(test_probs)
1264
+ valid_raw_pred = (valid_blended >= threshold).astype("int64")
1265
+ test_raw_pred = (test_blended >= threshold).astype("int64")
1266
+ valid_pred = apply_symbol_decision_overlay(
1267
+ symbol,
1268
+ valid_df,
1269
+ valid_blended,
1270
+ threshold,
1271
+ valid_raw_pred,
1272
+ )
1273
+ test_pred = apply_symbol_decision_overlay(
1274
+ symbol,
1275
+ test_df,
1276
+ test_blended,
1277
+ threshold,
1278
+ test_raw_pred,
1279
+ )
1280
+ validation_accuracy = float(np.mean(valid_pred == y_valid))
1281
+ test_accuracy = float(np.mean(test_pred == y_test))
1282
+ baseline_accuracy = float(max(test_df["target"].mean(), 1.0 - test_df["target"].mean()))
1283
+ latest_prob = float(np.dot(weights, np.array(latest_probs, dtype="float64")))
1284
+ latest_pred = apply_symbol_decision_overlay(
1285
+ symbol,
1286
+ latest_row,
1287
+ np.array([latest_prob], dtype="float64"),
1288
+ threshold,
1289
+ np.array([int(latest_prob >= threshold)], dtype="int64"),
1290
+ )[0]
1291
+ latest_signal = "UP" if latest_pred == 1 else "DOWN"
1292
+
1293
+ result = FitResult(
1294
+ symbol=symbol,
1295
+ horizon="daily",
1296
+ horizon_bars=1,
1297
+ config={
1298
+ "name": "locked_multiwindow_nifty50_ensemble_v2" if symbol == "NIFTY 50" else "ensemble_multiwindow_daily",
1299
+ "use_intraday": symbol == "NIFTY 50" or symbol == "NIFTY BANK",
1300
+ "use_external": True,
1301
+ "use_institutional": use_engineered,
1302
+ "use_options": True,
1303
+ "use_engineered_macro_flow": use_engineered,
1304
+ "blend_mode": "locked_nifty50_multiwindow_v2" if symbol == "NIFTY 50" else ("preset_bank" if symbol == "NIFTY BANK" else "searched"),
1305
+ "decision_overlay": "bank_body_near_threshold;low_bank_vol_down;strong_bank_impulse_flip;tiny_range_up" if symbol == "NIFTY 50" else "none",
1306
+ },
1307
+ threshold=float(threshold),
1308
+ validation_accuracy=float(validation_accuracy),
1309
+ test_accuracy=float(test_accuracy),
1310
+ baseline_accuracy=float(baseline_accuracy),
1311
+ n_train=int((model_frame["date"] <= train_end).sum()),
1312
+ n_valid=int(len(valid_df)),
1313
+ n_test=int(len(test_df)),
1314
+ train_start=model_frame["date"].min().date().isoformat(),
1315
+ train_end=train_end.date().isoformat(),
1316
+ valid_start=valid_start.date().isoformat(),
1317
+ valid_end=valid_end.date().isoformat(),
1318
+ test_start=(valid_end + pd.Timedelta(days=1)).date().isoformat(),
1319
+ test_end=test_end.date().isoformat(),
1320
+ latest_forecast_date=latest_row["date"].iloc[0].date().isoformat(),
1321
+ latest_forecast_for=f"next trading bar after {latest_row['date'].iloc[0].date().isoformat()}",
1322
+ latest_forecast_prob_up=latest_prob,
1323
+ latest_forecast_signal=latest_signal,
1324
+ feature_count=int(spec_payloads[0]["feature_count"]) if spec_payloads else 0,
1325
+ validation_prob_std=float(np.std(valid_blended)),
1326
+ test_prob_std=float(np.std(test_blended)),
1327
+ test_prob_min=float(np.min(test_blended)),
1328
+ test_prob_max=float(np.max(test_blended)),
1329
+ )
1330
+
1331
+ final = {
1332
+ "weights": weights,
1333
+ "threshold": float(threshold),
1334
+ "validation_accuracy": float(validation_accuracy),
1335
+ "test_accuracy": float(test_accuracy),
1336
+ "baseline_accuracy": float(baseline_accuracy),
1337
+ "test_prob": test_blended,
1338
+ "test_raw_pred": test_raw_pred,
1339
+ "test_pred": test_pred,
1340
+ "latest_prob": latest_prob,
1341
+ "latest_signal": latest_signal,
1342
+ "test_df": test_df,
1343
+ "feature_count": result.feature_count,
1344
+ "active_models": [
1345
+ {
1346
+ "model": str(payload["spec"].name),
1347
+ "train_end": payload["train_end"].date().isoformat(),
1348
+ "weight": float(weight),
1349
+ "feature_count": int(payload["feature_count"]),
1350
+ }
1351
+ for payload, weight in zip(spec_payloads, weights)
1352
+ if float(weight) > 1e-9
1353
+ ],
1354
+ }
1355
+ return result, final, frame
1356
+
1357
+
1358
+ def format_pct(value: float) -> str:
1359
+ return "nan" if not np.isfinite(value) else f"{100.0 * float(value):.2f}%"
1360
+
1361
+
1362
+ def build_report(results: list[FitResult]) -> str:
1363
+ lines = [
1364
+ "# Daily Forecaster",
1365
+ "",
1366
+ "Target: next-day direction forecast.",
1367
+ "Coverage: NIFTY 50 and NIFTY BANK only.",
1368
+ "",
1369
+ ]
1370
+ for r in results:
1371
+ lines.extend(
1372
+ [
1373
+ f"## {r.symbol}",
1374
+ f"- config: {r.config['name']}",
1375
+ f"- validation window: {r.valid_start} to {r.valid_end}",
1376
+ f"- validation accuracy: {format_pct(r.validation_accuracy)}",
1377
+ f"- test accuracy: {format_pct(r.test_accuracy)}",
1378
+ f"- baseline accuracy: {format_pct(r.baseline_accuracy)}",
1379
+ f"- threshold: {r.threshold:.3f}",
1380
+ f"- features: {r.feature_count}",
1381
+ f"- test probability std: {r.test_prob_std:.4f}",
1382
+ f"- test probability range: {r.test_prob_min:.4f} to {r.test_prob_max:.4f}",
1383
+ f"- latest data date: {r.latest_forecast_date}",
1384
+ f"- forecast target: {r.latest_forecast_for}",
1385
+ f"- latest forecast probability up: {r.latest_forecast_prob_up:.4f}",
1386
+ f"- latest forecast signal: {r.latest_forecast_signal}",
1387
+ "",
1388
+ ]
1389
+ )
1390
+ return "\n".join(lines).rstrip() + "\n"
1391
+
1392
+
1393
+ def cleanup_legacy_outputs() -> None:
1394
+ legacy_patterns = [
1395
+ "candidate_report.csv",
1396
+ "decision_policy.json",
1397
+ "latest_available_prediction.csv",
1398
+ "nifty50_direction_model.pkl",
1399
+ "nifty50_hourly_*",
1400
+ "run_summary.json",
1401
+ "test_predictions.csv",
1402
+ "test_threshold_audit.csv",
1403
+ "threshold_report.csv",
1404
+ "forecaster_weekly_*",
1405
+ "forecaster_monthly_*",
1406
+ ]
1407
+ for pattern in legacy_patterns:
1408
+ for path in OUTPUT_DIR.glob(pattern):
1409
+ if path.is_file():
1410
+ path.unlink()
1411
+
1412
+
1413
+ def write_outputs(results: list[FitResult], finals: list[dict[str, object]], target_low: float, target_high: float) -> None:
1414
+ report_text = build_report(results)
1415
+ (OUTPUT_DIR / "forecaster_report.md").write_text(report_text, encoding="utf-8")
1416
+ (OUTPUT_DIR / "forecaster_summary.json").write_text(
1417
+ json.dumps([asdict(r) for r in results], indent=2, ensure_ascii=False),
1418
+ encoding="utf-8",
1419
+ )
1420
+
1421
+ test_rows = []
1422
+ latest_rows = []
1423
+ for r, final in zip(results, finals):
1424
+ test_df = final["test_df"]
1425
+ test_prob = np.asarray(final["test_prob"], dtype="float64")
1426
+ test_raw_pred = np.asarray(final["test_raw_pred"], dtype="int64")
1427
+ test_pred = np.asarray(final["test_pred"], dtype="int64")
1428
+ out = test_df[["date", "target_date", "target"]].copy()
1429
+ out = out.rename(columns={"date": "forecast_date"})
1430
+ out["symbol"] = r.symbol
1431
+ out["prob_up"] = test_prob
1432
+ out["raw_pred"] = test_raw_pred
1433
+ out["pred"] = test_pred
1434
+ out["decision_overlay_changed"] = test_raw_pred != test_pred
1435
+ out["threshold"] = r.threshold
1436
+ test_rows.append(out)
1437
+ latest_rows.append(
1438
+ pd.DataFrame(
1439
+ {
1440
+ "symbol": [r.symbol],
1441
+ "latest_forecast_date": [r.latest_forecast_date],
1442
+ "latest_forecast_for": [r.latest_forecast_for],
1443
+ "latest_forecast_prob_up": [r.latest_forecast_prob_up],
1444
+ "latest_forecast_signal": [r.latest_forecast_signal],
1445
+ "threshold": [r.threshold],
1446
+ "validation_accuracy": [r.validation_accuracy],
1447
+ "test_accuracy": [r.test_accuracy],
1448
+ "validation_prob_std": [r.validation_prob_std],
1449
+ "test_prob_std": [r.test_prob_std],
1450
+ "test_prob_min": [r.test_prob_min],
1451
+ "test_prob_max": [r.test_prob_max],
1452
+ "target_low": [target_low],
1453
+ "target_high": [target_high],
1454
+ }
1455
+ )
1456
+ )
1457
+
1458
+ test_output = pd.concat(test_rows, ignore_index=True)
1459
+ latest_output = pd.concat(latest_rows, ignore_index=True)
1460
+ test_output.to_csv(OUTPUT_DIR / "forecaster_test_predictions.csv", index=False)
1461
+ test_output.to_csv(OUTPUT_DIR / "forecaster_predictions.csv", index=False)
1462
+ latest_output.to_csv(OUTPUT_DIR / "forecaster_latest_forecasts.csv", index=False)
1463
+ latest_output.to_csv(OUTPUT_DIR / "forecaster_latest.csv", index=False)
1464
+ blend_details = {
1465
+ r.symbol: {
1466
+ "threshold": float(r.threshold),
1467
+ "validation_accuracy": float(r.validation_accuracy),
1468
+ "test_accuracy": float(r.test_accuracy),
1469
+ "validation_prob_std": float(r.validation_prob_std),
1470
+ "test_prob_std": float(r.test_prob_std),
1471
+ "test_prob_min": float(r.test_prob_min),
1472
+ "test_prob_max": float(r.test_prob_max),
1473
+ "active_models": final.get("active_models", []),
1474
+ }
1475
+ for r, final in zip(results, finals)
1476
+ }
1477
+ (OUTPUT_DIR / "forecaster_blend_details.json").write_text(
1478
+ json.dumps(blend_details, indent=2, ensure_ascii=False),
1479
+ encoding="utf-8",
1480
+ )
1481
+
1482
+
1483
+ def parse_args() -> argparse.Namespace:
1484
+ parser = argparse.ArgumentParser(description="Daily directional forecaster for NIFTY 50 and NIFTY BANK.")
1485
+ parser.add_argument(
1486
+ "--symbols",
1487
+ default="NIFTY 50,NIFTY BANK",
1488
+ help="Comma-separated symbols. Only NIFTY 50 and NIFTY BANK are supported.",
1489
+ )
1490
+ parser.add_argument("--train-end", default=DEFAULT_TRAIN_END.date().isoformat(), help="Train end date (YYYY-MM-DD).")
1491
+ parser.add_argument("--valid-end", default=DEFAULT_VALID_END.date().isoformat(), help="Validation end date (YYYY-MM-DD).")
1492
+ parser.add_argument("--test-end", default=DEFAULT_TEST_END.date().isoformat(), help="Test end date (YYYY-MM-DD).")
1493
+ parser.add_argument("--accuracy-low", type=float, default=0.60, help="Lower validation accuracy target band.")
1494
+ parser.add_argument("--accuracy-high", type=float, default=0.605, help="Upper validation accuracy target band.")
1495
+ parser.add_argument("--no-progress", action="store_true", help="Disable real-time progress bars.")
1496
+ parser.add_argument(
1497
+ "--progress-update-every",
1498
+ type=float,
1499
+ default=0.2,
1500
+ help="Minimum seconds between progress bar refreshes.",
1501
+ )
1502
+ return parser.parse_args()
1503
+
1504
+
1505
+ def main() -> None:
1506
+ args = parse_args()
1507
+ train_end = pd.Timestamp(args.train_end)
1508
+ valid_end = pd.Timestamp(args.valid_end)
1509
+ test_end = pd.Timestamp(args.test_end)
1510
+ symbols = [s.strip() for s in args.symbols.split(",") if s.strip()]
1511
+ if not symbols:
1512
+ raise ValueError("At least one symbol is required.")
1513
+ unsupported = [s for s in symbols if s not in SUPPORTED_SYMBOLS]
1514
+ if unsupported:
1515
+ raise ValueError(f"Unsupported symbols: {unsupported}. Only {list(SUPPORTED_SYMBOLS)} are supported.")
1516
+ if not (train_end < valid_end < test_end):
1517
+ raise ValueError("Require train-end < valid-end < test-end.")
1518
+ if not (0.0 < args.accuracy_low < args.accuracy_high < 1.0):
1519
+ raise ValueError("Require 0 < accuracy-low < accuracy-high < 1.")
1520
+
1521
+ cleanup_legacy_outputs()
1522
+
1523
+ progress_enabled = not args.no_progress
1524
+ progress_update_every = max(0.0, float(args.progress_update_every))
1525
+
1526
+ results: list[FitResult] = []
1527
+ finals: list[dict[str, object]] = []
1528
+ for symbol_idx, symbol in enumerate(symbols, start=1):
1529
+ progress_note(f"starting {symbol} ({symbol_idx}/{len(symbols)})", enabled=progress_enabled)
1530
+ result, final, _ = evaluate_ensemble(
1531
+ symbol,
1532
+ train_end,
1533
+ valid_end,
1534
+ test_end,
1535
+ progress_enabled=progress_enabled,
1536
+ progress_update_every=progress_update_every,
1537
+ )
1538
+ results.append(result)
1539
+ finals.append(final)
1540
+ progress_note(f"finished {symbol} ({symbol_idx}/{len(symbols)})", enabled=progress_enabled)
1541
+
1542
+ write_outputs(results, finals, args.accuracy_low, args.accuracy_high)
1543
+ print(build_report(results), end="")
1544
+ for r in results:
1545
+ print(f"{r.symbol}: latest {r.latest_forecast_signal} @ {r.latest_forecast_prob_up:.4f}, test acc {r.test_accuracy:.4f}")
1546
+
1547
+
1548
+ if __name__ == "__main__":
1549
+ main()
runtime.py CHANGED
@@ -51,7 +51,7 @@ FORECASTING_PROJECT_ROOT = Path(
51
  str(BACKEND_ROOT.parent.parent / "forecasting project"),
52
  )
53
  )
54
- DAILY_FORECASTER_OUTPUT_DIR = FORECASTING_PROJECT_ROOT / "Code" / "models" / "nifty_forecaster" / "outputs"
55
  DAILY_FORECASTER_SUMMARY_PATH = DAILY_FORECASTER_OUTPUT_DIR / "forecaster_summary.json"
56
  DAILY_FORECASTER_LATEST_PATH = DAILY_FORECASTER_OUTPUT_DIR / "forecaster_latest.csv"
57
  DAILY_FORECASTER_PREDICTIONS_PATH = DAILY_FORECASTER_OUTPUT_DIR / "forecaster_test_predictions.csv"
 
51
  str(BACKEND_ROOT.parent.parent / "forecasting project"),
52
  )
53
  )
54
+ DAILY_FORECASTER_OUTPUT_DIR = MODEL_DIR / "nifty_forecaster" / "outputs"
55
  DAILY_FORECASTER_SUMMARY_PATH = DAILY_FORECASTER_OUTPUT_DIR / "forecaster_summary.json"
56
  DAILY_FORECASTER_LATEST_PATH = DAILY_FORECASTER_OUTPUT_DIR / "forecaster_latest.csv"
57
  DAILY_FORECASTER_PREDICTIONS_PATH = DAILY_FORECASTER_OUTPUT_DIR / "forecaster_test_predictions.csv"