Date
stringdate 2018-01-02 00:00:00
2024-12-30 00:00:00
| Close
float64 33.8
462
| High
float64 34.6
463
| Low
float64 33.8
459
| Open
float64 34.2
462
| Volume
int64 7.16M
427M
|
|---|---|---|---|---|---|
2018-01-02
| 40.341888
| 40.351258
| 39.639313
| 39.850088
| 102,223,600
|
2018-01-03
| 40.334862
| 40.878189
| 40.271633
| 40.40512
| 118,071,600
|
2018-01-04
| 40.522217
| 40.625262
| 40.299735
| 40.407462
| 89,738,400
|
2018-01-05
| 40.983574
| 41.070224
| 40.526901
| 40.618235
| 94,640,000
|
2018-01-08
| 40.831348
| 41.126429
| 40.732985
| 40.831348
| 82,271,200
|
2018-01-09
| 40.826668
| 40.997627
| 40.611212
| 40.87819
| 86,336,000
|
2018-01-10
| 40.817303
| 40.819647
| 40.515197
| 40.552668
| 95,839,600
|
2018-01-11
| 41.049137
| 41.098319
| 40.864127
| 40.887544
| 74,670,800
|
2018-01-12
| 41.473038
| 41.536271
| 41.135801
| 41.259922
| 101,672,400
|
2018-01-16
| 41.262264
| 42.011677
| 41.250554
| 41.66273
| 118,263,600
|
2018-01-17
| 41.943775
| 41.978903
| 40.999982
| 41.252906
| 137,547,200
|
2018-01-18
| 41.981236
| 42.177959
| 41.744703
| 42.006997
| 124,773,600
|
2018-01-19
| 41.79388
| 42.056174
| 41.547978
| 41.829008
| 129,700,400
|
2018-01-22
| 41.45195
| 41.634619
| 41.358275
| 41.522208
| 108,434,400
|
2018-01-23
| 41.461327
| 42.023389
| 41.409808
| 41.522219
| 130,756,400
|
2018-01-24
| 40.800911
| 41.522222
| 40.562034
| 41.510512
| 204,420,400
|
2018-01-25
| 40.072567
| 40.971863
| 39.936735
| 40.868818
| 166,116,000
|
2018-01-26
| 40.166252
| 40.281007
| 39.826675
| 40.281007
| 156,572,000
|
2018-01-29
| 39.334869
| 39.850091
| 39.126439
| 39.850091
| 202,561,600
|
2018-01-30
| 39.103012
| 39.196687
| 38.571396
| 38.765775
| 184,192,800
|
2018-01-31
| 39.210743
| 39.447279
| 38.992946
| 39.079596
| 129,915,600
|
2018-02-01
| 39.292706
| 39.489426
| 39.053829
| 39.149848
| 188,923,200
|
2018-02-02
| 37.587791
| 39.063201
| 37.494116
| 38.875847
| 346,375,200
|
2018-02-05
| 36.648678
| 38.379355
| 36.533923
| 37.259919
| 290,954,000
|
2018-02-06
| 38.180302
| 38.341895
| 36.065549
| 36.259929
| 272,975,200
|
2018-02-07
| 37.362965
| 38.266945
| 37.252898
| 38.194346
| 206,434,400
|
2018-02-08
| 36.334881
| 37.704905
| 36.306779
| 37.538627
| 217,562,000
|
2018-02-09
| 36.779301
| 37.127317
| 35.328446
| 36.934498
| 282,690,400
|
2018-02-12
| 38.260715
| 38.538187
| 37.03795
| 37.270746
| 243,278,000
|
2018-02-13
| 38.644012
| 38.740423
| 38.011467
| 38.082012
| 130,196,800
|
2018-02-14
| 39.356499
| 39.396473
| 38.300692
| 38.338313
| 162,579,600
|
2018-02-15
| 40.678024
| 40.701537
| 39.739788
| 39.925552
| 204,588,800
|
2018-02-16
| 40.546337
| 41.108341
| 40.391143
| 40.529879
| 160,704,400
|
2018-02-20
| 40.409958
| 40.976659
| 40.308843
| 40.456987
| 135,722,000
|
2018-02-21
| 40.226543
| 40.943738
| 40.212432
| 40.6404
| 149,886,400
|
2018-02-22
| 40.562801
| 40.903763
| 40.377037
| 40.398199
| 123,967,600
|
2018-02-23
| 41.268257
| 41.303528
| 40.807368
| 40.837938
| 135,249,600
|
2018-02-26
| 42.084198
| 42.182959
| 41.435195
| 41.468115
| 152,648,800
|
2018-02-27
| 41.947819
| 42.439274
| 41.893736
| 42.114774
| 155,712,400
|
2018-02-28
| 41.884323
| 42.47219
| 41.867865
| 42.15239
| 151,128,400
|
2018-03-01
| 41.150673
| 42.274674
| 40.60043
| 41.983091
| 195,208,000
|
2018-03-02
| 41.435204
| 41.456366
| 40.55105
| 40.633352
| 153,816,000
|
2018-03-05
| 41.578648
| 41.794982
| 41.037809
| 41.200061
| 113,605,600
|
2018-03-06
| 41.543369
| 41.914902
| 41.416392
| 41.834953
| 95,154,000
|
2018-03-07
| 41.157719
| 41.350541
| 40.979008
| 41.136556
| 126,814,000
|
2018-03-08
| 41.606869
| 41.649193
| 41.167145
| 41.263553
| 95,096,400
|
2018-03-09
| 42.321701
| 42.326405
| 41.712672
| 41.846707
| 128,740,800
|
2018-03-12
| 42.73085
| 42.888398
| 42.37578
| 42.394589
| 128,828,400
|
2018-03-13
| 42.319355
| 43.149423
| 42.147699
| 42.935438
| 126,774,000
|
2018-03-14
| 41.959572
| 42.448677
| 41.811428
| 42.401649
| 117,473,600
|
2018-03-15
| 42.008961
| 42.382847
| 41.872579
| 41.97369
| 90,975,200
|
2018-03-16
| 41.860817
| 42.119476
| 41.766756
| 42.008957
| 157,618,800
|
2018-03-19
| 41.221214
| 41.731482
| 40.835574
| 41.696212
| 133,787,200
|
2018-03-20
| 41.207111
| 41.57394
| 41.136567
| 41.207111
| 78,597,600
|
2018-03-21
| 40.273571
| 41.171829
| 40.271217
| 41.160071
| 148,219,600
|
2018-03-22
| 39.704521
| 40.60513
| 39.645735
| 39.974938
| 165,963,200
|
2018-03-23
| 38.785088
| 39.956117
| 38.785088
| 39.596343
| 164,115,200
|
2018-03-26
| 40.626301
| 40.7039
| 39.137822
| 39.521112
| 150,164,800
|
2018-03-27
| 39.584595
| 41.185943
| 39.250687
| 40.840277
| 163,690,400
|
2018-03-28
| 39.147213
| 39.979634
| 38.843875
| 39.328277
| 166,674,000
|
2018-03-29
| 39.452904
| 40.386436
| 39.245974
| 39.459958
| 153,594,000
|
2018-04-02
| 39.194241
| 39.725674
| 38.674569
| 39.184836
| 150,347,200
|
2018-04-03
| 39.596355
| 39.681008
| 38.770992
| 39.419995
| 121,112,000
|
2018-04-04
| 40.353519
| 40.447577
| 38.745117
| 38.770983
| 138,422,000
|
2018-04-05
| 40.633343
| 40.969601
| 40.464037
| 40.58161
| 107,732,800
|
2018-04-06
| 39.593994
| 40.558093
| 39.551666
| 40.203023
| 140,021,200
|
2018-04-09
| 39.986702
| 40.701546
| 39.939673
| 39.946727
| 116,070,800
|
2018-04-10
| 40.739166
| 40.915526
| 40.334714
| 40.68038
| 113,634,400
|
2018-04-11
| 40.548698
| 40.896715
| 40.374689
| 40.499316
| 89,726,400
|
2018-04-12
| 40.948444
| 41.150671
| 40.689782
| 40.776788
| 91,557,200
|
2018-04-13
| 41.087185
| 41.348198
| 40.880258
| 41.098943
| 100,497,200
|
2018-04-16
| 41.343494
| 41.430498
| 41.110698
| 41.157727
| 86,313,600
|
2018-04-17
| 41.912544
| 42.077146
| 41.482225
| 41.501038
| 106,421,600
|
2018-04-18
| 41.818493
| 42.048939
| 41.592754
| 41.811439
| 83,018,000
|
2018-04-19
| 40.633343
| 41.242372
| 40.600422
| 40.859082
| 139,235,200
|
2018-04-20
| 38.968513
| 40.26182
| 38.900319
| 40.116031
| 261,964,400
|
2018-04-23
| 38.855644
| 39.250689
| 38.585223
| 39.229527
| 146,062,000
|
2018-04-24
| 38.314812
| 39.111959
| 37.910359
| 38.956761
| 134,768,000
|
2018-04-25
| 38.481758
| 38.897969
| 38.190178
| 38.239557
| 113,528,400
|
2018-04-26
| 38.615788
| 38.970858
| 38.415911
| 38.592271
| 111,852,000
|
2018-04-27
| 38.169014
| 38.641658
| 37.771615
| 38.564059
| 142,623,200
|
2018-04-30
| 38.860344
| 39.330637
| 38.056143
| 38.124337
| 169,709,600
|
2018-05-01
| 39.763298
| 39.786811
| 38.862686
| 39.130753
| 214,277,600
|
2018-05-02
| 41.519859
| 41.797331
| 40.868502
| 41.20476
| 266,157,600
|
2018-05-03
| 41.595093
| 41.738532
| 41.018984
| 41.357596
| 136,272,800
|
2018-05-04
| 43.227013
| 43.325774
| 41.896082
| 41.914894
| 224,805,200
|
2018-05-07
| 43.539753
| 44.12997
| 43.443342
| 43.544453
| 169,805,600
|
2018-05-08
| 43.749039
| 43.789013
| 43.189388
| 43.499784
| 113,611,200
|
2018-05-09
| 44.057068
| 44.066472
| 43.553854
| 43.8666
| 92,844,800
|
2018-05-10
| 44.687271
| 44.76487
| 44.125271
| 44.146437
| 111,957,200
|
2018-05-11
| 44.517326
| 44.864325
| 44.248226
| 44.729777
| 104,848,800
|
2018-05-14
| 44.413441
| 44.739196
| 44.344987
| 44.616447
| 83,115,200
|
2018-05-15
| 44.0098
| 44.158515
| 43.693489
| 44.090057
| 94,780,800
|
2018-05-16
| 44.420536
| 44.486634
| 43.905941
| 43.922467
| 76,732,400
|
2018-05-17
| 44.139641
| 44.592863
| 43.990926
| 44.378054
| 69,176,000
|
2018-05-18
| 43.979122
| 44.333202
| 43.936634
| 44.18685
| 73,190,800
|
2018-05-21
| 44.290714
| 44.677842
| 44.120755
| 44.378053
| 73,603,200
|
2018-05-22
| 44.179764
| 44.585776
| 44.090062
| 44.467749
| 60,962,800
|
2018-05-23
| 44.463024
| 44.496071
| 43.849284
| 43.988558
| 80,233,600
|
2018-05-24
| 44.413441
| 44.576318
| 43.9555
| 44.559796
| 92,936,000
|
End of preview. Expand
in Data Studio
📊 Multi-Agent RL Trading System - Dataset
This dataset contains historical OHLCV (Open, High, Low, Close, Volume) data for AAPL, MSFT, and GOOGL, pre-processed for Reinforcement Learning based trading systems.
📁 Dataset Content
The dataset consists of CSV files downloaded via yfinance:
AAPL.csv: Apple Inc. daily data (Jan 2018 - Dec 2024).MSFT.csv: Microsoft Corp. daily data (Jan 2018 - Dec 2024).GOOGL.csv: Alphabet Inc. daily data (Jan 2018 - Dec 2024).
📝 Columns
| Column | Description |
|---|---|
| Date | Trading date (YYYY-MM-DD) |
| Open | Opening price |
| High | Highest price of the day |
| Low | Lowest price of the day |
| Close | Closing price (Adjusted for splits/dividends) |
| Volume | Number of shares traded |
⚙️ Usage
This data is designed to be fed into a Feature Engineering pipeline (calculating RSI, MACD, etc.) before being used by the TradingEnv.
import pandas as pd
# Load data
df = pd.read_csv("AAPL.csv", parse_dates=['Date'], index_col='Date')
print(df.head())
🔗 Related Models
- Trained Agents: AdityaaXD/Multi-Agent_Reinforcement_Learning_Trading_System_Models
- GitHub Repository: ADITYA-tp01/Multi-Agent-Reinforcement-Learning-Trading-System-Data
⚠️ Source
Data was sourced from Yahoo Finance API. Not intended for real financial advice or live trading decisions.
🛠️ Credits
Collected by Adityaraj Suman for the Multi-Agent RL Trading System project.
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