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AI-Stock-Predictor/Run Stock Model.ipynb
locker98 069c026d15 added code
2024-10-07 21:24:55 -04:00

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In [1]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pandas_ta as ta
import IPython
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import Dense
from keras.layers import TimeDistributed
from datetime import datetime, timedelta
import tensorflow as tf
import keras
from keras import optimizers
from keras.callbacks import History
from keras.models import Model
from keras.layers import Dense, Dropout, LSTM, Input, Activation, concatenate
import numpy as np

from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import layers

# Uncomment for Interactive Graphs
#%matplotlib widget


name = "GDX"
data = pd.read_csv("data/"+name + ".csv")
data.tail()

2024-10-07 19:52:55.791307: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-10-07 19:52:55.806858: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-10-07 19:52:55.811709: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-10-07 19:52:55.824455: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-10-07 19:52:56.419666: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Out[1]:
Open High Low Close Adj Close Volume Date
4608 39.80 40.22 39.77 40.09 40.09 27456000 24-09-13
4609 40.00 40.23 39.58 39.89 39.89 12162500 24-09-16
4610 39.70 40.08 39.31 39.49 39.49 17906900 24-09-17
4611 39.76 40.99 39.02 39.06 39.06 41241400 24-09-18
4612 40.10 40.25 39.26 39.72 39.72 22277500 24-09-19

Load Model

In [2]:
model = tf.keras.models.load_model("models/" + name + ".keras")


model.summary()

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1728345176.891277  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345176.933307  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345176.935466  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345176.938339  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345176.940357  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345176.942232  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345177.061726  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345177.063279  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1728345177.064609  163696 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-10-07 19:52:57.065904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5387 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050, pci bus id: 0000:2d:00.0, compute capability: 8.6

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 20, 128)        │         2,176 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 20, 64)         │         8,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 20, 32)         │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 20, 32)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 640)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_layer (Dense)             │ (None, 9)              │         5,769 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 54,845 (214.24 KB)

 Trainable params: 18,281 (71.41 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 36,564 (142.83 KB)
In [3]:
# Adding indicators
data['Diff_Close'] = data['Adj Close']-data.Open
data['RSI']=ta.rsi(data.Close, length=14)
data['EMAF']=ta.ema(data.Close, length=18)
data['EMAM']=ta.ema(data.Close, length=50)
data['EMAS']=ta.ema(data.Close, length=200)
data.ta.bbands(append=True)
data.ta.stoch(append=True)



data.reset_index(inplace = True)
pd.set_option('display.max_columns', None)

data.tail()
Out[3]:
index Open High Low Close Adj Close Volume Date Diff_Close RSI EMAF EMAM EMAS BBL_5_2.0 BBM_5_2.0 BBU_5_2.0 BBB_5_2.0 BBP_5_2.0 STOCHk_14_3_3 STOCHd_14_3_3
4608 4608 39.80 40.22 39.77 40.09 40.09 27456000 24-09-13 0.29 63.393660 38.020788 37.252686 33.997792 35.563316 38.134 40.704684 13.482372 0.880443 72.443237 48.919613
4609 4609 40.00 40.23 39.58 39.89 39.89 12162500 24-09-16 -0.11 61.923518 38.217547 37.356110 34.056421 36.311033 38.760 41.208967 12.636569 0.730710 91.645955 71.097146
4610 4610 39.70 40.08 39.31 39.49 39.49 17906900 24-09-17 -0.21 58.977635 38.351489 37.439792 34.110487 37.289938 39.214 41.138062 9.813140 0.571723 89.939111 84.676101
4611 4611 39.76 40.99 39.02 39.06 39.06 41241400 24-09-18 -0.70 55.899007 38.426069 37.503330 34.159736 38.764939 39.548 40.331061 3.960052 0.188402 77.535372 86.373479
4612 4612 40.10 40.25 39.26 39.72 39.72 22277500 24-09-19 -0.38 59.401965 38.562273 37.590258 34.215062 38.940183 39.650 40.359817 3.580413 0.549308 71.486727 79.653736
In [4]:
data_set = data.copy()
data_set.drop(['Volume', 'Close', 'Date'], axis=1, inplace=True)
data_set.tail()
Out[4]:
index Open High Low Adj Close Diff_Close RSI EMAF EMAM EMAS BBL_5_2.0 BBM_5_2.0 BBU_5_2.0 BBB_5_2.0 BBP_5_2.0 STOCHk_14_3_3 STOCHd_14_3_3
4608 4608 39.80 40.22 39.77 40.09 0.29 63.393660 38.020788 37.252686 33.997792 35.563316 38.134 40.704684 13.482372 0.880443 72.443237 48.919613
4609 4609 40.00 40.23 39.58 39.89 -0.11 61.923518 38.217547 37.356110 34.056421 36.311033 38.760 41.208967 12.636569 0.730710 91.645955 71.097146
4610 4610 39.70 40.08 39.31 39.49 -0.21 58.977635 38.351489 37.439792 34.110487 37.289938 39.214 41.138062 9.813140 0.571723 89.939111 84.676101
4611 4611 39.76 40.99 39.02 39.06 -0.70 55.899007 38.426069 37.503330 34.159736 38.764939 39.548 40.331061 3.960052 0.188402 77.535372 86.373479
4612 4612 40.10 40.25 39.26 39.72 -0.38 59.401965 38.562273 37.590258 34.215062 38.940183 39.650 40.359817 3.580413 0.549308 71.486727 79.653736
In [5]:
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(-1,1))

df_scaled = sc.fit_transform(data_set.to_numpy())
data_set_scaled_pd = pd.DataFrame(df_scaled, columns=data_set.columns.tolist())



data_set_scaled_pd['index'] = data_set['index']

data_set_scaled_pd.tail()
Out[5]:
index Open High Low Adj Close Diff_Close RSI EMAF EMAM EMAS BBL_5_2.0 BBM_5_2.0 BBU_5_2.0 BBB_5_2.0 BBP_5_2.0 STOCHk_14_3_3 STOCHd_14_3_3
4608 4608 0.004820 0.009989 0.021269 0.195898 0.420548 0.350430 -0.021347 -0.015309 -0.136385 -0.107757 -0.042160 -0.008961 -0.525940 0.761191 0.450564 -0.022171
4609 4609 0.012236 0.010359 0.014179 0.187526 0.365753 0.310595 -0.013510 -0.010960 -0.133631 -0.079099 -0.018426 0.009222 -0.556316 0.461328 0.839660 0.440119
4610 4610 0.001112 0.004809 0.004104 0.170783 0.352055 0.230773 -0.008174 -0.007441 -0.131091 -0.041580 -0.001213 0.006666 -0.657717 0.142935 0.805075 0.723172
4611 4611 0.003337 0.038476 -0.006716 0.152784 0.284932 0.147354 -0.005204 -0.004769 -0.128777 0.014953 0.011450 -0.022433 -0.867925 -0.624720 0.553744 0.758553
4612 4612 0.015944 0.011099 0.002239 0.180410 0.328767 0.242270 0.000222 -0.001113 -0.126178 0.021670 0.015317 -0.021396 -0.881560 0.098046 0.431183 0.618480
In [6]:
X = []
backcandles = 20


data_set_scaled = data_set_scaled_pd.to_numpy()

print("Length of Data", data_set_scaled.shape[0])

features = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

feature_count = len(features)

it = 0
for j in features:
    X.append([])
    for i in range(backcandles, data_set_scaled.shape[0]):
        X[it].append(data_set_scaled[i-backcandles:i, j])
    it += 1


X=np.moveaxis(X, [0], [2])

X = np.array(X)
#print(X)
print("X Shape:", X.shape)
print(X[-1][19])

Length of Data 4613
X Shape: (4593, 20, 16)
[ 0.00333704  0.03847577 -0.00671642  0.15278359  0.28493151  0.14735398
 -0.00520376 -0.00476876 -0.12877696  0.01495339  0.0114498  -0.02243267
 -0.86792546 -0.62472028  0.55374401  0.75855333]
In [7]:
y_pred = model.predict(X)
print("Y Predict Shape:", X.shape, len(y_pred)-1)

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1728345178.233869  163771 service.cc:146] XLA service 0x7a0588004680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1728345178.233901  163771 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 3050, Compute Capability 8.6
2024-10-07 19:52:58.246285: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-10-07 19:52:58.300650: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907
2024-10-07 19:52:59.745797: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_33', 36 bytes spill stores, 36 bytes spill loads

2024-10-07 19:53:00.087891: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_33', 360 bytes spill stores, 360 bytes spill loads


110/144 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step
I0000 00:00:1728345180.575803  163771 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
2024-10-07 19:53:02.168487: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_33', 84 bytes spill stores, 84 bytes spill loads

2024-10-07 19:53:02.252466: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_33', 396 bytes spill stores, 396 bytes spill loads


144/144 ━━━━━━━━━━━━━━━━━━━━ 5s 15ms/step
Y Predict Shape: (4593, 20, 16) 4592
In [8]:
start_date = datetime.strptime(data.Date[len(data.Date)-2], "%y-%m-%d")
next_dates = []

while len(next_dates) < 9:
    start_date += timedelta(days=1)
    
    # Skip Saturdays (5) and Sundays (6)
    if start_date.weekday() < 5:
        next_dates.append(start_date.strftime("%y-%m-%d"))



plt.figure(figsize=(10,5))

plt.plot(next_dates, y_pred[len(y_pred)-1], color='green', label='pred')
plt.legend()
plt.ion()
Out[8]:
<contextlib.ExitStack at 0x7a0649882350>
No description has been provided for this image
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