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import yfinance as yf
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.compose import ColumnTransformer
import joblib
import keras
import matplotlib.pyplot as plt
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
try:
# Disable all GPUS
tf.config.set_visible_devices([], 'GPU')
visible_devices = tf.config.get_visible_devices()
for device in visible_devices:
assert device.device_type != 'GPU'
except:
# Invalid device or cannot modify virtual devices once initialized.
pass
TEST_DAYS = 50
PERIOD = '5y'
INCLUDE_COMMODITIES = True
def preprocessBeforeScaling(df):
df['High'] = df['High'].pct_change() * 100
df['Low'] = df['Low'].pct_change() * 100
df['Open'] = df['Open'].pct_change() * 100
df['Close'] = df['Close'].pct_change() * 100
if INCLUDE_COMMODITIES:
df['gold_High'] = df['gold_High'].pct_change() * 100
df['gold_Low'] = df['gold_Low'].pct_change() * 100
df['gold_Open'] = df['gold_Open'].pct_change() * 100
df['gold_Close'] = df['gold_Close'].pct_change() * 100
df['crude_High'] = df['crude_High'].pct_change() * 100
df['crude_Low'] = df['crude_Low'].pct_change() * 100
df['crude_Open'] = df['crude_Open'].pct_change() * 100
df['crude_Close'] = df['crude_Close'].pct_change() * 100
return df
metrics = {
"TP": 0, "FP": 0, "TN": 0, "FN": 0
}
endpoint = keras.models.load_model('nifty_model_v3.h5')
try:
scaler
except NameError:
pkl = joblib.load('nifty_model_v3.pkl')
scaler = pkl['scaler']
today = yf.download(
tickers="^NSEI",
period=f'{TEST_DAYS}d',
interval='1d',
progress=False,
timeout=10
)
if INCLUDE_COMMODITIES:
gold = yf.download(
tickers="GC=F",
period=f'{TEST_DAYS}d',
interval='1d',
progress=False,
timeout=10
).add_prefix(prefix='gold_')
crude = yf.download(
tickers="CL=F",
period=f'{TEST_DAYS}d',
interval='1d',
progress=False,
timeout=10
).add_prefix(prefix='crude_')
today = pd.concat([today, gold, crude], axis=1)
today = today.drop(columns=['Adj Close', 'Volume', 'gold_Adj Close', 'gold_Volume', 'crude_Adj Close', 'crude_Volume'])
else:
today = today.drop(columns=['Adj Close', 'Volume'])
###
today = preprocessBeforeScaling(today)
today = today.drop(columns=['gold_Open', 'gold_High', 'gold_Low', 'crude_Open', 'crude_High', 'crude_Low'])
###
cnt_correct, cnt_wrong = 0, 0
for i in range(-TEST_DAYS,0):
df = today.iloc[i]
twr = today.iloc[i+1]['Close']
df = scaler.transform([df])
pred = endpoint.predict([df], verbose=0)
if twr > today.iloc[i]['Open']:
fact = "BULLISH"
else:
fact = "BEARISH"
if pred > 0.5:
out = "BEARISH"
else:
out = "BULLISH"
if out == fact:
cnt_correct += 1
if out == "BULLISH":
metrics["TP"] += 1
else:
metrics["TN"] += 1
else:
cnt_wrong += 1
if out == "BULLISH":
metrics["FN"] += 1
else:
metrics["FP"] += 1
print("{} Nifty Prediction -> Market may Close {} on {}! Actual -> {}, Prediction -> {}, Pred = {}".format(
today.iloc[i].name.strftime("%d-%m-%Y"),
out,
(today.iloc[i].name + pd.Timedelta(days=1)).strftime("%d-%m-%Y"),
fact,
"Correct" if fact == out else "Wrong",
str(np.round(pred[0][0], 2))
)
)
print("Correct: {}, Wrong: {}, Accuracy: {}".format(cnt_correct, cnt_wrong, cnt_correct/(cnt_correct+cnt_wrong)))
print(metrics) |