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c05ee75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | import numpy as np
import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose
import gradio as gr
import pickle
# Load Model
with open('stock_price_prediction_analysis_target_Trend_Next_Day.pkl', 'rb') as f:
model_1 = pickle.load(f)
with open('stock_price_prediction_analysis_target_Trend_Next_3_Days.pkl', 'rb') as f:
model_3 = pickle.load(f)
with open('stock_price_prediction_analysis_target_Trend_Next_7_Days.pkl', 'rb') as f:
model_7 = pickle.load(f)
with open('stock_price_prediction_analysis_target_Trend_Next_14_Days.pkl', 'rb') as f:
model_14 = pickle.load(f)
with open('stock_price_prediction_analysis_target_Trend_Next_30_Days.pkl', 'rb') as f:
model_30 = pickle.load(f)
# Feature Functions
def compute_rsi(series, period=14):
delta = series.diff(1)
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)
avg_gain = gain.rolling(window=period).mean()
avg_loss = loss.rolling(window=period).mean()
rs = avg_gain / avg_loss
rsi = 100 - (100 / (1 + rs))
return rsi
def compute_macd(series, short_period=12, long_period=26, signal_period=9):
short_ema = series.ewm(span=short_period, adjust=False).mean()
long_ema = series.ewm(span=long_period, adjust=False).mean()
macd = short_ema - long_ema
signal = macd.ewm(span=signal_period, adjust=False).mean()
return macd, signal
def compute_bollinger_bands(series, window=20, num_std=2):
sma = series.rolling(window=window).mean()
std = series.rolling(window=window).std()
upper_band = sma + (std * num_std)
lower_band = sma - (std * num_std)
return upper_band, lower_band
def compute_atr(df, window=14):
high_low = df['High'] - df['Low']
high_close = np.abs(df['High'] - df['Close'].shift(1))
low_close = np.abs(df['Low'] - df['Close'].shift(1))
tr = pd.concat([high_low, high_close, low_close], axis=1)
atr = tr.max(axis=1).rolling(window=window).mean()
return atr
def add_features(df):
df = df.copy()
df["Date"] = pd.to_datetime(df["Date"])
df = df.sort_values("Date").reset_index(drop=True)
# Time Features
df['Day_Of_Week'] = df['Date'].dt.dayofweek
df['Month'] = df['Date'].dt.month
df['Year'] = df['Date'].dt.year
df['Day_Of_Month'] = df['Date'].dt.day
df['Is_Month_Start'] = df['Date'].dt.is_month_start.astype(int)
df['Is_Month_End'] = df['Date'].dt.is_month_end.astype(int)
df['Is_Weakend'] = df['Date'].dt.weekday >= 5
df['Week_Of_Year'] = df['Date'].dt.isocalendar().week
df['Quarter'] = df['Date'].dt.quarter
# Lagged Price
df['Close_Lag_1'] = df['Close'].shift(1) # Previous day's closing price
df['Close_Lag_2'] = df['Close'].shift(7) # 7-day lag
df['Close_Lag_3'] = df['Close'].shift(14) # 14-day lag
# Returns
df['Log_Return_Lag_1'] = np.log(df['Close'] / df['Close'].shift(1))
df['Log_Return_Lag_7'] = np.log(df['Close'] / df['Close'].shift(7))
df['Log_Return_Lag_14'] = np.log(df['Close'] / df['Close'].shift(14))
df['Percent_Return'] = df['Close'].pct_change()
# Technical Features
df['SMA_5'] = df['Close'].rolling(window=5).mean()
df['SMA_10'] = df['Close'].rolling(window=10).mean()
df['SMA_20'] = df['Close'].rolling(window=20).mean()
df['SMA_50'] = df['Close'].rolling(window=50).mean()
df['SMA_100'] = df['Close'].rolling(window=100).mean()
df['SMA_200'] = df['Close'].rolling(window=200).mean()
df['EMA_5'] = df['Close'].ewm(span=5, adjust=False).mean()
df['EMA_10'] = df['Close'].ewm(span=10, adjust=False).mean()
df['EMA_20'] = df['Close'].ewm(span=20, adjust=False).mean()
df['EMA_50'] = df['Close'].ewm(span=50, adjust=False).mean()
df['EMA_100'] = df['Close'].ewm(span=100, adjust=False).mean()
df['EMA_200'] = df['Close'].ewm(span=200, adjust=False).mean()
df['Volatility'] = df['Log_Return_Lag_1'].rolling(window=14).std()
df['Return_MA_5'] = df['Log_Return_Lag_1'].rolling(window=5).mean()
df['Return_MA_10'] = df['Log_Return_Lag_1'].rolling(window=10).mean()
df['VMA_50'] = df['Volume'].rolling(window=50).mean()
df['RSI_14'] = compute_rsi(df['Close'], period=14)
df['MACD'], df['MACD_Signal'] = compute_macd(df['Close'])
df['Bollinger_Upper'], df['Bollinger_Lower'] = compute_bollinger_bands(df['Close'])
df['ATR_14'] = compute_atr(df, window=14)
# Seasonal Decomposition
if len(df) >= 60:
decomp = seasonal_decompose(df["Close"], model="multiplicative", period=30, extrapolate_trend="freq")
df["Trend"] = decomp.trend
df["Seasonality"] = decomp.seasonal
df["Residual"] = decomp.resid
else:
df["Trend"] = np.nan
df["Seasonality"] = np.nan
df["Residual"] = np.nan
return df
def get_data_from_csv(file):
df = pd.read_csv(file.name)
required = {'Date', 'Sentiment', 'Open', 'High', 'Low', 'Close', 'Volume'}
missing = []
for col in required:
if col not in df.columns:
missing.append(col)
if missing:
return pd.DataFrame([{'Error' : f'Missing Columns: {missing}'}])
df_new = add_features(df)
last_day_data = df_new.tail(1)
return last_day_data
def predict_next_days(file):
latest_data = get_data_from_csv(file)
pred_res = []
pred_model_1 = model_1.predict(latest_data)[0]
prob_model_1 = model_1.predict_proba(latest_data)[0, 1]
pred_res.append({'Day' : 'Next Day', 'Prediction' : pred_model_1, 'Prob(UP)' : prob_model_1})
pred_model_3 = model_3.predict(latest_data)[0]
prob_model_3 = model_3.predict_proba(latest_data)[0, 1]
pred_res.append({'Day' : 'Next 3 Days', 'Prediction' : pred_model_3, 'Prob(UP)' : prob_model_3})
pred_model_7 = model_7.predict(latest_data)[0]
prob_model_7 = model_7.predict_proba(latest_data)[0, 1]
pred_res.append({'Day' : 'Next 7 Days', 'Prediction' : pred_model_7, 'Prob(UP)' : prob_model_7})
pred_model_14 = model_14.predict(latest_data)[0]
prob_model_14 = model_14.predict_proba(latest_data)[0, 1]
pred_res.append({'Day' : 'Next 14 Days', 'Prediction' : pred_model_14, 'Prob(UP)' : prob_model_14})
pred_model_30 = model_30.predict(latest_data)[0]
prob_model_30 = model_30.predict_proba(latest_data)[0, 1]
pred_res.append({'Day' : 'Next 30 Days', 'Prediction' : pred_model_30, 'Prob(UP)' : prob_model_30})
return pd.DataFrame(pred_res)
# Interface
app = gr.Interface(
fn=predict_next_days,
inputs=gr.File(label="Upload CSV with Date, Sentiment, Open, High, Low, Close, Volume (>= 250 rows recommended)"),
outputs=gr.Dataframe(label="Predictions"),
description='Upload historical OHLCV data',
title="Stock Trend Predictor",
)
app.launch(share=True)
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