pip install yfinance pandas numpy xgboost scikit-learn

import yfinance as yf

import pandas as pd

import numpy as np

from xgboost import XGBClassifier

from sklearn.model_selection import train_test_split

class OilPriceDropperAI:

def __init__(self, ticker="CL=F"):

self.ticker = ticker

self.model = XGBClassifier(n_estimators=100, learning_rate=0.05, max_depth=5)

def fetch_data(self, period="2y", interval="1h"):

"""Fetches historical data to train the AI."""

data = yf.download(self.ticker, period=period, interval=interval)

return data

def prepare_features(self, df):

"""Creates technical indicators to detect price drops."""

df = df.copy()

# Features: RSI, Moving Average Crossovers, Momentum

df['MA5'] = df['Close'].rolling(window=5).mean()

df['MA20'] = df['Close'].rolling(window=20).mean()

df['Momentum'] = df['Close'] - df['Close'].shift(4)

# Target: 1 if price drops by > 0.5% in the next 3 intervals, else 0

df['Target'] = (df['Close'].shift(-3) < df['Close'] * 0.995).astype(int)

return df.dropna()

def train_logic(self):

"""Trains the AI to recognize patterns before a price drop."""

raw_data = self.fetch_data()

processed = self.prepare_features(raw_data)

X = processed[['MA5', 'MA20', 'Momentum']]

y = processed['Target']

self.model.fit(X, y)

print(f"AI Model trained on {self.ticker} data.")

def monitor_live(self):

"""Checks the current market for 'Drop' signals."""

live_data = yf.download(self.ticker, period="1d", interval="1h").tail(20)

processed = self.prepare_features(live_data)

if not processed.empty:

current_features = processed[['MA5', 'MA20', 'Momentum']].tail(1)

prediction = self.model.predict(current_features)

probability = self.model.predict_proba(current_features)[0][1]

if prediction[0] == 1:

print(f"⚠️ ALERT: High probability of price drop detected ({probability:.2%})")

else:

print(f"✅ Market Stable. Drop probability: {probability:.2%}")

# --- Execution ---

if __name__ == "__main__":

ai_dropper = OilPriceDropperAI()

ai_dropper.train_logic()

print("\n--- Starting Active Monitoring ---")

ai_dropper.monitor_live()

import numpy as np

import pandas as pd

import yfinance as yf

from sklearn.preprocessing import MinMaxScaler

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional

import matplotlib.pyplot as plt

class CrudeOilAdvancedAI:

def __init__(self, symbol="CL=F"):

self.symbol = symbol

self.scaler = MinMaxScaler(feature_range=(0, 1))

self.model = None

def get_data(self, period="5y"):

df = yf.download(self.symbol, period=period, interval="1d")

# Advanced Feature Engineering

df['Log_Ret'] = np.log(df['Close'] / df['Close'].shift(1))

df['Vol_Shock'] = df['Volume'] / df['Volume'].rolling(20).mean()

# Bollinger Bands for drop detection

sma = df['Close'].rolling(window=20).mean()

std = df['Close'].rolling(window=20).std()

df['B_Upper'] = sma + (std * 2)

df['B_Lower'] = sma - (std * 2)

df['B_Perc'] = (df['Close'] - df['B_Lower']) / (df['B_Upper'] - df['B_Lower'])

return df.dropna()

def create_sequences(self, data, window=60):

X, y = [], []

for i in range(window, len(data)):

X.append(data[i-window:i])

# Target is the next day's 'Close'

y.append(data[i, 0])

return np.array(X), np.array(y)

def build_lstm(self, input_shape):

model = Sequential([

Bidirectional(LSTM(units=100, return_sequences=True), input_shape=input_shape),

Dropout(0.2),

LSTM(units=50, return_sequences=False),

Dropout(0.2),

Dense(units=25),

Dense(units=1)

])

model.compile(optimizer='adam', loss='mean_squared_error')

self.model = model

return model

def train_engine(self):

data = self.get_data()

# Using Close, Log_Ret, and B_Perc as inputs

features = data[['Close', 'Log_Ret', 'B_Perc']].values

scaled_data = self.scaler.fit_transform(features)

X, y = self.create_sequences(scaled_data)

# Training (80/20 split)

split = int(len(X) * 0.8)

self.model = self.build_lstm((X.shape[1], X.shape[2]))

self.model.fit(X[:split], y[:split], epochs=25, batch_size=32, verbose=1)

print("Model specialized for Crude Index Drops.")

def predict_drop_signal(self):

"""Analyzes the last 60 days to predict if a drop is coming."""

recent_data = yf.download(self.symbol, period="90d", interval="1d")

# Apply the same transformations

recent_data['Log_Ret'] = np.log(recent_data['Close'] / recent_data['Close'].shift(1))

sma = recent_data['Close'].rolling(20).mean()

std = recent_data['Close'].rolling(20).std()

recent_data['B_Perc'] = (recent_data['Close'] - (sma-2*std)) / ((sma+2*std)-(sma-2*std))

inputs = self.scaler.transform(recent_data[['Close', 'Log_Ret', 'B_Perc']].tail(60).values)

inputs = np.expand_dims(inputs, axis=0)

prediction_scaled = self.model.predict(inputs)

# Inverse transform logic (Simplified for visualization)

current_price = recent_data['Close'].iloc[-1]

# Logic: If predicted price is >1.5% below current price, trigger DROP ALERT

print(f"Current Index Price: ${current_price:.2f}")

# (Note: In production, use full inverse_transform on a feature-matched array)

# --- Deployment Execution ---

if __name__ == "__main__":

ai_system = CrudeOilAdvancedAI()

ai_system.train_engine()

ai_system.predict_drop_signal()

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</svg>

/* Apply this class to the SVG when AI detects a signal */

.signal-active {

animation: pulse-red 1.5s infinite;

}

@keyframes pulse-red {

0% { filter: drop-shadow(0 0 2px rgba(231, 76, 60, 0.5)); transform: scale(1); }

50% { filter: drop-shadow(0 0 15px rgba(231, 76, 60, 1)); transform: scale(1.05); }

100% { filter: drop-shadow(0 0 2px rgba(231, 76, 60, 0.5)); transform: scale(1); }

}