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# FORECASTING EXPERT RF TOOLS
from datetime import datetime, timedelta
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from pydantic.v1 import BaseModel, Field
from langchain.tools import BaseTool
from typing import Optional, Type
from langchain.tools import StructuredTool
def forecasting_expert_rf_tools():
def RF_forecast(symbol,historical_data, train_days_ago, forecast_days):
"""Useful for forecasting a variable using ARIMA model.
Use historical 'Close' stock prices and get prediction.
Give prediction output.
Send mae_rf from the model to Evaluator.
"""
df=historical_data[['Close']]
df.index=pd.to_datetime(df.index)
df.index.names=['date']
end_date = datetime.now()
df=df.reset_index()
# Feature Engineering
df['day'] = df['date'].dt.day
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['lag1'] = df['Close'].shift(1)
df['lag2'] = df['Close'].shift(2)
df = df.dropna()
# Prepare the data
features = ['day','month', 'year', 'lag1', 'lag2']
X = df[features]
y = df['Close']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
# Initialize and train the model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Evaluate the model
mae_rf = mean_absolute_error(y_test, y_pred)
print(f'Mean Absolute Error: {mae_rf}')
# Forecast future values (next 12 months)
future_dates = pd.date_range(start=pd.to_datetime(end_date), end=pd.to_datetime(end_date)+ timedelta(days=forecast_days), freq='D')
future_df = pd.DataFrame(future_dates, columns=['date'])
future_df['day'] = future_df['date'].dt.day
future_df['month'] = future_df['date'].dt.month
future_df['year'] = future_df['date'].dt.year
future_df['lag1'] = df['Close'].iloc[-1]
future_df['lag2'] = df['Close'].iloc[-2]
# Use the last observed values for lag features
for i in range(1, len(future_df)):
future_df.loc[future_df.index[i], 'lag1'] = future_df.loc[future_df.index[i-1], 'Close'] if 'Close' in future_df.columns else future_df.loc[future_df.index[i-1], 'lag1']
future_df.loc[future_df.index[i], 'lag2'] = future_df.loc[future_df.index[i-1], 'lag1']
future_X = future_df[features]
future_df['Close'] = model.predict(future_X)
rf_prediction=future_df['Close']
# Print the forecasted values
return {"predicted_price": rf_prediction,"mae_rf": mae_rf}
class PredictStocksRFInput(BaseModel):
"""Input for Stock ticker check."""
stockticker: str = Field(..., description="Ticker symbol for stock or index")
days_ago: int = Field(..., description="Int number of days to look back")
class PredictStocksRFTool(BaseTool):
name = "Random_forest_forecast"
description = "Useful for forecasting stock prices using Random forest model."
def _run(self, stockticker: str, days_ago: int,historical_data: float, train_days_ago=int, forecast_days=int):
predicted_prices = RF_forecast(stockticker,historical_data, train_days_ago, forecast_days).predict_price
mae_rf= RF_forecast(stockticker,historical_data, train_days_ago, forecast_days).mae_rf
return {"rf_prediction":rf_prediction,"mae_rf":mae_rf}
def _arun(self, stockticker: str, days_ago: int,historical_data: float, train_days_ago=int, forecast_days=int):
raise NotImplementedError("This tool does not support async")
args_schema: Optional[Type[BaseModel]] = PredictStocksRFInput
tools_forecasting_expert_random_forest = [
StructuredTool.from_function(
func=PredictStocksRFTool,
args_schema=PredictStocksRFInput,
description="Function to predict stock prices with random forest model and to get mae_rf for the model.",
),
StructuredTool.from_function(
func=PredictStocksRFTool,
args_schema=PredictStocksRFInput,
description="Function to predict stock prices with random forest model and to get mae_rf for the model.",
),
]
return tools_forecasting_expert_random_forest |