Binary Trading AI Bot is a project idea aimed at developing an AI-powered bot for binary trading. The bot utilizes machine learning algorithms to predict the direction of the next candle (whether it will move up or down) with high accuracy.
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Run time error after running code in pycharm for this project #12
``import ccxt # Or platform-specific API library
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression # Example model
from tkinter import Tk, Label, Button # Example UI elements (replace with preferred library)
Data acquisition and feature engineering
def get_data(symbol, timeframe):
exchange = ccxt.binance() # Replace with your preferred exchange
data = exchange.fetch_ohlcv(symbol, timeframe)
df = pd.DataFrame(data, columns=['Open', 'High', 'Low', 'Close', 'Volume'])
Add technical indicators or other features as needed
return df
def prepare_features(df):
Feature engineering logic
features = df[['Open', 'Close', 'RSI']] # Example features
target = df['Close'].shift(-1) > df['Close'] # Predicting next candle direction (up or down)
return features, target
Model training and prediction
def train_model(features, target):
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2)
model = LogisticRegression() # Example model
model.fit(X_train, y_train)
return model
``import ccxt # Or platform-specific API library import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Example model from tkinter import Tk, Label, Button # Example UI elements (replace with preferred library)
Data acquisition and feature engineering
def get_data(symbol, timeframe): exchange = ccxt.binance() # Replace with your preferred exchange data = exchange.fetch_ohlcv(symbol, timeframe) df = pd.DataFrame(data, columns=['Open', 'High', 'Low', 'Close', 'Volume'])
Add technical indicators or other features as needed
def prepare_features(df):
Feature engineering logic
Model training and prediction
def train_model(features, target): X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2) model = LogisticRegression() # Example model model.fit(X_train, y_train) return model
def predict(model, features): predictions = model.predict(features) return predictions
NLP integration (optional)
def analyze_sentiment(text):
Implement NLP logic using a suitable library (e.g., NLTK)
Trading execution (replace with platform-specific logic)
def execute_trade(symbol,