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talib library python

singh ai answered on January 23, 2024 Popularity 6/10 Helpfulness 2/10

answer talib library python

talib library python

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import ccxt
import talib
import pandas as pd
import matplotlib.pyplot as plt

# Replace 'YOUR_API_KEY' and 'YOUR_API_SECRET' with your actual API key and secret
api_key = 'YOUR_API_KEY'
api_secret = 'YOUR_API_SECRET'

# Initialize Binance API
exchange = ccxt.binance({
    'apiKey': api_key,
    'secret': api_secret,
})

def fetch_historical_data(symbol, timeframe, limit):
    ohlcv = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
    ohlcv_df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
    ohlcv_df['timestamp'] = pd.to_datetime(ohlcv_df['timestamp'], unit='ms')
    ohlcv_df.set_index('timestamp', inplace=True)
    return ohlcv_df

def generate_signals(data):
    signals = pd.DataFrame(index=data.index)
    
    # RSI signal
    rsi_period = 14
    signals['rsi'] = talib.RSI(data['close'], timeperiod=rsi_period)
    
    # SMA crossover signal
    short_window = 10
    long_window = 30
    signals['short_mavg'] = data['close'].rolling(window=short_window, min_periods=1, center=False).mean()
    signals['long_mavg'] = data['close'].rolling(window=long_window, min_periods=1, center=False).mean()
    
    # Bollinger Bands signal
    bollinger_period = 20
    signals['upper_band'], signals['middle_band'], signals['lower_band'] = talib.BBANDS(data['close'], timeperiod=bollinger_period)
    
    # Generate signals
    signals['buy_signal'] = ((signals['rsi'] > 80) & (signals['short_mavg'] < signals['long_mavg']) & (data['close'] > signals['upper_band']))
    signals['sell_signal'] = ((signals['rsi'] < 30) | (signals['short_mavg'] > signals['long_mavg']) | (data['close'] < signals['lower_band']))
    
    return signals

# Example usage:
symbol = 'BTC/USDT'
timeframe = '1h'
limit = 100

# Fetch historical data
historical_data = fetch_historical_data(symbol, timeframe, limit)

# Generate signals
signals = generate_signals(historical_data)

# Print signals
print(signals[signals['buy_signal'] | signals['sell_signal']])

# Plot the signals
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(historical_data.index, historical_data['close'], label='Close Price')
ax.plot(signals[signals['buy_signal']].index, signals['upper_band'][signals['buy_signal']], '^', markersize=10, color='g', label='Buy Signal')
ax.plot(signals[signals['sell_signal']].index, signals['lower_band'][signals['sell_signal']], 'v', markersize=10, color='r', label='Sell Signal')

plt.title('Trading Signals')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()

Popularity 6/10 Helpfulness 2/10 Language python

Source: technical-analysis-library-in-python.readthedocs.io

Tags: python ta-lib

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Contributed on Jan 23 2024

singh ai

0 Answers Avg Quality 2/10

talib library python

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