Building a Cryptocurrency Trading Strategy: Technical Analysis with Python and CCXT

Let’s break down the code into the specified parts:

Part 1: Importing Libraries

import pandas as pd
import ccxt
import talib
import pandas_ta as ta

Explanation: This part imports essential libraries for data manipulation and technical analysis.

Use Cases:

• pandas: Used for handling data in tabular format.
• ccxt: Provides a unified way to access cryptocurrency trading data from various exchanges.
• talib: Library for technical analysis indicators.
• pandas_ta: Extension of Pandas library with additional technical analysis indicators.

Part 2: Function to Fetch Data

def fetch_data(symbol, timeframe='1m', limit=599):
    exchange = ccxt.huobi()
    ohlcv = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
    df = pd.DataFrame(ohlcv, columns=['timestamp', 'Open', 'High', 'Low', 'Close', 'Volume'])
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    df.set_index('timestamp', inplace=True)
    return df

Explanation: This function fetches historical OHLCV data for a specified trading pair and time frame from the Huobi exchange and returns it as a Pandas DataFrame.

Use Cases:

• Analyzing historical price data for a specific cryptocurrency pair.
• Backtesting trading strategies using historical data.

Part 3: Function to Generate Signals

def generate_signals(data):
    # Calculate MACD
    data['macd_red'], data['macd_signal_blue'], candle_macd = talib.MACD(data['Close'], fastperiod=34, slowperiod=144, signalperiod=9)

    # Calculate RSI
    data['rsi'] = talib.RSI(data['Close'])

    # Calculate Bollinger Bands
    data['upper_band'], data['middle_band'], data['lower_band'] = talib.BBANDS(data['Close'])

    # Calculate Donchian Channel
    data['upper_dc'], data['lower_dc'] = talib.MAX(data['High'], timeperiod=20), talib.MIN(data['Low'], timeperiod=20)

    return data

Explanation: This function calculates various technical indicators like MACD, RSI, Bollinger Bands, and Donchian Channel and adds them as columns to the input DataFrame.

Use Cases:

• Identifying trends and momentum in the market using MACD.
• Assessing overbought or oversold conditions with RSI.
• Analyzing volatility with Bollinger Bands.
• Identifying breakout or breakdown levels with the Donchian Channel.

Part 4: Function to Analyze Signals

def analyze_signals(data):
    signals = pd.DataFrame(index=data.index)
    
    # MACD signals
    signals['macd_buy'] = (data['macd_red'] > data['macd_signal_blue'])
    signals['macd_sell'] = (data['macd_red'] < data['macd_signal_blue']) 
    
    # RSI signals
    signals['rsi_overbought'] = (data['rsi'] > 70)
    signals['rsi_oversold'] = (data['rsi'] < 30)
    signals['rsi_middle'] = (data['rsi'] > 30) & (data['rsi'] < 70)
    
    # Bollinger Bands signals
    signals['bollinger_buy'] = data['Close'] < data['lower_band']
    signals['bollinger_sell'] = data['Close'] > data['upper_band']
    signals["bollinger_middle_line_buy"] = (data['middle_band'] < data['Close'])  
    signals["bollinger_middle_line_sell"] = (data['middle_band'] > data['Close'])
    
    # Donchian Channel signals
    signals['donchian_buy'] = data['Close'] > data['upper_dc']
    signals['donchian_sell'] = data['Close'] < data['lower_dc']
    signals['donchian_buy_middle'] = data['Close'] > data['upper_dc']
    signals['donchian_sell_middle'] = data['Close'] < data['lower_dc']
    
    # Print only the last row of the signals DataFrame
    print(signals.iloc[-1])
    
    return signals

Explanation: This function analyzes the calculated signals from Part 3 and generates buy/sell signals based on specific conditions for each indicator.

Use Cases:

• Generating trading signals for algorithmic trading strategies.
• Providing insights into potential entry and exit points based on technical analysis.

Part 5: Example Usage

# Example Usage
symbol = 'BTC/USDT'
data = fetch_data(symbol)
data = generate_signals(data)
display(data)
signals = analyze_signals(data)

Explanation: This part demonstrates how to use the functions. It fetches historical data for the ‘BTC/USDT’ trading pair, generates signals based on technical indicators, displays the modified DataFrame, and analyzes the signals to generate buy/sell signals.

Use Cases:

• Implementing and testing a basic trading strategy.
• Understanding how different indicators align with historical price data.

Each part is labeled with a comment indicating its purpose in the code. You can run these parts sequentially in a Python environment.

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