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API Reference: Quantum Cipher

This document provides a technical reference for the Quantum Cipher signal generation module. This module is responsible for calculating momentum, volume, and trend-based signals.

Core Logic

The Quantum Cipher combines several standard indicators into a single, cohesive signal: 1. An Adaptive Momentum Oscillator to gauge the speed and direction of price changes. 2. A Volume-Weighted Confirmation using VWAP and a volume spike ratio to ensure conviction. 3. A Dual EMA Trend Filter to ensure signals are only taken in the direction of the prevailing trend.

Functions

calculate_quantum_cipher(df)

Calculates the full set of Quantum Cipher signals from a market data DataFrame.

  • Parameters:

    • df (pandas.DataFrame): A DataFrame containing OHLCV data with the following columns: timestamp, open, high, low, close, volume.

  • Returns: (pandas.DataFrame)

    • A new DataFrame with the original OHLCV data plus several calculated columns, including:
      • vwap: The Volume-Weighted Average Price.
      • osc: The final Momentum Oscillator value.
      • buy_signal (bool): True if a buy signal is generated for a given candle.
      • sell_signal (bool): True if a sell signal is generated for a given candle.

  • Usage Example: 

    import pandas as pd
from app.logic.cipher import fetch_market_data, calculate_quantum_cipher

# 1. Fetch market data
market_df = fetch_market_data('binance', 'BTC/USDT', '1h')

# 2. Calculate signals
if not market_df.empty:
    signals_df = calculate_quantum_cipher(market_df)
    print(signals_df[['timestamp', 'close', 'buy_signal', 'sell_signal']].tail())

fetch_market_data(exchange, symbol, timeframe, limit=1000)

Fetches historical OHLCV data from a specified cryptocurrency exchange using the ccxt library.

  • Parameters:

    • exchange (str): The ID of the exchange (e.g., 'binance').
    • symbol (str): The trading pair symbol (e.g., 'BTC/USDT').
    • timeframe (str): The candle timeframe (e.g., '15m', '1h', '1d').
    • limit (int, optional): The number of candles to fetch. Defaults to 1000.

  • Returns: (pandas.DataFrame)

    • A DataFrame containing the requested OHLCV data.

Full Source Code

Click to view the full source code for `cipher.py` 
import pandas as pd
import numpy as np
import ccxt
import time

def calculate_quantum_cipher(df):
    """
    Calculate Quantum Cipher signals from market data
    """
    # 1. Calculate core price values
    df['hlc3'] = (df['high'] + df['low'] + df['close']) / 3

    # 2. Adaptive Momentum Oscillator
    df['mom'] = df['hlc3'].diff()
    df['abs_mom'] = df['mom'].abs()
    df['smooth_mom'] = df['mom'].ewm(span=14, adjust=False).mean()
    df['smooth_abs'] = df['abs_mom'].ewm(span=14, adjust=False).mean()
    df['osc'] = 100 * df['smooth_mom'] / (df['smooth_abs'] + 1e-9) # Avoid division by zero

    # 3. Volume-Weighted Confirmation
    cumulative_vp = (df['hlc3'] * df['volume']).cumsum()
    cumulative_vol = df['volume'].cumsum()
    df['vwap'] = cumulative_vp / (cumulative_vol + 1e-9) # Avoid division by zero
    df['volume_sma20'] = df['volume'].rolling(window=20).mean()
    df['v_ratio'] = df['volume'] / (df['volume_sma20'] + 1e-9) # Avoid division by zero

    # 4. Trend Filter
    df['ema_fast'] = df['close'].ewm(span=8, adjust=False).mean()
    df['ema_slow'] = df['close'].ewm(span=21, adjust=False).mean()

    # 5. Signal Conditions
    df['bull_vol'] = (df['close'] > df['vwap']
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