Quantopian-Algorithm/RSI EWMA Algorithm at master · elynagh/Quantopian-Algorithm · GitHub

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"""
This algorithm calculates the Relative Strength Index (RSI) for growth stocks I picked out over
a specified window length. The mean and weighted mean (exponential moving average)
are calculated. The weighted mean is compared with the actual RSI value of the stock
to determine when a RSI crossover occurs.
"""

import numpy as np
import pandas as pd
import talib

# Setup our variables
def initialize(context):
    context.assets = [sid(351), sid(5121), sid(62), sid(50713), sid(4221), sid(22009), sid(49610), sid(47975), sid(1131), sid(45815), sid(49222), sid(34395), sid(45847), sid(24)] # stock that I want to backtest (AMD, ABT, GOOS, KEY, SPYG, SQ, BST, BSX, TWTR, TDOC, LULU, CHGG, APPL)


    # Rebalance every day, 30 minutes hour after market open on Monday.
    schedule_function(my_rebalance,
                      date_rules.every_day(),
                      time_rules.market_open(minutes=30))

    # Record tracking variables at the end of each day.
    schedule_function(my_record_vars,
                      date_rules.every_day(),
                      time_rules.market_close())

    # Set commission to zero for testing purposes
    set_commission(commission.PerTrade(cost=0))


def my_rebalance(context, data):
    # Window length
    window = 14

    for stock in context.assets:
        # Load historical data for the stock
        hist = data.history(stock, 'price', 24, '1d')

        # Calculate RSI moving average
        rsi = talib.RSI(hist, timeperiod=window)
        context.rsi_current = rsi[-1] # Get most recent RSI of the stock
        context.rsi_mean = np.nanmean(rsi)

        # Calculate RSI expotential weighted moving average(EWMA)
        center_of_mass = (window - 1) / 2
        context.rsi_ewma = pd.ewma(rsi, com=center_of_mass, ignore_na=True)[-1]

        # Calculate rsi moving average crossover with weighted average
        rsi_crossover = context.rsi_current - context.rsi_ewma


        # Grab portfolio information
        current_position = context.portfolio.positions[stock].amount
        cash = context.portfolio.cash


        # RSI > RSI_mean (long position) if RSI is greater than 80, or less than 60 ~ trending positive
        if context.rsi_ewma > 80 and data.can_trade(stock):
            log.info("Sell.")
            order_target_percent(stock, 0)
        elif rsi_crossover > 0 and data.can_trade(stock) and context.rsi_current < 60:
            log.info("Buy.")
            order_target_percent(stock, .0714)
        # RSI < RSI_mean (short position) if RSI is less than 30 ~ trending negative
        elif context.rsi_ewma < 30 and data.can_trade(stock):
            log.info("Pray")
            order_target_percent(stock, .0714)


def my_record_vars(context, data):
    """
    Plot variables at the end of each day.
    """

    record(
        stock_rsi=context.rsi_current,
        stock_rsi_ewma=context.rsi_ewma,
    )