Custom strategy using RSI

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Introduction

The purpose of this notebook is to show how to create a custom strategy which acts on an RSI indicator.

Environment

This section will import relevant internal and external libraries, as well as setting up the platform environment.

import sigtech.framework as sig
from sigtech.framework.signal.library.momentum import rsi

import pandas as pd
import datetime as dtm
import seaborn as sns

sns.set(rc={'figure.figsize': (18, 6)})

env = sig.init(env_date=dtm.date(2024, 2, 22))

Universe

sig_master = env.sig_master()
rdsa = sig_master.filter('RDSA', 'EXCHANGE_TICKER')\
          .filter_primary_tradable() \
          .filter_operating_mic(operating_mics='XLON')
rdsa = rdsa.to_single_stock()
rdsa_xlon = rdsa[list(rdsa.keys())[0]]

rs = sig.ReinvestmentStrategy(
    currency=rdsa_xlon.currency,
    start_date=dtm.date(2010, 1, 4),
    underlyer=rdsa_xlon.name,
)

Strategy

class CustomRSIStrategy(sig.DailyStrategy):

    asset_ticker: str

    def __post_init__(self):
        self.asset = sig.obj.get(self.asset_ticker)
        self.asset_history = self.asset.history()
        super().__post_init__()

    def strategy_initialization(self, dt):
        """ "Magic" function that is run before the strategy's first trading date. """
        # We loop over every day in our history schedule
        for d in self.history_schedule().all_data_dates()[1:]:
            self.add_method(d, self.rsi_decision)  # a processor is just a method

    def rsi_decision(self, dt):
        """ Takes a trading decision based on RSI level. """

        # Calculate the 14-period RSI
        h = self.asset_history
        d = self.size_date_from_decision_dt(dt)
        asset_history = h[:d].iloc[-20:].to_frame()  # we cut to the last prices
        rsi_series = rsi(14, asset_history).squeeze().dropna()

        # Store today's and yesterday's values
        rsi_tod = rsi_series.iloc[-1]
        rsi_yday = rsi_series.iloc[-2]

        # Decide on how to trade
        # Exiting out of oversold - we buy
        if rsi_tod > 30. and rsi_yday < 30.:
            self.add_position_target(dt, self.asset_ticker, 1., unit_type='WEIGHT')
        # Crossing 50 from beneath, exiting
        elif rsi_tod > 50. and rsi_yday < 50.:
            self.add_position_target(dt, self.asset_ticker, 0., unit_type='WEIGHT')
        # Exiting out of overbought - we sell
        elif rsi_tod < 70. and rsi_yday > 70.:
            self.add_position_target(dt, self.asset_ticker, -1., unit_type='WEIGHT')
        # Crossing 50 from above, exiting
        elif rsi_tod < 50. and rsi_yday > 50.:
            self.add_position_target(dt, self.asset_ticker, 0., unit_type='WEIGHT')

rsi_strat = CustomRSIStrategy(
    currency=rs.currency,
    start_date=dtm.date(2011, 1, 4),
    asset_ticker=rs.name,
    include_trading_costs=False,
)

Performance Analytics

strategies = pd.concat({
    'RDSA RSI Strategy': rsi_strat.history(),
    'RDSA Long-only Strategy': rs.history()
}, axis=1).dropna()

sig.PerformanceReport(strategies).report()