FX Manufacturing PMI Momentum

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Introduction

This notebook showcases how a simple Manufacturing PMI strategy trading AUDUSD 3M IMM forwards can be constructed in the SigTech Platform. The strategy will go long/short when Australian PMI is greater/less than its 10 month moving average.

Environment

This section imports the relevant internal and external libraries, and sets up the platform environment.

import sigtech.framework as sig
from sigtech.framework.default_strategy_objects.rolling_fx_forwards import usd_aud_3m_imm

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

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

start_date = dtm.date(2019,10,1)

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

Universe

An AUDUSD 3M IMM Fx Forward is defined as the instrument to trade

fx_forward = usd_aud_3m_imm()

Strategy

Australian Manufacturing PMI data is retrieved using the sig.obj.get method

te_series = sig.obj.get("ECO AUS MANU PMI").data_df()

The 10 month moving average is calculated

pmi_df = pd.DataFrame({'date': te_series.index,
                       'value': te_series['actual_clean']})
pmi_df.index = pd.to_datetime(pmi_df['date'])

pmi_df['ma'] = pmi_df['value'].rolling(window=10).mean()
pmi_df = pmi_df.dropna()

The signal is calculated as 1 or -1 based on whether or not current PMI is greater than its moving average

pmi_df['signal'] = np.select([pmi_df['value'] > pmi_df['ma']], [1], -1)

pmi_df.plot(x='date',y=['value','ma'])
pmi_df.plot(x='date',y='signal',figsize=(18,2));

The signal column is renamed to the instrument to trade and the dataframe is used to create a signal object using the sig.signal_library.from_ts method. Note that the dataframe must be indexed by date.

signal_df = pmi_df[['signal']].rename(columns={'signal': fx_forward.name})
signal_df.index = signal_df.index.date
signal_obj = sig.signal_library.from_ts(signal_df)

Portfolio

To build our portoflio, the SignalStrategy building block is used. By default, the identity allocation function is used, which returns the unchanged allocations as given by the signal strategy output.

pmi_signal_strategy = sig.SignalStrategy(
    start_date=start_date,
    currency=fx_forward.currency,
    signal_name=signal_obj.name,
    use_signal_for_rebalance_dates=True,
    end_date=dtm.date(2021,9,1)
)

pmi_signal_strategy.history()

pmi_signal_strategy.history().plot(label='signal',legend=True);
fx_forward.history()[start_date:].plot(label='rollingforward',legend=True);

Performance

Shows some of the statistics and relevant performance metrics available on the platform for the strategy.

sig.PerformanceReport(pd.concat({'Inverse Vol Scaled': pmi_signal_strategy.history()}, axis=1),
                  cash=sig.CashIndex.from_currency('USD')).report()