6. Multi-instrument signal strategy

A multi-asset signal strategy
1. Multi-instrument RollingFutureStrategy setup
The first few code blocks are very close to what you already used to create multi-instrument Rolling Future Strategy in tutorial 4.
Set up environment
Note that for this tutorial we will need the pandas library:
import sigtech.framework as sig
import datetime as dtm
import pandas as pd
​
sig.init();
Define instruments, time period, and RollingFutureStrategy function
my_instruments = [
    'C  COMDTY FUTURES GROUP', # corn
    'S  COMDTY FUTURES GROUP', # soybeans
    'SB COMDTY FUTURES GROUP' # sugar
]
​
my_instruments = [sig.obj.get(i) for i in my_instruments]
​
my_start = dtm.date(2019, 1, 1)
my_end = dtm.date(2021, 12, 31)
    
# Define function for creating multiple RollingFutureStrategies
def create_rfs(instrument):
    rfs = sig.RollingFutureStrategy(
        start_date=my_start,
        end_date=my_end,
        currency = instrument.currency,
        contract_code = instrument.contract_code,
        contract_sector = instrument.contract_sector,
        rolling_rule='F_0'
    )
    return rfs.name
Add the RollingFutureStrategy instances to a dictionary
basket = {}
​
# Populate the dictionary with the chosen instruments:
for instrument in my_instruments:
    basket[instrument.asset] = create_rfs(instrument)
2. Loop to create multi-instrument DataFrame of signals
This following code is very similar to that used in the previous tutorial, except you're using a loop to create signals for multiple instruments, and combining them into a single DataFrame:
# List to store signals for all assets
all_signals = []
​
# Loop through all provided assets
for key, strategy_name in basket.items():
    
    # Duplicate the instrument's history Series and reset its values
    signals = sig.obj.get(strategy_name).history()
    signals.values[:] = 0
    
    # Assign signals as per your strategy
    signals.loc[signals.index.month <= 4] = -1 
    signals.loc[signals.index.month >= 8] = 1 
    
    # Convert the signal Series into a DataFrame
    # Give it the same name as the corresponding RollingFutureStrategy instance
    # Add the signal for this asset to the list of stored signals.
    all_signals.append(signals.to_frame(strategy_name))
    
# Convert the list to a DataFrame and remove all NaN values
all_signals_df = pd.concat(all_signals, axis=1).dropna()
​
# View signals DataFrame
all_signals_df
3. Define SignalStrategy 
This step is identical to defining a single-instrument SignalStrategy, except for the reference to the new DataFrame:
seasonal_strategy = sig.SignalStrategy(
    
    # Reference the multi-instrument hsignals DataFrame
    signal_name=sig.signal_library.from_ts(all_signals_df).name,
    
    start_date=my_start,
    end_date=my_end,
    currency='USD',
    rebalance_frequency='SOM',
    allocation_function=sig.signal_library.allocation.normalize_weights,
)
4. View performance
seasonal_strategy.plot.performance()
​👷
Your turn: customise the strategy
1.Add additional instruments
2.Try a different set of signals.