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