defaults.yaml

# YAML
#
#           PRODUCTION STUFF
#
#
## Strategy configuration
# This won't actually work unless you have a strategy called example.. so don't uncomment use as a template
#strategy_list:
#  example:
#    load_backtests:
#      object: sysproduction.strategy_code.run_system_classic.runSystemClassic
#      function: system_method
#    reporting_code:
#      function: sysproduction.strategy_code.report_system_classic.report_system_classic
#
# Allocate capital to strategies
# strategy_weights *must* be defined in private config
strategy_capital_allocation:
  function: sysproduction.strategy_code.strategy_allocation.weighted_strategy_allocation
#  strategy_weights:
#    example: 100.0
#
## Where do we save backtests
backtest_store_directory: 'private'
#
# And backups
csv_backup_directory: 'data.backups_csv'
mongo_dump_directory: 'data.mongo_dump'
echo_directory: 'data.echos'
#
# Interactive brokers
ib_ipaddress: 127.0.0.1
ib_port: 4001
ib_idoffset: 100
#
# Broker class list resolution function
broker_factory_func: 'sysbrokers.broker_factory.get_ib_class_list'
#
# Mongo DB
mongo_host: 127.0.0.1
mongo_db: 'production'
# DO NOT CHANGE THIS VALUE!!!! IT WILL SCREW UP ARCTIC. If you need to use a port other than 27017, use the url
# format for mongo_host, eg mongodb://127.0.0.1:27018
mongo_port: 27017
#
# Needs to be consistent with what you are using in crontab
echo_extension: '.txt'
# Spike checker
max_price_spike: 8.0
# Default get_GMT_offset_hours
GMT_offset_hours: 0
# Behaviour of price filtering
ignore_future_prices: True
ignore_prices_with_zero_volumes_intraday: True
ignore_prices_with_zero_volumes_daily: False
ignore_zero_prices: True
ignore_negative_prices: False
#
# Price frequency (we collect daily data, and separately this frequency
intraday_frequency: H
#
# Capital calculation
production_capital_method: 'full'
#
stale_instruments: ['an_instrument_i_no_longer_trade_or_sample']
#
#
#           BACKTESTING STUFF
#
# Raw data
#
volatility_calculation:
  func: "sysquant.estimators.vol.mixed_vol_calc"
  days: 35
  min_periods: 10
  slow_vol_years: 10
  proportion_of_slow_vol: 0.3
  vol_abs_min: 0.0000000001
#
# forecast capping and scaling
# fixed values
#
forecast_scalar: 1.0
#
# with varying stuff
#
#
instruments_with_threshold: []
use_forecast_scale_estimates: False
#
forecast_scalar_estimate:
   pool_instruments: True
   func: "sysquant.estimators.forecast_scalar.forecast_scalar"
   window: 250000
   min_periods: 500
   backfill: True
#
# capping
forecast_cap: 20.0
#
average_absolute_forecast: 10.0
#
#
# forecast combination
#
forecast_div_multiplier: 1.0
#
use_forecast_div_mult_estimates: False
#
forecast_correlation_estimate:
   pool_instruments: True
   func: sysquant.estimators.pooled_correlation.pooled_correlation_estimator
   frequency: "W"
   date_method: "expanding"
   using_exponent: True
   ew_lookback: 250
   min_periods: 20
   cleaning: True
   rollyears: 20
   floor_at_zero: True
   forward_fill_data: True
#
forecast_div_mult_estimate:
   func: sysquant.estimators.diversification_multipliers.diversification_multiplier_from_list
   # this moving average is in business day space to smooth jumps
   ewma_span: 125
   dm_max: 2.5
#
use_forecast_weight_estimates: False
#
forecast_cost_estimates:
   use_pooled_costs: False
   use_pooled_turnover: True
#
# Smooth forecast weights; weights are in business days
forecast_weight_ewma_span: 125
#
# Set this to 0.13 to enforce the speed limit even for fixed weights or
#   regardless of what an estimator does
forecast_post_ceiling_cost_SR: 999
#
forecast_weight_estimate:
   func: sysquant.optimisation.generic_optimiser.genericOptimiser
   pool_gross_returns: True
   equalise_gross: False
   cost_multiplier: 2.0
   apply_cost_weight: False
   ceiling_cost_SR: 9999
   frequency: "W"
   date_method: "expanding"
   rollyears: 20
   method: handcraft
   cleaning: True
   equalise_SR: False
   ann_target_SR: 0.5
   equalise_vols: True
   shrinkage_SR: 0.9
   shrinkage_corr: 0.5
   correlation_estimate:
     func: sysquant.estimators.correlation_estimator.correlationEstimator
     using_exponent: True
     ew_lookback: 50000
     min_periods: 10
     cleaning: False
     floor_at_zero: False
   mean_estimate:
     func: sysquant.estimators.mean_estimator.meanEstimator
     using_exponent: True
     ew_lookback: 50000
     min_periods: 5
   vol_estimate:
     func: sysquant.estimators.stdev_estimator.stdevEstimator
     using_exponent: True
     ew_lookback: 50000
     min_periods: 5
#
# Capital correction
#
percentage_vol_target: 16.0
notional_trading_capital: 1000000
base_currency: "USD"
# other options are full_compounding and half_compounding, but see syscore.capital.py
capital_multiplier:
   func: syscore.capital.fixed_capital
#
# add instrument codes here to make long only at position sizing
long_only_instruments:
  - "an_instrument"
  - "another_instrument"
# Portfolio creation
#
instrument_div_multiplier: 1.0
#
use_instrument_div_mult_estimates: False
#
instrument_correlation_estimate:
   func: sysquant.estimators.correlation_over_time.correlation_over_time_for_returns
   frequency: "W"
   date_method: "expanding"
   using_exponent: True
   ew_lookback: 25
   min_periods: 20
   cleaning: True
   rollyears: 20
   floor_at_zero: True
   forward_fill_price_index: True
#
instrument_div_mult_estimate:
   func: sysquant.estimators.diversification_multipliers.diversification_multiplier_from_list
   # smooth IDM in daily space
   ewma_span: 125
   dm_max: 2.5
#
use_instrument_weight_estimates: False
#
# smooth weights they will are in daily space
instrument_weight_ewma_span: 125
#
instrument_weight_estimate:
   func: sysquant.optimisation.generic_optimiser.genericOptimiser
   method: handcraft
   frequency: "W"
   equalise_gross: False
   cost_multiplier: 1.0
   apply_cost_weight: False
   date_method: "expanding"
   rollyears: 20
   cleaning: True
   equalise_SR: True
   ann_target_SR: 0.5
   equalise_vols: True
   shrinkage_mean: 1.00
   shrinkage_corr: 0.50
   monte_runs: 100
   correlation_estimate:
     func: sysquant.estimators.correlation_estimator.correlationEstimator
     using_exponent: True
     ew_lookback: 500000
     min_periods: 10
     floor_at_zero: True
     cleaning: False
   mean_estimate:
     func: sysquant.estimators.mean_estimator.meanEstimator
     using_exponent: True
     ew_lookback: 50000
     min_periods: 5
   vol_estimate:
     func: sysquant.estimators.stdev_estimator.stdevEstimator
     using_exponent: True
     ew_lookback: 50000
     min_periods: 5
#
# buffering / position inertia
buffer_method: forecast
buffer_size: 0.10
buffer_trade_to_edge: True
# costs and accounting
use_SR_costs: False
vol_normalise_currency_costs: True
multiply_roll_costs_by: 0.5
#
# risk overlay - make these numbers and uncomment
#risk_overlay:
#  max_risk_fraction_normal_risk: 99999
#  max_risk_fraction_stdev_risk: 99999
#  max_risk_limit_sum_abs_risk: 99999
#  max_risk_leverage: 99999
#
instrument_returns_correlation:
  func: sysquant.estimators.correlation_over_time.correlation_over_time_for_returns
  interval_frequency: "W"
  frequency: "7D"
  date_method: "rolling"
  using_exponent: True
  ew_lookback: 75
  min_periods: 10
  cleaning: True
  offdiag: 0.0
  rollyears: 5
  floor_at_zero: False
  forward_fill_price_index: True
  clip: 0.99
# small system optimisation
small_system:
  shadow_cost: 50
  cost_multiplier: 1.0
  tracking_error_buffer: 0.0125
  shrink_instrument_returns_correlation: 0.5
#
# duplicated/excluded instruments are ignored in backtests
# we still collect price data for them in production, do rolls etc
# this is to avoid double counting of basically the same instrument
# anything in the 'exclude' list is ignored in backtests
# we use the 'include section to match up for reporting when deciding which to trade
# Be careful when swapping between instruments, a bit dangerous if a position is held...
# You can override this by modifying the backtest config .yaml file used in either sim or production
#
# THe following example shows how you would select one instrument out of a choice of 3
#
duplicate_instruments:
  include:
    things: 'thing_we_want'
  exclude:
    things: ['bad_thing', 'Another_thing']
exclude_instrument_lists:
  # ignore_instruments are ignored in backtests
  # we still collect price data for them in production, do rolls etc
  # You can override this by modifying the backtest config .yaml file used in either sim or production
  # If you want to ignore because a duplicate market, use duplicate_instruments
  # If you want to include in sim data, but not actually trade, use trading_restrictions
  #
  # BEST FOR INSTRUMENTS THAT LOOK EXPENSIVE, BUT WHERE WE HAVEN'T GOT ACCURATE DATA YET
  #
  # REVIEW REGULARLY AND CHECK THESE INSTRUMENTS AREN'T BETTER ELSEWHERE...
  ignore_instruments:
    - 'EXAMPLE'
  #
  # Here we put stuff we can't trade, but for which we still want to bring in prices in sim
  # For the dynamically optimised strategy this list will be used in sim as 'don't trade'
  # For static strategies, you will probably want to set these to zero instrument weights
  # In production, things here will be added to the 'reduce_only' list of instruments
  # NOTE: This will be regionally biased, you will probably want to override this in private_config.yaml
  #
  trading_restrictions:
    - RESTRICTED_EXAMPLE
  # These are markets that are too expensive or illiquid to actually trade
  # For the dynamically optimised strategy this list will be used in sim as 'don't trade'
  # For static strategies, you will probably want to set these to zero instrument weights
  # In production, things here will be added to the 'reduce_only' list of instruments
  # Run interactive controls to get a list of suggested markets here
  # To override this list, change in sim .yaml config (sim) and private_config.yaml (production)
  bad_markets:
    - BAD_EXAMPLE