Optimize the Trend Following strategy with custom args

magenta.grimer

Hi! Can you help me in using the optimizer to optimize this strategy?

# In your final submission you can remove/deactivate all the other cells to reduce the checking time.
# The checking system will run this book multiple times for every trading day within the in-sample period.
# Every pass the available data will be isolated till the current day.
# qnt.backtester is optimized to work with the checking system.
# The checking system will override test_period=1 to make your strategy to produce weights for 1 day per pass.

import xarray as xr
import numpy as np

import qnt.ta as qnta
import qnt.backtester as qnbt
import qnt.data as qndata
import qnt.xr_talib as xrtl
import xarray.ufuncs as xruf
import qnt.ta as qnta


def load_data(period):
    return qndata.futures_load_data(tail=period)


def calc_positions(futures, ma_periods, roc_periods, sideways_threshold):
    """ Calculates positions for given data(futures) and parameters """
    close = futures.sel(field='close')
    
    # calculate MA 
    ma = qnta.lwma(close, ma_periods)
    # calcuate ROC
    roc = qnta.roc(ma, roc_periods)

    # positive trend direction
    positive_trend = roc > sideways_threshold
    # negtive trend direction
    negative_trend = roc < -sideways_threshold 
    # sideways
    sideways_trend = abs(roc) <= sideways_threshold
    
    # We suppose that a sideways trend after a positive trend is also positive
    side_positive_trend = positive_trend.where(sideways_trend == False).ffill('time').fillna(False)
    # and a sideways trend after a negative trend is also negative
    side_negative_trend = negative_trend.where(sideways_trend == False).ffill('time').fillna(False)

    # define signals
    buy_signal = positive_trend
    buy_stop_signal = side_negative_trend

    sell_signal = negative_trend
    sell_stop_signal = side_positive_trend

    # calc positions 
    position = close.copy(True)
    position[:] = np.nan
    position = xr.where(buy_signal, 1, position)
    position = xr.where(sell_signal, -1, position)
    position = xr.where(xruf.logical_and(buy_stop_signal, position.ffill('time') > 0), 0, position)
    position = xr.where(xruf.logical_and(sell_stop_signal, position.ffill('time') < 0), 0, position)
    position = position.ffill('time').fillna(0)

    return position


def calc_output_all(data, params):
    positions = data.sel(field='close').copy(True)
    positions[:] = np.nan
    for futures_name in params.keys(): 
        p = params[futures_name]
        futures_data = data.sel(asset=futures_name).dropna('time','any')
        p = calc_positions(futures_data, p['ma_periods'], p['roc_periods'], p['sideways_threshold'])
        positions.loc[{'asset':futures_name, 'time':p.time}] = p
    
    return positions

# say we select futures and their parameters for technical algorithm
params = {
    'F_NY': {
        'ma_periods': 200, 
        'roc_periods': 5, 
        'sideways_threshold': 2,
    },
    'F_GX': {
        'ma_periods': 200, 
        'roc_periods': 20, 
        'sideways_threshold': 2
    },
    'F_DX': {
        'ma_periods': 40, 
        'roc_periods': 6, 
        'sideways_threshold': 1
    },
}
futures_list = list(params.keys())


def strategy(data):
    output = calc_output_all(data.sel(asset = futures_list), params)
    return output.isel(time=-1)


weights = qnbt.backtest(
    competition_type="futures",
    load_data=load_data,
    lookback_period=5*365,
    start_date='2006-01-01',
    strategy=strategy
)

support

Hello.

We prepared an example with optimization for each asset.

Check it out in the examples tab: "Technical Analysis with optimization for each asset".

Regards.

magenta.grimer

This post is deleted!

support

I adapted this strategy for optimization according to my example.

oprimizator_per_asset__1___2_ (2) (2).ipynb

It just shows how to perform optimization with multiple parameters for each asset.

Pay attention to #TODO. It is necessary to set the right ranges for better optimization.

Unfortunately, this process is slow. It is better to reduce the number of parameters and assets...

magenta.grimer

@support yes, and the "default" strategy with the earlier code got rejected because it took more time than allowed to be checked

support

Hello.

I checked this problem. The script which cut "###DEBUG###" cells was incorrect. I fixed this and resent your strategies (filtered by time out) to checking.

Regards.