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newbiequant96

Hello,

I'm testing combining crypto with stocks for Q21 but I'm getting an error. We hope to help.

Thank you.

Below is my code

# Import basic libraries.
import xarray as xr
import pandas as pd
import numpy as np
# Import Quantiacs libraries.
import qnt.data    as qndata  # load and manipulate data
import qnt.output as qnout   # manage output
import qnt.backtester as qnbt # backtester
import qnt.stats   as qnstats # statistical functions for analysis
import qnt.graph   as qngraph # graphical tools
import qnt.ta      as qnta    # indicators library
import qnt.xr_talib as xr_talib   # indicators library

def load_data(period):
    futures = qndata.futures.load_data(tail=period).isel(asset=0)
    stocks  = qndata.stocks.load_ndx_data(tail=period)
    crypto= qndata.crypto.load_data(tail=period)
    return {"futures": futures, "stocks": stocks, "crypto": crypto}, futures.time.values



def window(data, max_date: np.datetime64, lookback_period: int):
    min_date = max_date - np.timedelta64(lookback_period, "D")
    return {
        "futures": data["futures"].sel(time=slice(min_date, max_date)),
        "stocks":  data["stocks"].sel(time=slice(min_date, max_date)),
        "crypto":  data["crypto"].sel(time=slice(min_date, max_date)),
    }


def strategy(data):
    close_futures = data["crypto"].sel(field="close")
    close_stocks  = data["stocks"].sel(field="close")
    sma20 = qnta.sma(close_futures, 20).isel(time=-1)
    sma20_stocks = qnta.sma(close_stocks, 20).isel(time=-1)
    is_liquid = data["stocks"].sel(field="is_liquid").isel(time=-1)
    weights = xr.where(sma20 < sma20_stocks, 1, -1)
    weights = weights * is_liquid 
    weights = weights / 100.0
    return weights

qnbt.backtest(
    competition_type= "stocks_nasdaq100",
    load_data= load_data,
    lookback_period= 90,
    start_date= "2006-01-01",
    strategy= strategy,
    window= window
)

newbiequant96

Hello,

I'm trying to apply ticker filters from stocks to futures, but it doesn't work. Below is my code

For stock:

import qnt.stats as qnstats

# data = qndata.stocks.load_ndx_data(tail = 17*365, dims = ("time", "field", "asset"))
data = qndata.stocks.load_ndx_data(min_date="2005-01-01")
def get_best_instruments(data, weights, top_size):
    # compute statistics:
    stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True)
    # calculate ranks of assets by "sharpe_ratio":
    ranks = (-stats_per_asset.sel(field="sharpe_ratio")).rank("asset")
    # select top assets by rank "top_period" days ago:
    top_period = 1
    rank = ranks.isel(time=-top_period)
    top = rank.where(rank <= top_size).dropna("asset").asset

    # select top stats:
    top_stats = stats_per_asset.sel(asset=top.values)

    # print results:
    print("SR tail of the top assets:")
    display(top_stats.sel(field="sharpe_ratio").to_pandas().tail())

    print("avg SR = ", top_stats[-top_period:].sel(field="sharpe_ratio").mean("asset")[-1].item())
    display(top_stats)
    return top_stats.coords["asset"].values

get_best_instruments(data, weight, 15)

For futures

import qnt.stats as qnstats

# data = qndata.stocks.load_ndx_data(tail = 17*365, dims = ("time", "field", "asset"))
data = qndata.futures_load_data(min_date="2005-01-01")
def get_best_instruments(data, weights, top_size):
    # compute statistics:
    stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True)
    # calculate ranks of assets by "sharpe_ratio":
    ranks = (-stats_per_asset.sel(field="sharpe_ratio")).rank("asset")
    # select top assets by rank "top_period" days ago:
    top_period = 1
    rank = ranks.isel(time=-top_period)
    top = rank.where(rank <= top_size).dropna("asset").asset

    # select top stats:
    top_stats = stats_per_asset.sel(asset=top.values)

    # print results:
    print("SR tail of the top assets:")
    display(top_stats.sel(field="sharpe_ratio").to_pandas().tail())

    print("avg SR = ", top_stats[-top_period:].sel(field="sharpe_ratio").mean("asset")[-1].item())
    display(top_stats)
    return top_stats.coords["asset"].values

get_best_instruments(data, weight, 15)

Please help me. I hope you can provide an example on how to filter ticker futures by sharpe similar to the get_best_instruments function. Thank you

@support @Vyacheslav_B

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