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Q18 Quick Start

This trading strategy is designed for the Quantiacs platform, which hosts competitions for trading algorithms. Detailed information about the competitions is available on the official Quantiacs website.

How to Run the Strategy

In an Online Environment

The strategy can be executed in an online environment using Jupiter or JupiterLab on the Quantiacs personal dashboard. To do this, clone the template in your personal account.

In a Local Environment

To run the strategy locally, you need to install the Quantiacs Toolbox.

Strategy Overview

This Jupyter notebook shows you the basic steps for taking part to the Q18 NASDAQ-100 Stock Long-Short contest.

Example

import xarray as xr

import qnt.ta as qnta
import qnt.backtester as qnbt
import qnt.data as qndata
import qnt.exposure as qnexp


def load_data(period):
    return qndata.stocks.load_ndx_data(tail=period)


def strategy(data):
    close = data.sel(field="close")
    is_liquid = data.sel(field="is_liquid")  # this field tags NASDAQ-100 stocks
    sma_slow = qnta.sma(close, 200).isel(time=-1)
    sma_fast = qnta.sma(close, 20).isel(time=-1)
    # 1 - long position (**buy**), -1 - short position (**sell**)
    weights = xr.where(sma_slow < sma_fast, 1, -1)
    weights = weights * is_liquid  # trade only NASDAQ-100 stocks
    # Normalize positions and cut big positions
    weights_sum = abs(weights).sum('asset')
    weights = xr.where(weights_sum > 1, weights / weights_sum, weights)
    weights = qnexp.cut_big_positions(weights=weights, max_weight=0.049)
    return weights


weights = qnbt.backtest(
    competition_type="stocks_nasdaq100",
    load_data=load_data,
    lookback_period=365 * 4,
    start_date="2006-01-01",
    strategy=strategy,
    analyze=True,
    build_plots=True,
    check_correlation=False
)

Single-pass version

import xarray as xr

import qnt.ta as qnta
import qnt.data as qndata
import qnt.output as qnout
import qnt.stats as qns
import qnt.exposure as qnexp

data = qndata.stocks.load_ndx_data(min_date="2005-01-01")

close = data.sel(field="close")
is_liquid = data.sel(field="is_liquid")
sma_slow = qnta.sma(close, 200)
sma_fast = qnta.sma(close, 20)
weights = xr.where(sma_slow < sma_fast, 1, -1)
weights = weights * is_liquid

# Normalize positions and cut big positions
weights_sum = abs(weights).sum('asset')
weights = xr.where(weights_sum > 1, weights / weights_sum, weights)
weights = qnexp.cut_big_positions(weights=weights, max_weight=0.049)

weights = qnout.clean(weights, data, "stocks_nasdaq100")

qnout.check(weights, data, "stocks_nasdaq100", check_correlation=False)

# calc stats
stats = qns.calc_stat(data, weights.sel(time=slice("2006-01-01", None)))
display(stats.to_pandas().tail())

# Graph
performance = stats.to_pandas()["equity"]
import qnt.graph as qngraph

qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log")

qnout.write(weights)  # To participate in the competition, save this code in a separate cell.