Cryptocurrency¶
Cryptocurrency Daily Data¶
Quantiacs provides up-to-date daily data for cryptocurrencies:
import qnt.data as qndata
btc_data = qndata.cryptodaily.load_data(tail=365 * 8, dims=('time', 'field', 'asset'))
['ADA', 'AUR', 'AVAX', 'BCH', 'BCN', 'BLK', 'BNB', 'BSV', 'BTC', 'BTG',
'BTS', 'DASH', 'DGC', 'DGD', 'DOGE', 'DOT', 'EOS', 'ETC', 'ETH', 'FCT',
'FRC', 'FTC', 'GNT', 'ICP', 'IFC', 'IXC', 'LINK', 'LSK', 'LTC', 'MAID',
'MNC', 'NEO', 'NMC', 'NXT', 'OMNI', 'PPC', 'PTS', 'QRK', 'REP', 'SOL',
'STEEM', 'STRAX', 'THETA', 'TRC', 'TRX', 'UNI', 'WAVES', 'WDC', 'XCP',
'XEM', 'XLM', 'XMR', 'XPM', 'XPY', 'XRP']
| Number | Ticker | Full Name |
|---|---|---|
| 1 | ADA | Cardano |
| 2 | AUR | Auroracoin |
| 3 | AVAX | Avalanche |
| 4 | BCH | Bitcoin Cash |
| 5 | BCN | Bytecoin |
| 6 | BLK | BlackCoin |
| 7 | BNB | Binance Coin |
| 8 | BSV | Bitcoin SV |
| 9 | BTC | Bitcoin |
| 10 | BTG | Bitcoin Gold |
| 11 | BTS | BitShares |
| 12 | DASH | Dash |
| 13 | DGC | DigitalCoin |
| 14 | DGD | DigixDAO |
| 15 | DOGE | Dogecoin |
| 16 | DOT | Polkadot |
| 17 | EOS | EOS |
| 18 | ETC | Ethereum Classic |
| 19 | ETH | Ethereum |
| 20 | FCT | Factom |
| 21 | FRC | Freicoin |
| 22 | FTC | Feathercoin |
| 23 | GNT | Golem |
| 24 | ICP | Internet Computer |
| 25 | IFC | Infinitecoin |
| 26 | IXC | Ixcoin |
| 27 | LINK | Chainlink |
| 28 | LSK | Lisk |
| 29 | LTC | Litecoin |
| 30 | MAID | MaidSafeCoin |
| 31 | MNC | Mincoin |
| 32 | NEO | NEO |
| 33 | NMC | Namecoin |
| 34 | NXT | Nxt |
| 35 | OMNI | Omni |
| 36 | PPC | Peercoin |
| 37 | PTS | ProtoShares |
| 38 | QRK | Quark |
| 39 | REP | Augur |
| 40 | SOL | Solana |
| 41 | STEEM | Steem |
| 42 | STRAX | Stratis |
| 43 | THETA | Theta Network |
| 44 | TRC | Terracoin |
| 45 | TRX | TRON |
| 46 | UNI | Uniswap |
| 47 | WAVES | Waves |
| 48 | WDC | WorldCoin |
| 49 | XCP | Counterparty |
| 50 | XEM | NEM |
| 51 | XLM | Stellar |
| 52 | XMR | Monero |
| 53 | XPM | Primecoin |
| 54 | XPY | Paycoin |
| 55 | XRP | XRP |
Examples¶
This trading strategy uses a moving average crossover system for Crypto Daily Long contest, taking long positions when the 10-day LWMA is above the 50-day LWMA, considering the asset’s liquidity.
# import os
# os.environ['API_KEY'] = "{your_api_key_here}" # you may need it for local development
import xarray as xr
import qnt.stats as qnstats
import qnt.data as qndata
import qnt.output as qnout
import qnt.ta as qnta
def calculate_weights(data):
close = data.sel(field="close")
liquidity = data.sel(field='is_liquid')
long_lwma = qnta.lwma(close, 50)
short_lwma = qnta.lwma(close, 10)
buy = 1
return xr.where(short_lwma > long_lwma, buy, 0) * liquidity
# SINGLE-PASS
data = qndata.cryptodaily.load_data(min_date="2013-04-01") # load data
weights = calculate_weights(data)
weights = qnout.clean(weights, data, 'crypto_daily_long') # fix common errors
qnout.check(weights, data, 'crypto_daily_long') # check that weights are correct.
qnout.write(weights) # write results, necessary for submission.
statistics = qnstats.calc_stat(data, weights.sel(time=slice("2014-01-01", None))) # calc stats
print(statistics.to_pandas().tail())
# import qnt.graph as qngraph
# qngraph.make_major_plots(statistics) # works in Jupyter Notebook
# # MULTI-PASS
# import qnt.backtester as qnbt
# weights = qnbt.backtest(
# competition_type='crypto_daily_long', # Crypto Daily Long contest
# lookback_period=365, # lookback in calendar days
# start_date="2014-01-01",
# strategy=calculate_weights,
# analyze=True,
# build_plots=True
# )
This example demonstrates how to use various data sources to predict the stock market. It leverages futures, stocks, and cryptocurrency data to build a strategy.
# import os
# os.environ['API_KEY'] = "{your_api_key_here}" # you may need it for local development
# 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
import qnt.exposure as qnexp # exposure calculation
def load_data(period):
stocks = qndata.stocks.load_ndx_data(tail=period)
futures = qndata.futures.load_data(tail=period, assets=["F_DX"]).isel(asset=0)
futures = xr.align(futures, stocks.isel(field=0), join='right')[0]
try:
crypto = qndata.cryptodaily.load_data(tail=period, assets=["BTC"]).isel(asset=0)
crypto = xr.align(crypto, stocks.isel(field=0), join='right')[0]
except Exception as e:
# Cryptocurrency data is not available in 2006.
print(f"Failed to load crypto data: {e}")
crypto = futures # Fallback to futures data if crypto data loading fails
return {"futures": futures, "stocks": stocks, "crypto": crypto}, stocks.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["futures"].sel(field="close")
close_crypto = data["crypto"].sel(field="close")
close_stocks = data["stocks"].sel(field="close")
# Generate buy signal if 20-day SMA < 50-day SMA, otherwise hold (0) or sell (-1)
accuracy = 0.00001 # to avoid floating point errors
weights_stocks = xr.where(qnta.sma(close_stocks, 20) < qnta.sma(close_stocks, 50) + accuracy, 1, 0)
weights_crypto = xr.where(qnta.sma(close_crypto, 20) < qnta.sma(close_crypto, 60) + accuracy, 1, 0)
weights = (weights_stocks + weights_crypto) / 2
# Apply liquidity filter
is_liquid = data["stocks"].sel(field="is_liquid")
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)
return weights
weights = qnbt.backtest(
competition_type="stocks_nasdaq100",
load_data=load_data,
lookback_period=90,
start_date="2005-12-30",
strategy=strategy,
window=window,
check_correlation=False
)
Cryptocurrency Hourly Data¶
Quantiacs provides up-to-date hourly data - price and volume - for the following cryptocurrencies:
import qnt.data as qndata
crypto_data = qndata.crypto.load_data(tail=365 * 10)
['BCH', 'BTC', 'EOS', 'ETH', 'LTC', 'USDT', 'XRP']
| Number | Ticker | Full Name |
|---|---|---|
| 1 | BCH | Bitcoin Cash |
| 2 | BTC | Bitcoin |
| 3 | EOS | EOS |
| 4 | ETH | Ethereum |
| 5 | LTC | Litecoin |
| 6 | USDT | Tether |
| 7 | XRP | XRP |
BTC Futures from spot price¶
For a more detailed description on loading and accessing BTC Futures consult our API-Reference: Loading BTC Futures Data
The Bitcoin Futures data for the last 8 years (history extended with Bitcoin spot price) can be loaded using:
import qnt.data as qndata
btc_data = qndata.cryptofutures.load_data(tail=365 * 8, dims=('time', 'field', 'asset'))