Json data and general programming question

hippylover

Hi, i have json data with open, close, high, low and time in unix time. How would i use backtrader to take the data as i feed it and get an alert every time there's a buy signal(for example ema goes over sma)?

Also, does backtester only take open and close data or can i feed it just last bid for example?

hippylover

Well, i managed to get it to show on plot, but had to convert it to csv and put it in a file first... Is there no way to take the data directly from an array or something? Also, if i want each "period" for calculating/plotting ema/sma/etc to be 30-minutes, is it possible to set this or do i have to feed it half-hour data?

import backtrader as bt
import requests
from jq import jq
import csv
import backtrader.feeds as btfeeds

class smaEmaCross(bt.SignalStrategy):
    def __init__(self):
        sma = bt.ind.SMA(period=50)
        ema = bt.ind.EMA(period=20)
        crossover = bt.ind.CrossOver(ema,sma)
        self.signal_add(bt.SIGNAL_LONG, crossover)


cerebro = bt.Cerebro()
cerebro.addstrategy(smaEmaCross)

#get data
currency = "MAID"
symbol = "BTC"
requestLine = 'https://min-api.cryptocompare.com/data/' + "histominute?fsym=" + currency + "&tsym=" + symbol + "&allData=True&e=Poloniex"
res = requests.get(requestLine)
start = str(int((datetime.datetime.now() - datetime.timedelta(minutes=10000)).timestamp()))

dataDownload = jq('.Data[]|select(.time > ' + start + ')|{date: .time |strftime("%d/%m/%y"), time: .time |strftime("%H:%M:%S"), open: .open, close: .close, high: .high, low: .low, volume: .volumeto}').transform(res.json(), multiple_output=True)
csvData = []
for i in dataDownload:
    csvData.append(str(i['date']) + "," + str(i['time']) + "," + str(i['open']) + "," + str(i['close']) + "," + str(i['high']) + "," + str(i['low']) + "," + str(i['volume']))

print(csvData)

#write file
with open("ohlc.txt","w") as outf:
	outf.write("date,time,open,close,high,low,volume\n")
	for x in csvData:
		outf.write(x + "\n")

data = btfeeds.GenericCSVData(
    dataname="ohlc.txt",
    dtformat='%d/%m/%y',
    #date=0,
    time=1,
    open=2,
    close=3,
    high=4,
    low=5,
    volume=6,
    openinterest=-1,  # -1 for not present
    #fromdate=datetime.datetime(2017, 1, 1),
    #todate=datetime.datetime(2017, 12, 31),
    reverse=False)

cerebro.adddata(data)

cerebro.run()
cerebro.plot()```

Curtis Miller

Since you can create a data feed with a pandas DataFrame, perhaps consider using the pandas function from_json() to convert the JSON data to a DataFrame then use that DataFrame as your data feed.

I'm also curious about data that isn't OHLC (like tick data).

ab_trader

Maybe you want to try to search community using words bid or tick, you may find several posts that can help. As remember it was also a blog post about it. Maybe not single post.

backtrader

You can create your own data feed and fill the values from your JSON data. See

Docs - Binary Datafeed Development

You basically subclass and override _load to deliver your set of OHLC prices each time.

@hippylover said in Json data and general programming question:

Also, does backtester only take open and close data or can i feed it just last bid for example?
@Curtis-Miller said in Json data and general programming question:
I'm also curious about data that isn't OHLC (like tick data).

You can extend the data feed to pass any values you wish. See: Docs - Extending a Datafeed

In any case, bid-ask are values tied to ticks and not to larger timeframes (mostly resampled) which would only have the latest of the bid-ask values to act upon or else the next incoming. This platform doesn't use bid-ask for anything.

@hippylover said in Json data and general programming question:

Also, if i want each "period" for calculating/plotting ema/sma/etc to be 30-minutes, is it possible to set this or do i have to feed it half-hour data?

With no time sample it is impossible to know what timeframe your data is in. But you can resample the data (and even replay it). See Docs - Data Resampling

You may also want to read the FAQ, to remind yourself of setting the right timeframe and compression for your data. See Community - FAQ