Technical Analyis (MACD) for crpto trading

Question

Background: I have writing a crypto trading bot for fun and profit. So far, it connects to an exchange and gets streaming price data. I am using this price to create a technical indicator (MACD). Generally for MACD, it is recommended to use closing prices for 26, 12 and 9 days. However, for my trading strategy, I plan to use data for 26, 12 and 9 minutes.

Question: I am getting multiple (say 10) price ticks in a minute. Do I simply average them and round the time to the next minute (so they all fall in the same minute bucket)? Or is there is better way to handle this.

Many Thanks!

Answer 1

As far as I know, most or all technical indicator formulas rely on same-sized bars to produce accurate and meaningful results. You'll have to do some data transformation. Here's an example of an aggregation technique that uses quantization to get all your bars into uniform sizes. It will convert small bar sizes to larger bar sizes; e.g. second to minute bars.

// C#, see link above for more info

quoteHistory
  .OrderBy(x => x.Date)
  .GroupBy(x => x.Date.RoundDown(newPeriod))
  .Select(x => new Quote
    {
       Date = x.Key,
       Open = x.First().Open,
       High = x.Max(t => t.High),
       Low = x.Min(t => t.Low),
       Close = x.Last().Close,
       Volume = x.Sum(t => t.Volume)
    });

See Stock.Indicators for .NET for indicators and related tools.

Answer 2

This is how I handled it. Streaming data comes in < 1s period. Code checks for new low and high during streaming period and builds the candle. Probably ugly since I'm not a trained developer, but it works.

Adjust "...round('20s')" and "if dur > 15:" for whatever candle period you want.

def on_message(self, msg):
   
        df = pd.json_normalize(msg, record_prefix=msg['type'])
        df['date'] = df['time']
        df['price'] = df['price'].astype(float)
        df['low'] = df['low'].astype(float)

           for i in range(0, len(self.df)):
            if i == (len(self.df) - 1):
                self.rounded_time = self.df['date'][i]
                self.rounded_time = pd.to_datetime(self.rounded_time).round('20s')
                self.lhigh = self.df['price'][i]
                self.lhighcandle = self.candle['high'][i]
                self.llow = self.df['price'][i]
                self.lowcandle = self.candle['low'][i]
                self.close = self.df['price'][i]

        if self.lhigh > self.lhighcandle:
            nhigh = self.lhigh

        else:
            nhigh = self.lhighcandle

        if self.llow < self.lowcandle:
            nlow = self.llow

        else:
            nlow = self.lowcandle

        newdata = pd.DataFrame.from_dict({
            'date': self.df['date'],
            'tkr': tkr,
            'open': self.df.price.iloc[0],
            'high': nhigh,
            'low': nlow,
            'close': self.close,
            'vol': self.df['last_size']})

        self.candle = self.candle.append(newdata, ignore_index=True).fillna(0)

        if ctime > self.rounded_time:
            closeit = True

        self.en = time.time()

        if closeit:
            dur = (self.en - self.st)
            if dur > 15:
                self.st = time.time()
                out = self.candle[-1:]
                out.to_sql(tkr, cnx, if_exists='append')

                dat = ['tkr', 0, 0, 100000, 0, 0]
                self.candle = pd.DataFrame([dat], columns=['tkr', 'open', 'high', 'low', 'close', 'vol'])