Hands-On Machine Learning for Algorithmic Trading

Time bars

Time bars involve trade aggregation by period:

def get_bar_stats(agg_trades):    vwap = agg_trades.apply(lambda x: np.average(x.price,            weights=x.shares)).to_frame('vwap')    ohlc = agg_trades.price.ohlc()    vol = agg_trades.shares.sum().to_frame('vol')    txn = agg_trades.shares.size().to_frame('txn')    return pd.concat([ohlc, vwap, vol, txn], axis=1)resampled = trades.resample('1Min')time_bars = get_bar_stats(resampled)

We can display the result as a price-volume chart:

def price_volume(df, price='vwap', vol='vol', suptitle=title):    fig, axes = plt.subplots(nrows=2, sharex=True, figsize=(15, 8))    axes[0].plot(df.index, df[price])    axes[1].bar(df.index, df[vol], width=1 / (len(df.index)),                 color='r')    xfmt = mpl.dates.DateFormatter('%H:%M') axes[ ...