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The Triple Barrier Method, described by Marco Lopez de Prado in Advances in Financial Machine Learning, is a powerful technique for labeling events based on price movements. It mirrors real-world trading, where take-profits and stop-losses influence decisions — even implicitly, as in the case of margin calls. By defining three barriers — an upper barrier (profit-taking level), a lower barrier (stop-loss level), and a vertical barrier (time-based exit) — this method provides a structured framework for identifying meaningful price actions.

In this blog post, we’ll explore the intuition behind the method and demonstrate its implementation in Python for tick-level data. Note: This data will likely not fit in memory, but we’ll cover strategies for handling such large datasets in a future post. I personally use a custom + Dask solution which I can cover if enough people are interested.

How It Works

1. Upper Barrier:
   Set above the initial price. If the price hits this level first, the label is 1.

2. Lower Barrier:
   Set below the initial price. If the price hits this level first, the label is -1.

3. Vertical Barrier:
   If neither horizontal barrier is reached within a predefined time frame, the label depends on the price at the exit:

• 1 if the exit price is above the event’s start price.

• -1 if the exit price is at or below the event’s start price.

(Alternative: Some use a neutral label (0) for time-based exits but assigning 1 or -1 and incorporating sample weights often yields better results, as we’ll discuss in a later post.)

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Examples

Setting Barrier Widths

Vertical Barrier

The vertical barrier is a time-based exit. While Lopez de Prado suggests setting it a fixed number of days after the event, this approach doesn’t account for nuances like business days, half-days, or specific trading hours. A better alternative is to define it as a fixed number of bars (e.g., volume bars, time bars, or dollar bars). For instance:

• Set the vertical barrier to 50 bars after the event’s start time.

Horizontal Barriers

Horizontal barriers depend on price volatility. A common approach is to compute a rolling exponential weighted standard deviation (EWMA) of log returns over a fixed period. For example:

1. Calculate the EWMA standard deviation, σₜ​, of 50-period log returns with a 100-bar window.

2. Compute barriers:

• Upper Barrier: Pₜ​⋅exp(C⋅σₜ​)​

• Lower Barrier: Pₜ​⋅exp(-Cσₜ​)

where Pₜ is the current price and C is a configurable constant to control barrier width

Python Implementation

Assume we have dollar bars in a dataframe df_dollar_bars and trade level data in a dataframe df with columns Price, Size and index timestamp.

Add Barriers to Dollar Bars

C = 1
df_dollar_bars['Close_shift']=df_dollar_bars.Close.shift(50)
df_dollar_bars['Vertical_Barrier_TS']=df_dollar_bars.START_TS.shift(-50)
df_dollar_bars['std']=np.log(df_dollar_bars['Close']/df_dollar_bars['Close_shift']).ewm(100,min_periods=100).std()
df_dollar_bars['Upper_Barrier']=df_dollar_bars['Close']*np.exp(C*df_dollar_bars['std'])
df_dollar_bars['Lower_Barrier']=df_dollar_bars['Close']*np.exp(-C*df_dollar_bars['std'])

Label Events

def get_labels(df,dollar_bars,events):
    dollar_bars_events = dollar_bars.loc[events]
    first_barrier_touch_times = []
    for index,row in dollar_bars_events.iterrows():
        vertical_barrier = row['Vertical_Barrier_TS']
        prices =  df.loc[index:vertical_barrier].price
        upper_barrier_first_touch=prices[prices>row['Upper_Barrier']].index.min()
        lower_barrier_first_touch=prices[prices<row['Lower_Barrier']].index.min()
        first_barrier_touch_time=pd.Series([upper_barrier_first_touch,lower_barrier_first_touch,vertical_barrier]).min()
        first_barrier_touch_times.append(first_barrier_touch_time)
    labels = [1 if df.price[end].min()>df.price[start].min() else -1 for start,end in zip(events,first_barrier_touch_times)]
    return labels

labels = get_labels(df,df_dollar_bars,cusum_events)

References

1. De Prado, M.L. Advances in Financial Machine Learning. Wiley, 2018. Chapter 3 introduces the concept of the Triple Barrier Method and its use in labeling financial data.

2. GitHub Repository: Explore implementations at Hudson & Thames ML Finlab.

3. Research Article: Lopez de Prado, M.L. (2018). “The Triple Barrier Method for Labeling.” Available via SSRN: https://ssrn.com/abstract=3328466.