Setup

Like Tiny, Bombardier is just one of those Canadian company that I would like to own. And the trend was looking super solid on both the daily and weekly chart. So I just somehow decided to enter on 2025-11-13¹. I had no exit strategy since it keeps making new high, so my plan was to exit using a 3ATR channel which was hit on 2025-11-27 (highlighted in orange in Figure 1)

Entry

Given the small success of Figure 1, I revisited this trade on 2026-01-27, this time with a stop set at 228.21 based off of the “low” set on 2025-12-31 (marked by red arrow in #fig-daily-20260128). And I was more agressive staking 200CAD on this position.

Bot 5 shares @ 248.00 on 2026-01-27
Bot 5 shares @ 250.74 on 2026-01-28

Stop

the Monday after, the stock gapped down and opened at 220 (a 2.9ATR move!). Since I used a stop-limit order, I was lucky and got filled higher than my stop at 230.00 (around 9:32, see Figure 5)

Sold 10 shares @ 230.00

Loss: 193.7 CAD

Code

import yfinance as yf
import pandas as pd
import plotly.graph_objects as go

# Example for a specific stock and date range
ticker = "BBD-A.TO"  
start_date = "2026-01-30"
end_date = "2026-01-31"

# Download minute-level data
df = yf.download(ticker, start=start_date, end=end_date, interval="1m", multi_level_index = False)

# Convert UTC index to EST
df.index = df.index.tz_convert('US/Eastern')


fig = go.Figure(data=go.Candlestick(x=df.index,
                    open=df['Open'],
                    high=df['High'],
                    low=df['Low'],
                    close=df['Close'],
                    name = "Price"))
fig.update_layout(width = 800, height = 600, title='Candles for BBD-A.TO on 2026-01-30', 
                  template='plotly_dark'
                 )

1: see available themes here; note that for plotly chart in streamlit, a different set of themes are available per the examples here

Code

import plotly.io as pio
pio.renderers.default = "notebook"

# compute volume profile
def get_volume_profile(data:pd.DataFrame, base:int=2, num_bins: int = 20, do_round: bool = False):
    if do_round:
        df = data[['Close', 'Volume']].copy()
        #Round to nearest X
        df['Last'] = df['Close'].apply(lambda x: round(x, base))
        # Remove the date index
        df = df.set_index('Last')[['Volume']]
        df = df.groupby(['Last'], observed = True).sum()
    else:
        # We bin the prices and sum the volume for each bin
        price_buckets = pd.cut(data['Close'], bins=num_bins)
        volume_profile = data.groupby(price_buckets, observed=True)['Volume'].sum()
        # Get the midpoint of each price bin for the Y-axis coordinates
        volume_profile.index = [interval.mid for interval in volume_profile.index]        
        df = volume_profile
    return df

def add_volume_profile(fig, data: pd.DataFrame, in_place: bool = False):
    fig = go.Figure(fig)
    my_volume_profile = get_volume_profile(data = df, do_round = False)
    # display(my_volume_profile)

    # Trace 2: Volume Profile (Horizontal Bars)
    # We assign this to 'x2' so it can have its own scale
    fig.add_trace(go.Bar(
        x = my_volume_profile.values,
        y = my_volume_profile.index,
        orientation='h',
        name='Volume Profile',
        xaxis='x2',
        marker_color='rgba(100, 150, 250, 0.3)', # Semi-transparent blue
        hoverinfo='x+y'
    ))

    # 4. Layout Configuration
    fig.update_layout(
        yaxis_title='Price',
        xaxis_rangeslider_visible=False,
        showlegend=False,
        
        # Primary X-axis (Date)
        xaxis=dict(domain=[0, 1]),
        
        # Secondary X-axis (Volume)
        # overlaying='x' makes it share the plot area
        # side='top' puts the axis at the top (or hide it with showticklabels=False)
        xaxis2=dict(
            title='Volume',
            overlaying='x',
            side='top',
            showticklabels=False,
            showgrid=False,
            # range: Increasing the max range "pushes" the profile to the left
            # Here we make it occupy roughly the first 25% of the chart
            range=[0, volume_profile.max() * 4] 
        )
    )
    
    return fig

add_volume_profile(fig = fig, data= df)

1: plotly’s volume profile is very sensitive to the price value selected, luckily I had the help of a coding agent for the addition of the volume profile.

(a) BBD-A.TO interday data on 2026-01-30

(b)

Figure 5

Learning

As Figure 6 shows, two weeks after breaking support the stock continues in its uptrend showing that:

if you are patient and ready, there will be opportunities to get into a trending stock
strong trend can break, setting stop at a reasonable level is key to avoid getting bumped out of a trade.
down side breakout like that show in Figure 6 might reveal what a good stop level is going forward²

Figure 6: Daily Chart on 23 Feb 2026, red arrow indicates when stop was hit

Tip 1: determining a reasonable stop

Gap size should be analyzed to see if the initial stop allows room for a “normal” size Gap down occurrance
the use of volume profile (as shown in Figure 5) might reveal a “popular” price level that could be used to form a stop

Footnotes

most likely with a stop at 189.87 at the slow EMA with 100 CAD at risk, which explains the 5 shares.↩︎
as L.TO shows, a few ticks below the obvious level might be wise↩︎