Bearish Cont.Heuristic1 min read
◆ The PatternSharp decline + upward-sloping channel = continuation down
The Bear Flag is the bearish mirror: a sharp decline (pole) followed by a gentle upward-sloping consolidation (flag). Breakdown below the lower trendline continues the downtrend.
Target = Breakdown point − Pole length
Bear flags in strong downtrends can resolve very quickly — the consolidation may be brief.
// Bear Flag — pole and flag anatomy
Key insight: Bear flags tend to resolve faster than bull flags — fear is a stronger emotion than greed, so selling accelerates more quickly.
Pattern bridge: The mirror of a bull flag. In ML, gradient clipping creates brief pauses in the descent before loss resumes falling.
Worked example
A pole from 100 down to 85 in five days, then eleven days drifting up to 90 (slope +0.32 a day). Day 16 closes at 84, below the flag’s low of 85. Measured move: 85 − 15 = 70.
The example data
The turning points the daily closes run through, in straight lines between them; high and low are half a point either side of each bar.
| Day | Close |
|---|---|
| 0 | 100 |
| 5 | 85 |
| 7 | 88 |
| 9 | 86 |
| 11 | 89 |
| 13 | 87 |
| 15 | 90 |
| 16 | 84 |
Where it misleads
- A slow, long rise after the fall is more likely the start of a recovery than a pause.
- Short sellers covering can turn the breakdown into a sharp reversal.
- As with the bull flag, the target assumes a second leg as long as the first.
In code
c = df['close'] pole = c.iloc[-17] - c.iloc[-12] # the 5-bar fall flag = c.iloc[-12:-1] slope = np.polyfit(np.arange(len(flag)), flag.values, 1)[0] is_flag = pole / c.iloc[-17] > 0.08 and slope > 0 # sharp fall, then a drift up breakdown = c.iloc[-1] < flag.min() target = flag.min() - pole
Assumes import numpy as np, import pandas as pd, and a DataFrame df with columns open, high, low and close for the data above.
Sources
- Encyclopedia of Chart Patterns (2nd ed.), T. N. Bulkowski, Wiley, 2005 — success and failure rates from large counts of patterns
- Technical Analysis of Stock Trends, R. D. Edwards & J. Magee, 1948 (later editions revised by W. H. C. Bassetti)
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