Pattern Match & Forward Projection – Weekly (v6.0)

📌 Pattern Match & Forward Projection – Weekly (v6.0)
This indicator is designed to spot situations in the past that resemble the current price behavior and then project their possible future outcomes.
🔍 How it works
Dynamic Lookback
The algorithm scans a wide history of candles (in this case, 750 weekly bars).
Within this history, it searches for price sequences that closely resemble the recent move.
Pattern Matching
When a match is found, the indicator assigns a validity score (e.g., 82%).
This value shows how similar the current structure is to those past cases.
Quality Filters
Users can fine-tune thresholds and parameters (e.g., terminal mean curve, max distance allowed, shape filter).
These filters reduce “false positives” and keep only the most significant matches.
Forward Projection
Once a match is confirmed, the indicator calculates the average forward performance based on how price behaved after similar cases in the past.
It then displays percentage statistics:
+1w, +2w, +3w, +4w, +5w …
each showing the historical average move over those timeframes.
📊 What we see on this chart (CRM – Salesforce, weekly)
A match was found with 82% validity.
Historically, in similar setups, price performance showed on average:
+0.87% after 1 week
+2.89% after 2 weeks
+7.25% after 3 weeks
+2.81% after 4 weeks
+3.39% after 5 weeks
The dashed lines on the chart represent projected price levels based on these statistical averages.
⚙️ In summary
This tool doesn’t produce “magical predictions.” Instead, it applies statistical analysis and pattern recognition: it compares the present with the past and highlights the probabilities of future moves based on historical analogs.
🔧 You can customize:
the lookback window (how many bars to scan),
the similarity threshold,
the shape filter,
and the forward periods to track.
👉 In short, the Pattern Match & Forward Projection is a practical tool for traders who want to complement traditional technical analysis with a quantitative, probability-driven approach, rooted in the market’s own memory