Long back, I read a book “Encyclopedia of Chart Patterns” by Thomas N. Bukowski. It’s a brilliant work (Must read).
Thread: 5 Important bullish & bearish chart patterns in technical analysis.
Triangles, Broadenings and Rectangles.
#trading #investing
Long back, I read a book “Encyclopedia of Chart Patterns” by Thomas N. Bukowski. It’s a brilliant work (Must read).
I read about those patterns at many other places & in different theories and I realised that most of them are-
Triangles, Broadenings & Brackets or Rectangle patterns are most common & effective patterns. Below are 5 bullish & bearish proven, time-tested patterns of these categories.
Rectangle Breakout:
Horizontal pattern breakout. Price remains between two horizontal lines before upward brkout. Its a decent continuation breakout pattern when appears in uptrend. Reversal pattern when appear in downtrend (Decent Risk-reward). Tend to pullback
Two-bottoms around same level followed by the breakout. Good reversal patterns when they appear in downtrend. If you don’t wait for the breakout, the failure rate is significantly high. Many call it double bottom even before the breakout, they must know it.
Pattern of convergence followed by upside breakout. Nice continuation pattern if appears in uptrend. Bullish triangles breakouts have got better performance. High volume at breakout would be nice.
The top is horizontal in the triangle and the bottoms are upsloping. Ascending triangles are bullish in nature. Resistance at top but bottoms are rising. Bullish breakouts have better ratio. The reward to risk ratio in triangle patterns is high.
Price remains within the down slopping and converging trendlines followed by upside breakout. I also call it falling triangle. It is a decent bullish trend reversal pattern.
Symmetrical triangle - bearish
Symmetrical triangle appears in the downtrend followed by the bearish breakout. It is a bearish continuation pattern having high risk-reward ratio.
Top is descending and bottom is horizontal in the triangle pattern. Descending tops indicate supply and horizontal bottom is a strong support. The pattern is bearish in nature and downside breakout is a decent trade setup.
Also known as Megaphone or Expanding triangle. When it appears in downtrend, its more of a continuation pattern (downside breakouts are better) than reversal. When it appears in uptrend, downside breakout would be a better setup.
The top is horizontal in the broadening pattern. The lows are following the down slopping trendline. New lows are being made and there is a strong resistance at top. Downside breakout would be a bearish setup.
The pattern that looks like letter J reversed. It is a bearish continuation pattern. The trend is down and established. The bearish trades are expected to have a better risk-reward.
More from Prashant Shah
Thread: P&F Super Pattern
An effective price pattern defined using properties of P&F charts.
#Superpattern #Pointandfigure #Definedge
Point & Figure is an oldest charting method where price is plotted vertically, and the chart moves only when price moves. It is a different way of looking at the price, the objective box-value and reversal value offers advantage of identifying objective price patterns.
When price is moving up, it is plotted in a column of 'X'. When it is going down, it is plotted in a column of ‘O’. Normally, three-box reversal criteria is used to define the trend & reversal. Unlike a bar or candle, the P&F column can have multiple sessions in it.
Link to know more about the subject:
https://t.co/2xtLAVPBvm
See below chart. Price is in a strong uptrend, P&F chart would produce a long of column of 'X' with more number of boxes in it.
If such a trend is followed by some time bars without meaningful price correct, P&F chart would not move, and it will remain in column of 'X' in such a scenario.
An effective price pattern defined using properties of P&F charts.
#Superpattern #Pointandfigure #Definedge
Point & Figure is an oldest charting method where price is plotted vertically, and the chart moves only when price moves. It is a different way of looking at the price, the objective box-value and reversal value offers advantage of identifying objective price patterns.
When price is moving up, it is plotted in a column of 'X'. When it is going down, it is plotted in a column of ‘O’. Normally, three-box reversal criteria is used to define the trend & reversal. Unlike a bar or candle, the P&F column can have multiple sessions in it.
Link to know more about the subject:
https://t.co/2xtLAVPBvm
See below chart. Price is in a strong uptrend, P&F chart would produce a long of column of 'X' with more number of boxes in it.
If such a trend is followed by some time bars without meaningful price correct, P&F chart would not move, and it will remain in column of 'X' in such a scenario.
More from All
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
