One of the most underrated aspects of building tech at Level2 is the utilization of optimized quantitative algorithms. (1/12)

These algorithms enable us to identify and visualize trading opportunities that were previously either too complex to execute manually on charts or prohibitively time-consuming.For example, consider a 60-minute Netflix trading chart. (2/12)

Our objective was to pinpoint a Bearish Harmonic pattern to potentially short the stock. (3/12)

I personally favor trading Harmonic patterns due to their foundation in Fibonacci ratios and their demonstrated ability to anticipate potential price reversals or continuations with a high degree of accuracy. (4/12)

(I've also heard that Fibonacci retracement ratios exhibit exceptional accuracy in Indian equities – a research project I've added to my to-do list!)Now, let's delve into the fascinating aspect: the algorithm. (5/12)

One of the core techniques we employ to identify patterns is called "Perceptually Important Points" (PIPs).Imagine a line on a graph representing a variable that changes over time, such as a stock price or daily temperature. (6/12)

PIPs are akin to the most prominent landmarks on this line. They signify points where the line dramatically shifts direction – sharp upward or downward turns, peaks, troughs, or sudden plateaus. These points are pivotal because they illuminate the key moments of change, (7/12)

facilitating a deeper understanding of the data's overall trend and behavior.Think of it like observing a mountain range. The PIPs would correspond to the highest peaks and deepest valleys. By focusing on these points, (8/12)

you can quickly grasp the overall form and character of the mountain range, even without discerning every minute detail. Similarly, PIPs in data help us swiftly comprehend the key events and patterns, simplifying analysis and interpretation. (9/12)

It feels almost illicit to have access to this level of knowledge freely available (thanks, Google Scholar!). While the identification of patterns within time series data remains a relatively unexplored domain, (10/12)

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