OHLC - Open, High, Low & Close

Everybody that looks into the charts absolutely must know what OHLC means.

Doesn't matter which TF you are looking at, each candle consists of these.

- Wick is the thinnest of the volume of the candle
- Body is the strongest - Most Volume traded

Post Independence, Army wanted to select the most experienced Indian commander as Army chief. But Nehru without consulting anyone appointed Gen Rob Lockhart, a senior British Army officer as the Commander-in-Chief (C-in-C) of the Indian Army.

Most people were shocked to know that Nehru selected a British Man after fighting for independence for over 200 years! Major Gen A A ‘Jick’ Rudra of the Indian Army was completely upset with the decision.

One day Lockhart met Nehru with a strategic plan and proposed many government directive defence policies for the country. He placed it on the table and explained the point to PM Nehru. After a while Lockhart walked out of the room and met AA Jick Rudra,+

Lockhart looked shell shocked and stunned. When asked what happened he said “The PM took one look at my paper and blew his top. Rubbish! Total rubbish! We don’t need a defence plan. Our policy is ahimsa (non-violence). We foresee no military threats. Scrap the army!”

One year after the incident the Kashmir crisis had increased and Gen Lockhart used to exchange ideas with the Gen Douglas Gracey who was commander in chief of Pakistan Army.There were many infiltrations along the border which Lockhart knew and took no action. But Lt Gen Cariappa+

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

Make them irrelevant > Hold a grudge

Holding a grudge is the easy thing to do.

But holding a grudge is not the right thing to do.

A grudge puts poison in your body & rewires your subconscious mind.

You consume the poison & destroy your reality while your attacker sleeps like a baby.