3 Ways to ride a stock (a thread) >Do retweet if it adds value as this took a long time for me to make

1) 10/20 Day EMA - Enter the stock as it comes up back to the 10/20 day EMA and ride it till it drops below or SL hits as explained below

Eg -Nitin Spinners as below chart

THE LAST PRINCESS OF LAHORE - BAMBA DULEEP SINGH (1869 - 1957)

Thread

Post partition, every morning a weak old woman would board the Model Town bus service headed to the city. The conductor never asked her for money or he would invite anger of the last Queen of Punjab.

1/n

Princess Bamba Sutherland, the eldest daughter of Maharaja Duleep Singh and grand-daughter of Maharaja Ranjit Singh, was born on Sep 29, 1869, in London.

Duleep Singh, the last Maharaja of Punjab was taken away to England by British after annexing Punjab.

2/n

Since he was still a child he was forcibly converted to Christianity and made to adopt British values in order to prevent him from ever thinking about regaining Punjab.

He was also kept away from his mother Maharani Jindan.
When he finally met his mother years later...

3/n

...he was told about his lost kingdom and religion after which he decided to reclaim it from British treachery.
During his struggle he married and had six children, 3 sons & 3 daughters from the 1st marriage.

Bamba was the most colorful character among 6 and was a rebel...
4/n

...like her father and daring like her Grandfather Ranjit Singh. She married Col Sutherland who later became principal of King Edward Medical College.

When she got permission to bring her grandmother Maharani Jindan's ashes to Lahore in 1924, she saw a huge crowd gathered...
5/n

The thread explains my momentum scanner and how to build it in #TradePoint software.
@Definedge
Conditions :
1)EMA 8 above EMA 34 and rising
2)Candle close above previous 5 candle high
3) RSI above 60
4) Volume above previous 5 candles
5) ADX above 30
1/n

Select Double Moving Average 8 and 34
2/n

Under Patterns select Close Above Prev Bars
3/n

Qualify the candle close
4/n

RSI above 60
5/n

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.