#FreeTip Highest weekly volume with highest weekly price range ——>
If it happens after a strong uptrend, it is sign of end of trend / consolidation/ reversal .
If it happens before an uptrend above 30 week ma, it is a sign of beginning of a trend.

Great seeing one of the fastest natural drivers of the last decade Robert Kubica today with @WilliamsRacing at @COTA #USGP #f1 #robertkubica #Poland #richenergy

(THREAD) The Whitaker scandal is deepening, as it becomes clear that Sessions' Russia recusal was a coordinated sham and his firing an act of obstruction—and that his replacement is a White House plant inside the Mueller probe. I hope you'll retweet and read on for more details.

1/ From 2014-17 Whitaker worked for World Patent Marketing—which during his tenure defrauded consumers out of $26 million and was successfully prosecuted by the feds. His involvement in the scam confirmed he had loose morals and that the feds would never want to employ him again.

2/ Despite the seeming impossibility of a man with Whitaker's background getting a job at Justice ever again—he'd been found to have used his former title as a US attorney to fraudulently threaten consumers with valid complaints with criminal penalties—Whitaker found an opening.

3/ Within 60 days of parachuting out of World Patent Marketing as it was being fined $26 million by the feds, Whitaker was working for CNN and telling a fellow attorney panelist that his purpose in working for CNN was to get noticed by one man—Donald Trump—and thereby get a job.

4/ Whitaker spent his time at CNN making ludicrous statements about the Mueller probe: there was no obstruction or collusion, he said; Mueller had no authority to look into any aspect of Trump's finances or to subpoena him, he said. All the while, he hoped Trump was watching him.

A Thread on the Boss himself @Mitesh_Engr

Mitesh Sir's Positional Option Selling 101:

• How to find direction
• Which options to sell
• How to deploy capital
• Exit criteria
• What ROI he targets weekly
• What % risk he takes

Done with the help of @niki_poojary

How @Mitesh_Engr Sir finds the direction?

• Daily charts S/R
• 75 min charts S/R
• Intraday trend
• Always play directional
• Never trades in strategies

Which options to sell in weekly expiry according to @Mitesh_Engr?

• Weekly candle High/Low
• Sell 1% away options from those
• Exit when levels breached

Position Sizing by @Mitesh_Engr Sir.

How to deploy your capital?

• First sell 20%
• Pyramid the next day
• When to exit
• What to do when view goes wrong
• What to do with idle capital

Some important tweets of @Mitesh_Engr Sir

• What Data to look at
• ROI for safe players per month
• Max risk per day
• Gather knowledge and play directional
• Never play strategies

प्राचीन काल में गाधि नामक एक राजा थे।उनकी सत्यवती नाम की एक पुत्री थी।राजा गाधि ने अपनी पुत्री का विवाह महर्षि भृगु के पुत्र से करवा दिया।महर्षि भृगु इस विवाह से बहुत प्रसन्न हुए और उन्होने अपनी पुत्रवधु को आशीर्वाद देकर उसे कोई भी वर मांगने को कहा।

सत्यवती ने महर्षि भृगु से अपने तथा अपनी माता के लिए पुत्र का वरदान मांगा।ये जानकर महर्षि भृगु ने यज्ञ किया और तत्पश्चात सत्यवती और उसकी माता को अलग-अलग प्रकार के दो चरू (यज्ञ के लिए पकाया हुआ अन्न) दिए और कहा कि ऋतु स्नान के बाद तुम्हारी माता पुत्र की इच्छा लेकर पीपल का आलिंगन...

...करें और तुम भी पुत्र की इच्छा लेकर गूलर वृक्ष का आलिंगन करना। आलिंगन करने के बाद चरू का सेवन करना, इससे तुम दोनो को पुत्र प्राप्ति होगी।परंतु मां बेटी के चरू आपस में बदल जाते हैं और ये महर्षि भृगु अपनी दिव्य दृष्टि से देख लेते हैं।

भृगु ऋषि सत्यवती से कहते हैं,"पुत्री तुम्हारा और तुम्हारी माता ने एक दुसरे के चरू खा लिए हैं।इस कारण तुम्हारा पुत्र ब्राह्मण होते हुए भी क्षत्रिय सा आचरण करेगा और तुम्हारी माता का पुत्र क्षत्रिय होकर भी ब्राह्मण सा आचरण करेगा।"
इस पर सत्यवती ने भृगु ऋषि से बड़ी विनती की।

सत्यवती ने कहा,"मुझे आशीर्वाद दें कि मेरा पुत्र ब्राह्मण सा ही आचरण करे।"तब महर्षि ने उसे ये आशीर्वाद दे दिया कि उसका पुत्र ब्राह्मण सा ही आचरण करेगा किन्तु उसका पौत्र क्षत्रियों सा व्यवहार करेगा। सत्यवती का एक पुत्र हुआ जिसका नाम जम्दाग्नि था जो सप्त ऋषियों में से एक हैं।

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.