Nice Run. Eight weeks into the new #Gold Weekly Cycle and now there is some renewed interest.

Gold now stands at the fork and an important tell is up ahead.

My views are in this blog post from few weeks back.

Link >> https://t.co/RrA0WhiZQb

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@kuttrapali26 @AmitabhJha3
#stocks #StockMarket
1/n

Moving averages
EMA and SMA
1. Exponential Moving Average (EMA) is similar to Simple Moving Average (SMA), measuring trend direction over a period of time.
2. However, whereas SMA simply calculates an average of price data, EMA applies more weight to data that is more current
2/

21 EMA : SHORT TERM TREND
1. Calculates the Average Price of past 21 days
2. A stock above 21 ema is in strong momentum , which will give pullbacks to 21 ema again and again.
3. Stock above 21 ema should never be shorted so as to stay with the trend
3/n

3. A stock which moves 10-15% above 21 ema will be too extended and hence will generally pullback
Stocks take dynamic support on 21 ema
4. You can Short only if its too extended like 15 to 20% from 21 ema ( Not advised )
#ADANIPOWER:
#pnbhousing
4/n

1. Whenever 21 ema is sloping upwards its called uptrend
2. Whenever 21 ema is sloping downwards its called dowtrend
3. Whenever 21 ema is sideways its called consolidation
⏫Buy in uptrend
⏬Sell in downtrend
Examples:
5/n

दधीचि ऋषि को मनाही थी कि वह अश्विनी कुमारों को किसी भी अवस्था में ब्रह्मविद्या का उपदेश नहीं दें। ये आदेश देवराज इन्द्र का था।वह नहीं चाहते थे कि उनके सिंहासन को प्रत्यक्ष या परोक्ष रुप से कोई भी खतरा हो।मगर जब अश्विनी कुमारों ने सहृदय प्रार्थना की तो महर्षि सहर्ष मान गए।

और उन्होनें ब्रह्मविद्या का ज्ञान अश्विनि कुमारों को दे दिया। गुप्तचरों के माध्यम से जब खबर इन्द्रदेव तक पहुंची तो वे क्रोध में खड़ग ले कर गए और महर्षि दधीचि का सर धड़ से अलग कर दिया।मगर अश्विनी कुमार भी कहां चुप बैठने वाले थे।उन्होने तुरंत एक अश्व का सिर महर्षि के धड़ पे...

...प्रत्यारोपित कर उन्हें जीवित रख लिया।उस दिन के पश्चात महर्षि दधीचि अश्वशिरा भी कहलाए जाने लगे।अब आगे सुनिये की किस प्रकार महर्षि दधीचि का सर काटने वाले इन्द्र कैसे अपनी रक्षा हेतु उनके आगे गिड़गिड़ाए ।

एक बार देवराज इन्द्र अपनी सभा में बैठे थे, तो उन्हे खुद पर अभिमान हो आया।

वे सोचने लगे कि हम तीनों लोकों के स्वामी हैं। ब्राह्मण हमें यज्ञ में आहुति देते हैं और हमारी उपासना करते हैं। फिर हम सामान्य ब्राह्मण बृहस्पति से क्यों डरते हैं ?उनके आने पर क्यों खड़े हो जाते हैं?वे तो हमारी जीविका से पलते हैं। देवर्षि बृहस्पति देवताओं के गुरु थे।

अभिमान के कारण ऋषि बृहस्पति के पधारने पर न तो इन्द्र ही खड़े हुए और न ही अन्य देवों को खड़े होने दिया।देवगुरु बृहस्पति इन्द्र का ये कठोर दुर्व्यवहार देख कर चुप चाप वहां से लौट गए।कुछ देर पश्चात जब देवराज का मद उतरा तो उन्हे अपनी गलती का एहसास हुआ।

Mangottu Bhagavathi Temple
#Palakkad #Kerala
#keralatemples 🚩

Mangottu Bhagavathi Temple is situated in Athipotta near Alathur of palakkad district. Devi is kshipraprasadi here and grants relief to those who worship with full faith.
Mookken Chathen, situated on the Northern 👇

side of the temple, is guarding Devi here.
Story says that, once there lived a weaver named Kuttan who was a Devi Bhaktan. During one festival time Kuttan was so pleased by the Devi Vigraham that he requested her to come along with him. At that time wind blew heavily and Cloths

in Vigraham flew. Kuttan immediately decorated Devi with his cloth which stuck to Vigraham and returned to his village and with his Umbrella. Devi came with him in that Umbrella and stayed in his place. This place is Mangottu Bhagavathi temple now.

Amme Narayana
🙏🕉🙏

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.