IRCTC - Learning?

Runaway Gaps are traded like flag & pennant patterns. The price move before the gap is projected upwards for the price target. Here the target comes out to be 3230. https://t.co/QuKrekfHG3

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

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.