In rule based trading you donโt take trades based on what you think is going to happen , rather depend on data to tell you what can probably happen.
2/16
๐๐ฎ๐ป๐ธ๐ป๐ถ๐ณ๐๐ ๐๐๐๐๐ฟ๐ฒ๐ ๐ง๐ฟ๐ฎ๐ฑ๐ถ๐ป๐ด ๐ฆ๐๐ฟ๐ฎ๐๐ฒ๐ด๐
Complete Strategy With Backtest
๐ญ๐ฌ๐ฌ๐ฌ ๐ฅ๐ฒ๐๐๐ฒ๐ฒ๐๐ ๐ณ๐ผ๐ฟ ๐๐ป๐ฑ๐ถ๐ฐ๐ฎ๐๐ผ๐ฟ ๐๐ถ๐ป๐ธ ( ๐๐ฅ๐๐ )
๐งต Thread ๐งต
1/16
In rule based trading you donโt take trades based on what you think is going to happen , rather depend on data to tell you what can probably happen.
2/16
ย 1. Supertrend (5, 4.5)
ย 2. EMA( 21 Period)ย
3. Volume(20 MA Period)
3/16
(Refer Pics)
๐ญ.๐๐ ๐:
Use 21 Period on 5 min time frame
๐ฎ. ๐ฆ๐๐ฝ๐ฒ๐ฟ๐๐ฟ๐ฒ๐ป๐ฑ ( ๐ฑ, ๐ฐ.๐ฑ )
4/16
Time Frame: 5 min
Buy only when :
๐ธ Banknifty is above 21 ema
๐ธ Supertrend gives buy and candle closes above supertrend
๐ธ Enter on next candle opening
๐ธ Volume>20 MA on Supertrend Buy Signal
๐ธ Take Entry only from 9:20 to 3:15
๐ธ Max 2 Entries per day
5/16
Pic 2 . When not to take a trade ?
Pic 3 . M2M Stoploss Hit
Pic 4. Long Short in single day
6/16
๐ธ Stoploss is 6500 Rs Per lot or Closing below supertrend
๐ธ Target 20,000 Rs
๐ธ We do not take buy trades if already the supertrend is green on day open .
๐ธ We only take trades when there is fresh signal on a particular day
7/16
๐ธ Time Frame: 5 min
๐ธ Sell only when
๐ธ Banknifty is Below 21 ema
๐ธ Supertrend gives Sell and candle closes below supertrend
๐ธ Enter on next candle opening
๐ธ Volume > 20 MA on Supertrend sell Signal
๐ธ Take Entry only from 9:20 to 3:15
8/16
๐ธ Stoploss is 6500rs Per lot or Closing above supertrend
๐ธ Target 20,000
๐ธ We do not take sell trades if already the supertrend is green on day open . We only take trades when there is fresh signal on a particular day
๐ธ Max 2 entries per day
9/16
Pic 2 : Trade not Taken on next day as fresh signal was not generated
Pic 3 : Trades explained
10/16
Starting Capital : 2.5 Lakh
Profit : 7 Lakh in 3 years
11/16
14/16
๐ฅ๐ฒ๐๐๐ฒ๐ฒ๐ the first Tweet and Watch the video to understand the strategy better !
15/16
16/16
\U0001d5d5\U0001d5ee\U0001d5fb\U0001d5f8\U0001d5fb\U0001d5f6\U0001d5f3\U0001d601\U0001d606 \U0001d5d9\U0001d602\U0001d601\U0001d602\U0001d5ff\U0001d5f2\U0001d600 \U0001d5e7\U0001d5ff\U0001d5ee\U0001d5f1\U0001d5f6\U0001d5fb\U0001d5f4 \U0001d5e6\U0001d601\U0001d5ff\U0001d5ee\U0001d601\U0001d5f2\U0001d5f4\U0001d606
— JayneshKasliwal (@JayneshKasliwal) July 16, 2022
Complete Strategy With Backtest
\U0001d7ed\U0001d7ec\U0001d7ec\U0001d7ec \U0001d5e5\U0001d5f2\U0001d601\U0001d604\U0001d5f2\U0001d5f2\U0001d601\U0001d600 \U0001d5f3\U0001d5fc\U0001d5ff \U0001d5dc\U0001d5fb\U0001d5f1\U0001d5f6\U0001d5f0\U0001d5ee\U0001d601\U0001d5fc\U0001d5ff \U0001d5df\U0001d5f6\U0001d5fb\U0001d5f8 ( \U0001d5d9\U0001d5e5\U0001d5d8\U0001d5d8 )
\U0001f9f5 Thread \U0001f9f5
1/16
More from JayneshKasliwal
๐๐ผ๐ ๐๐ผ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐๐ผ๐๐ฟ ๐ผ๐๐ป ๐๐ต๐ฎ๐ฟ๐๐ถ๐ป๐ธ ๐ฆ๐ฐ๐ฟ๐ฒ๐ฒ๐ป๐ฒ๐ฟ
๐งต Thread ๐งต
[1/15]
๐๐ต๐ฎ๐ฟ๐๐ถ๐ป๐ธ ๐ฆ๐ฐ๐ฟ๐ฒ๐ฒ๐ป๐ฒ๐ฟ is a Stock screening tool that is useful to screen stocks based on Technical & Fundamentals.
๐ธ It scans stocks by using technical indicators like RSI, MACD, divergences, etc.
๐ธ It also uses fundamental tools like Book Value, EPS,PE etc
[2/15]
How To Create A Scan ?
Go to https://t.co/JJEJBg3Cd8 & click on ๐๐ณ๐ฆ๐ข๐ต๐ฆ ๐๐ค๐ข๐ฏ under ๐๐ค๐ณ๐ฆ๐ฆ๐ฏ๐ฆ๐ณ๐ด.
[3/15]
๐๐ถ๐ฟ๐๐ ๐ฆ๐๐ฒ๐ฝ ๐ง๐ผ ๐๐ถ๐น๐๐ฒ๐ฟ ๐ฆ๐๐ผ๐ฐ๐ธ๐
We can select segments of stocks of our choice, e.g. cash or futures stocks, Nifty 100 etc.
Click on green โ+โ icon to add conditions.
[4/15]
๐๐ฎ๐ป๐ฑ๐น๐ฒ ๐ง๐ถ๐บ๐ฒ๐ณ๐ฟ๐ฎ๐บ๐ฒ๐ & ๐๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ถ๐๐ผ๐ป ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ผ๐ฟ๐๐ธ We can select any timeframe of candles in Chartink, from minutes to years.
๐ธ And compare two candles using comparison operators like ๐๐ฒ๐ถ๐ข๐ญ ๐ต๐ฐ or ๐๐ณ๐ฆ๐ข๐ต๐ฆ๐ณ ๐๐ฉ๐ข๐ฏ , crossovers etc
[5/15]
๐งต Thread ๐งต
[1/15]
๐๐ต๐ฎ๐ฟ๐๐ถ๐ป๐ธ ๐ฆ๐ฐ๐ฟ๐ฒ๐ฒ๐ป๐ฒ๐ฟ is a Stock screening tool that is useful to screen stocks based on Technical & Fundamentals.
๐ธ It scans stocks by using technical indicators like RSI, MACD, divergences, etc.
๐ธ It also uses fundamental tools like Book Value, EPS,PE etc
[2/15]
How To Create A Scan ?
Go to https://t.co/JJEJBg3Cd8 & click on ๐๐ณ๐ฆ๐ข๐ต๐ฆ ๐๐ค๐ข๐ฏ under ๐๐ค๐ณ๐ฆ๐ฆ๐ฏ๐ฆ๐ณ๐ด.
[3/15]
๐๐ถ๐ฟ๐๐ ๐ฆ๐๐ฒ๐ฝ ๐ง๐ผ ๐๐ถ๐น๐๐ฒ๐ฟ ๐ฆ๐๐ผ๐ฐ๐ธ๐
We can select segments of stocks of our choice, e.g. cash or futures stocks, Nifty 100 etc.
Click on green โ+โ icon to add conditions.
[4/15]
๐๐ฎ๐ป๐ฑ๐น๐ฒ ๐ง๐ถ๐บ๐ฒ๐ณ๐ฟ๐ฎ๐บ๐ฒ๐ & ๐๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ถ๐๐ผ๐ป ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ผ๐ฟ๐๐ธ We can select any timeframe of candles in Chartink, from minutes to years.
๐ธ And compare two candles using comparison operators like ๐๐ฒ๐ถ๐ข๐ญ ๐ต๐ฐ or ๐๐ณ๐ฆ๐ข๐ต๐ฆ๐ณ ๐๐ฉ๐ข๐ฏ , crossovers etc
[5/15]
More from All
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
๐งต๐(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
๐งต๐(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
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The entire discussion around Facebookโs disclosures of what happened in 2016 is very frustrating. No exec stopped any investigations, but there were a lot of heated discussions about what to publish and when.
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didnโt have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I canโt speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
The story doesn\u2019t say you were told not to... it says you did so without approval and they tried to obfuscate what you found. Is that true?
— Sarah Frier (@sarahfrier) November 15, 2018
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didnโt have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I canโt speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
