Day Trading Stocks & Futures

mohan.sic

Well-Known Member
These all have good dividend yield.

Need an opinion on IRFC and RVNL.

IRFC- I read about this in detail a year back and it has pros and cons.
Worst-case - Max downside 30% and stock will sail at same levels for years with occasional surges.
Best-case- May go up to 80 to 100 levels in 3 years and turn as good dividend stock and follow the pattern of other high dividend psu's.

As of now, I think it's worth giving a shot and accumulate from now.
 

mohan.sic

Well-Known Member
i dont know if i can post here or not, u can contact my friend, he is expert in automation n has already used free data to trade in zerodha :pompus:

u can use his free utility also for nifty options trading :pompus: good luck :pompus:

View attachment 48968

https://github.com/srikar-kodakandla

View attachment 48969

https://github.com/srikar-kodakandl...ions-trading/commits?author=srikar-kodakandla
You friend said he is busy for now or something like that.
But its ok, one of our members here is helping.
 
Both of the stocks exhibit similar trend... wait for further correction before investing
IRFC- I read about this in detail a year back and it has pros and cons.
Worst-case - Max downside 30% and stock will sail at same levels for years with occasional surges.
Best-case- May go up to 80 to 100 levels in 3 years and turn as good dividend stock and follow the pattern of other high dividend psu's.

As of now, I think it's worth giving a shot and accumulate from now.
Both are trading above their 200 SMA after a huge upside (nearly double from bottom).

I can't seem to find the news item which said that IRFC and PTC India were to be included in some indices from April.

1678809946700.png
 

mohan.sic

Well-Known Member
Both are trading above their 200 SMA after a huge upside (nearly double from bottom).

I can't seem to find the news item which said that IRFC and PTC India were to be included in some indices from April.

View attachment 48988
I think we can ignore movements caused by such news like adding/removing the stock to index or TA related views. On long run only it's performance matters. My view on it's stock price in the other post is based only on how as a business it could do and being a psu how the management may distribute the earnings.

I feel we can try on this at 25 levels and below.
 
Both are trading above their 200 SMA after a huge upside (nearly double from bottom).

I can't seem to find the news item which said that IRFC and PTC India were to be included in some indices from April.

View attachment 48988
The way I look at is this.
If I were to invest in this stock, I need to expect a decent returns - say I need 10% net of cost or 12% gross ROI.

What I see is that price is down almost 25% from the recent top. Since the present trend is down, I can assume further cut in price.
Now, the 200 MA is at 25.36 - if I were to assume 2% dip from that level as my exit point. My SL would be 24.85
Normally the investments are 1:2 RR trades, so the risk I am willing to take is 5% from stop level. This would give the entry point at 26.15 and exit at 29.70 minimum.
 
I think we can ignore movements caused by such news like adding/removing the stock to index or TA related views. On long run only it's performance matters. My view on it's stock price in the other post is based only on how as a business it could do and being a psu how the management may distribute the earnings.

I feel we can try on this at 25 levels and below.
The way I look at is this.
If I were to invest in this stock, I need to expect a decent returns - say I need 10% net of cost or 12% gross ROI.

What I see is that price is down almost 25% from the recent top. Since the present trend is down, I can assume further cut in price.
Now, the 200 MA is at 25.36 - if I were to assume 2% dip from that level as my exit point. My SL would be 24.85
Normally the investments are 1:2 RR trades, so the risk I am willing to take is 5% from stop level. This would give the entry point at 26.15 and exit at 29.70 minimum.
Thanks. That 25-26 was also the issue price and previous ceiling, which should act as a support.

1678855951706.png
 

Raj232

Well-Known Member
i will have to explain a lot...
i will explain in short :pompus:
in machine learning, there r several ready made models which we can modify parameters n make changes accordingly n test :pompus:
linear regression model is not very good for predicting future stock prices
for that we need time series prediction models like arima and garch....
i know all these sound big names, etc.... but for me arima is like elliott waves and garch is like moving averages....
this is what google say...

ARIMA models are generally denoted as ARIMA (p,d,q) where p is the order of autoregressive model, d is the degree of differencing, and q is the order of moving-average model. ARIMA models use differencing to convert a non-stationary time series into a stationary one, and then predict future values from historical data.
The ARIMA methodology is a statistical method for analyzing and building a forecasting model which best represents a time series by modeling the correlations in the data. Owing to purely statistical approaches, ARIMA models only need the historical data of a time series to generalize the forecast and manage to increase prediction accuracy while keeping the model parsimonious.
Despite being parsimonious, there are multiple potential disadvantages to using ARIMA models. Most important of them stems from the subjectivity involved in identifying p and q parameters. Although autocorrelation and partial autocorrelations are used, the choice of p and q depend on the skill and experience of the model developer. Additionally, compared to simple exponential smoothing and the Holt Winters method, ARIMA models are more complex and thus, have lower explanatory power.
Lastly, similar to all forecasting methods, by being backward looking, ARIMA models are not good at long term forecasts and are poor at predicting turning points. They can also be computationally expensive.
Thus, ARIMA models can be easily and accurately used for short-term forecasting with just the time series data, but it can take some experience and experimentation to find an optimal set of parameters for each use case.

Heteroskedasticity (GARCH) is a statistical model used in analyzing time-series data where the variance error is believed to be serially autocorrelated. GARCH models assume that the variance of the error term follows an autoregressive moving average process.

anyone can say nifty will go here or there.... n it will go also... but timing the market is most important.... n that is what i m trying to do by using gann time calculations with EW with help of machine learning models :pompus:

just search in google, arima stock predictions :pompus: that will be more helpful :pompus: good luck :pompus:
Thanks @Romeo1998 bhai for the detailed explanation !!
 

Raj232

Well-Known Member

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