Equity Stock Recommendation
Excel trading system, excel buy sell system, intraday trading system, best trading system, trading system for traders, stock analysis, free stock tips, free stock recommendations, excel stock tips, accurate trading, holygrail system,
- This Excel is based completely on integrated automation of order flow system
and chooses from over 200 Stocks and then display Buy/Sell recommendations on every morning of trading day. - On Investment of Rs.20000, we can easily earn more than Rs.15000 per month
(with exposure of 5 times on category A shares) - As this is an advanced version so No need for any confirmation,
- Just enter but with STRICT Stoploss.
- It reduces all your pain of where to enter and in which stock to enter.
- Success rate is very high, believe me its highly profitable.
- Normally targets were met before 11am and trades are completed in morning itself
- You can check accuracy of Excel software by downloading trial.
- Trial file displays only first signal but
after 3.00pm you can view all the signals generated in morning. - File can be accessed anytime and from anywhere on any medium.
For trial, Join whatsapp group:
https://chat.whatsapp.com/BXStlSfhXoSLKH1bWsO7zA
Cost for system is Rs.2500 + taxes (per month)
Call: (+91) 817-877-3712
Call: (+91) 817-877-3712
Above screenshot is for 1st week of april,2019 in which there was clear entries with small SL and relative large targets. I myself trade with my sheets shared here so I know how profitable it is. Watch and see yourself.
This excel is based on order flow system and to understand the order flow system, here's the explanation by well known forex trader, Dali
Algo Trading
Data collection phase:
Firstly, the system needs to have input. This can be anything from technical data like prices of stocks, commodities, indices, volume, option price, open interest, etc. or fundamental data like PE ratios, ROCE, Book Value, OPM, etc.
The data can also be news, special situations, inflation numbers, GDP numbers, interest rates, etc. The data is the key for any algorithm, and utmost care is to be taken to collect the data and clean it.
If the data is incorrect then the results cannot be relied upon. In the computing world, it’s called garbage in and garbage out. The data has to be stored in a structured manner so that it can be retrieved as and when required.
This data can be sliced to in-sample data and out-of-sample data. The in-sample data means the data on which the rules are run and optimised. Out-of-sample data is the data that is unseen. The strategy once finalised is to be tested on this data for authenticity and confirmation.
Development phase:
The data collected has to be processed step by step with some rules or conditions. The conditions can be as simple as two moving averages crossovers or it can be extremely complex.
To create a viable system there has to be a hypothesis or in simple words, an idea. This idea has to be explained to the computer by a language. There are many computer languages which can be used but the most popular ones are Python, C++, Java, Julia, R and Amibroker Formula language.
Language selection depends on coder’s comfort, frequency of trades, acceptable latency, database size, etc. The back-test results are the output of the algorithm.
Care has to be taken for not adding unnecessary rules. Simple strategy with lesser and simple rules is much more robust than a complex strategy with too many conditions.
Optimisation phase:
After coding the idea, the system may or may not produce desirable risk-adjusted returns on the in-sample data. The strategy may be optimised by adding or removing certain rules.
If after multiple attempts, the strategy does not give decent results then the idea needs to be scrapped. The results may be optimised by changing some parameters. Care has to be taken as there is a danger of over-optimisation or curve fitting.
Testing or validation phase:
If the strategy looks decent then it is tested on the out-of-sample data. By decent result, one may assume a reward to risk ratio of at least 1.8 to 1.
The returns should beat the buy on hold returns and the max drawdown should not be more than 35 percent. These metrics are subjective depending on the nature of the strategy and the psychological ability of the executor.
Execution phase:
Only if the strategy survives in the out-of-sample data then it can be tested for execution. The strategy is then connected to the API of a broker for auto execution.
At this stage, some of the mistakes of system traders can be highlighted like wrong assumption of brokerages and slippage, liquidity, coding errors related to signal time and execution time.
The execution part has to be tested toughly with small order size like 1 share per signal for at least a month. Now your system is ready to be deployed on real money.
-From Web
Firstly, the system needs to have input. This can be anything from technical data like prices of stocks, commodities, indices, volume, option price, open interest, etc. or fundamental data like PE ratios, ROCE, Book Value, OPM, etc.
The data can also be news, special situations, inflation numbers, GDP numbers, interest rates, etc. The data is the key for any algorithm, and utmost care is to be taken to collect the data and clean it.
If the data is incorrect then the results cannot be relied upon. In the computing world, it’s called garbage in and garbage out. The data has to be stored in a structured manner so that it can be retrieved as and when required.
This data can be sliced to in-sample data and out-of-sample data. The in-sample data means the data on which the rules are run and optimised. Out-of-sample data is the data that is unseen. The strategy once finalised is to be tested on this data for authenticity and confirmation.
Development phase:
The data collected has to be processed step by step with some rules or conditions. The conditions can be as simple as two moving averages crossovers or it can be extremely complex.
To create a viable system there has to be a hypothesis or in simple words, an idea. This idea has to be explained to the computer by a language. There are many computer languages which can be used but the most popular ones are Python, C++, Java, Julia, R and Amibroker Formula language.
Language selection depends on coder’s comfort, frequency of trades, acceptable latency, database size, etc. The back-test results are the output of the algorithm.
Care has to be taken for not adding unnecessary rules. Simple strategy with lesser and simple rules is much more robust than a complex strategy with too many conditions.
Optimisation phase:
After coding the idea, the system may or may not produce desirable risk-adjusted returns on the in-sample data. The strategy may be optimised by adding or removing certain rules.
If after multiple attempts, the strategy does not give decent results then the idea needs to be scrapped. The results may be optimised by changing some parameters. Care has to be taken as there is a danger of over-optimisation or curve fitting.
Testing or validation phase:
If the strategy looks decent then it is tested on the out-of-sample data. By decent result, one may assume a reward to risk ratio of at least 1.8 to 1.
The returns should beat the buy on hold returns and the max drawdown should not be more than 35 percent. These metrics are subjective depending on the nature of the strategy and the psychological ability of the executor.
Execution phase:
Only if the strategy survives in the out-of-sample data then it can be tested for execution. The strategy is then connected to the API of a broker for auto execution.
At this stage, some of the mistakes of system traders can be highlighted like wrong assumption of brokerages and slippage, liquidity, coding errors related to signal time and execution time.
The execution part has to be tested toughly with small order size like 1 share per signal for at least a month. Now your system is ready to be deployed on real money.
-From Web
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