Metadata-Version: 2.1
Name: nseta
Version: 0.6.182
Summary: Library to analyse and predict financial data from National Stock Exchange (NSE - India) in pandas dataframe 
Home-page: https://github.com/pkjmesra/nseta
Author: Praveen K Jha
Author-email: pkjmesra@gmail.com
License: OSI Approved (MIT)
Download-URL: https://github.com/pkjmesra/nseta/archive/v0.6.182.zip
Description: # nseta :nerd_face:
        
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        Python Library (and console/CLI application) to 
        -  Get publicly available data on [NSE India website](https://www1.nseindia.com/) <img src="/docs/assets/NSE_Logo.svg" width="118" height="28" alt="NSE India" > ie. stock live-quotes and historical data[ *EQUITY ONLY* AT THIS TIME. No support for Futures/Options/Derivatives yet.]
        -  Plot various technical indicators
        -  Pattern recognition and fitment using candlestick charts
        -  Backtest trading strategies
        -  Forecasting with standard as well as custom strategies
        -  Create scanners and generate signals for various technical indicators or for BUY/SELL
        -  Create and build your own trading strategy
        
        ## Disclaimer
        - The recommendations that you receive when you run the scan for intraday or swing trading is only for academic research purposes.
        - Though, you are free to take the recommended BUY/SELL positions, any loss you make from those are entirely at your own risk.
        - The author of this library/console cannot be held responsible and is deemed free from any legal liability.
        
        ## Donate
        
        [![Otechie](https://api.otechie.com/pkjmes/badge.svg)](https://otechie.com/pkjmes)
        
        ## Libraries Required
        - (See requirements.txt file for more)
        
        For Windows systems you can install Anaconda, this will cover many dependancies (You'll have to install requests and beautifulsoup additionally though)
        
        ## Installation
        -  From code
        ```
        python3 setup.py clean build install
        ```
        -  Python pip
        ```
        pip3 install nseta
        ```
        -  Installing specific version
        ```
        pip3 install nseta==0.6.68
        ```
          You can also directly install specific versions from pypi.org:
        ```
        pip install --index-url https://pypi.org/simple/ --extra-index-url https://pypi.org/simple nseta==<Specific_Version>
        ```
        
        -  Python shell
        ``` 
        python3 -m pip install --upgrade nseta
        ```
        -  Wheel (.whl) file from PyPi.org
          Just go ahead and download the ```.whl``` file from ```https://pypi.org/project/nseta/#files``` and install from the downloaded directory:
        
        ```
        pip3 install ./nseta-0.6.68-py3-none-any.whl
        ```
          where 0.6.68 is the version of the library.
        
        - Specific test versions(under development) can be installed from test.pypi.org
        ```
        pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple nseta==<Specific_Version>
        ```
        
        After installation you can check what version you got installed
        ```
        python3 -c "import nseta; print(nseta.__version__)"
        ```
        
        ## Usage
        
        - Top level command options that provide you with various features
        ```python
        
        #Usage Commands (You can use nsetacli or nseta - either is good.)
        $ nseta
        Usage: nseta [OPTIONS] COMMAND [ARGS]...
        
        Options:
          --debug / --no-debug  --debug to turn debugging on. Default is off
          --trace / --no-trace  --trace to turn tracing on (works only with --debug). Default is off.
          --version             Shows the version of this library
          --help                Show this message and exit.
        
        Commands:
          create-cdl-model       Create candlestick model.Plot uncovered patterns
          forecast-strategy      Forecast & measure performance of a trading model
          history                Get price history of a security for given dates
          live-quote             Get live price quote of a security
          pe-history             Get PE history of a security for given dates
          plot-ta                Plot various technical analysis indicators
          scan                   Scan live price quotes and calculate RSI for...
          test-trading-strategy  Measure the performance of your trading strategy
        ```
        - Sample commands
        ```python
        
          Example:
          nseta create-cdl-model -S bandhanbnk -s 2019-07-30 -e 2020-11-20 --steps
          nseta forecast-strategy -S bandhanbnk -s 2019-07-30 -e 2020-11-20 --strategy rsi
          nseta history -S bandhanbnk -s 2019-07-30 -e 2020-11-20
          nseta live-quote -S bandhanbnk
          nseta pe-history -S bandhanbnk -s 2019-07-30 -e 2020-11-20
          nseta plot-ta -S bandhanbnk -s 2019-07-30 -e 2020-11-20
          nseta test-trading-strategy -S bandhanbnk -s 2019-07-30 -e 2020-11-20 --strategy rsi
          nseta scan -S HDFC,ABB -s
          nseta scan -i
        ```
        - Test your trading strategies
        
        ```python
        nseta test-trading-strategy
        Please provide start and end date in format yyyy-mm-dd
        Usage:  [OPTIONS]
        
          Measure the performance of your trading strategy
        
        Options:
          -S, --symbol TEXT               Security code
          -s, --start TEXT                Start date in yyyy-mm-dd format
          -e, --end TEXT                  End date in yyyy-mm-dd format
          --strategy [rsi|smac|macd|emac|bbands|multi|custom]
                                          rsi, smac, macd, emac, bbands, multi,
                                          custom. Choose one.
          -u, --upper TEXT                Used as upper limit, for example, for RSI.
                                          Only when strategy is "custom", we buy the
                                          security when the predicted next day return
                                          is > +{upper} %
          -l, --lower TEXT                Used as lower limit, for example, for RSI.
                                          Only when strategy is "custom", we sell the
                                          security when the predicted next day return
                                          is < -{lower} %
          --autosearch / --no-autosearch  --auto for allowing to automatically measure
                                          the performance of your trading strategy on
                                          multiple combinations of parameters.
          -i, --intraday                  Test trading strategy for the current
                                          intraday price history (Optional)
          --help                          Show this message and exit.
        ```
        
        - Test your trading strategies (for example, using *RSI* as technical indicator)
        
        ```python
        $ nseta test-trading-strategy -S bandhanbnk -s 2020-01-01 -e 2020-10-03 --strategy rsi --autosearch
        
        init_cash : 100000
        buy_prop : 1
        sell_prop : 1
        commission : 0.0075
        ===Strategy level arguments===
        rsi_period : 14
        rsi_upper : 70
        rsi_lower : 15
        Final Portfolio Value: 162418.36025
        Final PnL: 62418.36
        
        Time used (seconds): 0.13728976249694824
        Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'execution_type': 'close', 'channel': None, 'symbol': None, 'rsi_period': 14, 'rsi_upper': 70, 'rsi_lower': 15}
        Optimal metrics: {'rtot': 0.4850052910757702, 'ravg': 0.00255265942671458, 'rnorm': 0.9026928562651005, 'rnorm100': 90.26928562651005, 'sharperatio': None, 'pnl': 62418.36, 'final_value': 162418.36025}
           rsi_period  rsi_upper  rsi_lower  init_cash    final_value       pnl
        0          14         70         15     100000  162418.360250  62418.36
        1          11         70         15     100000  154007.773625  54007.77
        2           7         70         15     100000   96213.602375  -3786.40
        3          14         70         30     100000   83074.073000 -16925.93
        4          11         70         30     100000   78397.304875 -21602.70
        ```
        ![](./docs/assets/trading_strategy_rsi.png)
        
        - Check historical data and export to csv file
        
        ```
        $ nseta history -S bandhanbnk -s 2019-01-01 -e 2020-09-30
               Symbol Series        Date  Prev Close    Open   High     Low    Last   Close    VWAP   Volume      Turnover  Trades  Deliverable Volume  %Deliverable
        0  BANDHANBNK     EQ  2019-01-01      550.15  552.50  560.0  544.10  558.00  556.70  552.21   589317  3.254256e+13   16658              175430        0.2977
        1  BANDHANBNK     EQ  2019-01-02      556.70  553.00  563.7  549.60  551.40  552.15  556.91   834846  4.649319e+13   32119              250782        0.3004
        2  BANDHANBNK     EQ  2019-01-03      552.15  551.00  554.0  530.00  532.05  533.80  540.61   620161  3.352631e+13   18616              282037        0.4548
        3  BANDHANBNK     EQ  2019-01-04      533.80  534.25  541.7  527.05  528.05  528.90  533.42   579027  3.088645e+13   22405              186702        0.3224
        4  BANDHANBNK     EQ  2019-01-07      528.90  540.00  542.0  495.55  495.55  498.05  509.49  2684675  1.367813e+14   76816             1160901        0.4324
        Saved to: bandhanbnk.csv
        ```
        - Create candlestick model
        ```python
        nseta create-cdl-model
        Usage:  [OPTIONS]
        
          Create candlestick model.Plot uncovered patterns
        
        Options:
          -S, --symbol TEXT       Security code
          -s, --start TEXT        Start date in yyyy-mm-dd format
          -e, --end TEXT          End date in yyyy-mm-dd format
          -o, --file TEXT         Output file name. Default is {symbol}.csv
          --steps / --no-steps    --steps for saving intermediate steps in output file
          -f, --format [csv|pkl]  Output format, pkl - to save as Pickel and csv - to
                                  save as csv
          --help                  Show this message and exit.
        ```
        - Create candlestick models with pattern recognition
        ```
        $ nseta create-cdl-model -S bandhanbnk -s 2019-01-01 -e 2020-09-30 --steps
                        Symbol Series  Prev Close    Open   High  ...  CDLUNIQUE3RIVER  CDLUPSIDEGAP2CROWS  CDLXSIDEGAP3METHODS  candlestick_pattern  candlestick_match_count
        Date                                                      ...                                                                                                        
        2019-01-01  BANDHANBNK     EQ      550.15  552.50  560.0  ...                0                   0                    0           CDLHARAMI_Bull                      0.0
        2019-01-02  BANDHANBNK     EQ      556.70  553.00  563.7  ...                0                   0                    0           CDLHARAMI_Bull                      0.0
        2019-01-03  BANDHANBNK     EQ      552.15  551.00  554.0  ...                0                   0                    0           CDLMATCHINGLOW_Bull                      0.0
        2019-01-04  BANDHANBNK     EQ      533.80  534.25  541.7  ...                0                   0                    0           CDLBELTHOLD_Bull                      0.0
        2019-01-07  BANDHANBNK     EQ      528.90  540.00  542.0  ...                0                   0                    0           CDLTHRUSTING_Bear                      0.0
        
        [5 rows x 72 columns]
        Model saved to: bandhanbnk.csv
        Candlestick pattern model plot saved to: bandhanbnk_candles.html
        ```
        ![](./docs/assets/cdl_model.png)
        
        - Create various plots for analysis with technical indicators 
        ```
        $ nseta plot-ta -S bandhanbnk -s 2019-01-01 -e 2020-09-30
        ```
        ![](./docs/assets/ti_plots.png)
        
        - Forecast strategies
        ```python
        nseta forecast-strategy
        Usage:  [OPTIONS]
        
          Forecast & measure performance of a trading model
        
        Options:
          -S, --symbol TEXT               Security code
          -s, --start TEXT                Start date in yyyy-mm-dd format
          -e, --end TEXT                  End date in yyyy-mm-dd format
          --strategy [rsi|smac|macd|emac|bbands|multi|custom]
                                          rsi, smac, macd, emac, bbands, multi,
                                          custom. Choose one.
          -u, --upper FLOAT               Only when strategy is "custom". We buy the
                                          security when the predicted next day return
                                          is > +{upper} %
          -l, --lower FLOAT               Only when strategy is "custom". We sell the
                                          security when the predicted next day return
                                          is < -{lower} %
          --help                          Show this message and exit.
        ```
        - Create forecast strategies and verify them
        ```python
        $ nseta forecast-strategy -S bandhanbnk -s 2019-01-01 -e 2020-09-30 --upper 1.5 --lower 1.5
        Initial log joint probability = -6.20343
            Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
              99       930.108     0.0162936       321.927           1           1      117   
            Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
             199       959.793     0.0202279       367.334          10           1      235   
            Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
             201       959.932   0.000323678       119.582    8.93e-07       0.001      274  LS failed, Hessian reset 
             299       966.946    0.00436297       112.347      0.8895      0.8895      391   
            Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
             313       969.159   0.000423916       207.361   9.919e-07       0.001      450  LS failed, Hessian reset 
             399       974.294   0.000208377        85.133      0.5089      0.5089      552   
            Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
             487       980.981   0.000350673         190.2   2.604e-06       0.001      700  LS failed, Hessian reset 
             499       981.522   0.000224398       86.8409      0.8047      0.8047      713   
            Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
             595       982.077    0.00011557       96.0631   1.437e-06       0.001      871  LS failed, Hessian reset 
             599       982.082   4.96415e-05       69.7541      0.5502           1      876   
            Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
             643       982.086   5.63279e-06       71.6814   6.367e-08       0.001      975  LS failed, Hessian reset 
             663       982.086   7.38231e-09       89.4916     0.07783     0.07783     1004   
        Optimization terminated normally: 
          Convergence detected: absolute parameter change was below tolerance
        Starting Portfolio Value: 100000.00
        ===Global level arguments===
        init_cash : 100000
        buy_prop : 1
        sell_prop : 1
        commission : 0.0075
        ===Strategy level arguments===
        Upper limit:  1.5
        Lower limit:  -1.5
        2019-01-02, BUY CREATE, 552.15
        2019-01-02, Cash: 100000.0
        2019-01-02, Price: 552.15
        2019-01-02, Buy prop size: 179
        2019-01-02, Afforded size: 179
        2019-01-02, Final size: 179
        2019-01-03, BUY EXECUTED, Price: 552.15, Cost: 98834.85, Comm: 741.26, Size: 179.00
        2019-01-11, SELL CREATE, 443.20
        2019-01-14, SELL EXECUTED, Price: 443.20, Cost: 98834.85, Comm: 595.00, Size: -179.00
        2019-01-14, OPERATION PROFIT, GROSS: -19502.05, NET: -20838.31
        2019-02-06, BUY CREATE, 440.40
        2019-02-06, Cash: 79161.692625
        2019-02-06, Price: 440.4
        2019-02-06, Buy prop size: 178
        2019-02-06, Afforded size: 178
        2019-02-06, Final size: 178
        2019-02-07, BUY EXECUTED, Price: 440.40, Cost: 78391.20, Comm: 587.93, Size: 178.00
        2019-03-01, SELL CREATE, 486.50
        2019-03-05, SELL EXECUTED, Price: 486.50, Cost: 78391.20, Comm: 649.48, Size: -178.00
        2019-03-05, OPERATION PROFIT, GROSS: 8205.80, NET: 6968.39
        2019-04-05, BUY CREATE, 548.15
        2019-04-05, Cash: 86130.08112500001
        2019-04-05, Price: 548.15
        2019-04-05, Buy prop size: 155
        2019-04-05, Afforded size: 155
        2019-04-05, Final size: 155
        2019-04-08, BUY EXECUTED, Price: 548.15, Cost: 84963.25, Comm: 637.22, Size: 155.00
        2019-07-12, SELL CREATE, 549.40
        2019-07-15, SELL EXECUTED, Price: 549.40, Cost: 84963.25, Comm: 638.68, Size: -155.00
        2019-07-15, OPERATION PROFIT, GROSS: 193.75, NET: -1082.15
        2019-10-01, BUY CREATE, 470.35
        2019-10-01, Cash: 85047.92925
        2019-10-01, Price: 470.35
        2019-10-01, Buy prop size: 179
        2019-10-01, Afforded size: 179
        2019-10-01, Final size: 179
        2019-10-03, BUY EXECUTED, Price: 470.35, Cost: 84192.65, Comm: 631.44, Size: 179.00
        2019-10-25, SELL CREATE, 592.15
        2019-10-27, SELL EXECUTED, Price: 592.15, Cost: 84192.65, Comm: 794.96, Size: -179.00
        2019-10-27, OPERATION PROFIT, GROSS: 21802.20, NET: 20375.79
        2020-01-31, BUY CREATE, 450.35
        2020-01-31, Cash: 105423.723
        2020-01-31, Price: 450.35
        2020-01-31, Buy prop size: 232
        2020-01-31, Afforded size: 232
        2020-01-31, Final size: 232
        2020-02-01, BUY EXECUTED, Price: 450.35, Cost: 104481.20, Comm: 783.61, Size: 232.00
        2020-02-01, SELL CREATE, 438.00
        2020-02-03, SELL EXECUTED, Price: 438.00, Cost: 104481.20, Comm: 762.12, Size: -232.00
        2020-02-03, OPERATION PROFIT, GROSS: -2865.20, NET: -4410.93
        2020-04-01, BUY CREATE, 194.90
        2020-04-01, Cash: 101012.794
        2020-04-01, Price: 194.9
        2020-04-01, Buy prop size: 513
        2020-04-01, Afforded size: 513
        2020-04-01, Final size: 513
        2020-04-03, BUY EXECUTED, Price: 194.90, Cost: 99983.70, Comm: 749.88, Size: 513.00
        2020-04-03, SELL CREATE, 167.25
        2020-04-07, SELL EXECUTED, Price: 167.25, Cost: 99983.70, Comm: 643.49, Size: -513.00
        2020-04-07, OPERATION PROFIT, GROSS: -14184.45, NET: -15577.82
        2020-04-08, BUY CREATE, 193.75
        2020-04-08, Cash: 85434.971875
        2020-04-08, Price: 193.75
        2020-04-08, Buy prop size: 437
        2020-04-08, Afforded size: 437
        2020-04-08, Final size: 437
        2020-04-09, BUY EXECUTED, Price: 193.75, Cost: 84668.75, Comm: 635.02, Size: 437.00
        2020-05-08, SELL CREATE, 239.85
        2020-05-11, SELL EXECUTED, Price: 239.85, Cost: 84668.75, Comm: 786.11, Size: -437.00
        2020-05-11, OPERATION PROFIT, GROSS: 20145.70, NET: 18724.58
        2020-05-13, BUY CREATE, 252.20
        2020-05-13, Cash: 104159.547875
        2020-05-13, Price: 252.2
        2020-05-13, Buy prop size: 409
        2020-05-13, Afforded size: 409
        2020-05-13, Final size: 409
        2020-05-14, BUY EXECUTED, Price: 252.20, Cost: 103149.80, Comm: 773.62, Size: 409.00
        2020-05-20, BUY CREATE, 222.10
        2020-05-20, Cash: 236.12437500001556
        2020-05-20, Price: 222.1
        2020-05-20, Buy prop size: 1
        2020-05-20, Afforded size: 1
        2020-05-20, Final size: 1
        2020-05-21, BUY EXECUTED, Price: 222.10, Cost: 222.10, Comm: 1.67, Size: 1.00
        2020-07-10, SELL CREATE, 370.10
        2020-07-13, SELL EXECUTED, Price: 370.10, Cost: 103371.90, Comm: 1138.06, Size: -410.00
        2020-07-13, OPERATION PROFIT, GROSS: 48369.10, NET: 46455.75
        2020-08-26, BUY CREATE, 298.05
        2020-08-26, Cash: 150615.30112500003
        2020-08-26, Price: 298.05
        2020-08-26, Buy prop size: 501
        2020-08-26, Afforded size: 501
        2020-08-26, Final size: 501
        2020-08-27, BUY EXECUTED, Price: 298.05, Cost: 149323.05, Comm: 1119.92, Size: 501.00
        Final Portfolio Value: 137220.87825000004
        Final PnL: 37220.88
        ==================================================
        Number of strat runs: 1
        Number of strats per run: 1
        Strat names: ['custom']
        **************************************************
        --------------------------------------------------
        {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'execution_type': 'close', 'channel': None, 'symbol': None, 'upper_limit': 1.5, 'lower_limit': -1.5, 'custom_column': 'custom'}
        OrderedDict([('rtot', 0.3164216915602497), ('ravg', 0.0007307660313169739), ('rnorm', 0.2021997935449528), ('rnorm100', 20.21997935449528)])
        OrderedDict([('sharperatio', 1.3576522240477626)])
        Time used (seconds): 0.1845560073852539
        Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'execution_type': 'close', 'channel': None, 'symbol': None, 'upper_limit': 1.5, 'lower_limit': -1.5, 'custom_column': 'custom'}
        Optimal metrics: {'rtot': 0.3164216915602497, 'ravg': 0.0007307660313169739, 'rnorm': 0.2021997935449528, 'rnorm100': 20.21997935449528, 'sharperatio': 1.3576522240477626, 'pnl': 37220.88, 'final_value': 137220.87825000004}
           init_cash   final_value       pnl
        0     100000  137220.87825  37220.88
        ```
        ![](./docs/assets/forecast-closing.png)
        ![](./docs/assets/forecast.png)
        
        -  Get live quotes for a security
        ```python
        nseta live-quote
        Please provide security/index code
        Usage:  [OPTIONS]
        
          Get live price quote of a security along with other (Optional) parameters
        
        Options:
          -S, --symbol TEXT  Security code
          --series TEXT      Default series - EQ (Equity) (Optional)
          -g, --general      Get the general (Name, ISIN) details also (Optional)
          -o, --ohlc         Get the OHLC values also (Optional)
          -w, --wk52         Get the 52 week high/low values also (Optional)
          -v, --volume       Get the traded volume details also (Optional)
          -b, --orderbook    Get the current bid/offer details also (Optional)
          -p, --plot         Plot the "Close" values (Optional)
          -r, --background   Keep running the process in the background (Optional)
          --help             Show this message and exit.
        ```
        -  Get live quotes with multiple options along with intraday history
        ```python
        nseta live-quote -S bandhanbnk -gowvb
        ------------------------------------------
                                                   
        Name                |  Bandhan Bank Limited
        ISIN                |          INE545U01014
        Last Updated        |  29-DEC-2020 16:00:00
        Prev Close          |                406.15
        Last Trade Price    |                413.50
        Change              |                  7.35
        % Change            |                  1.81
        Avg. Price          |                414.43
        Upper Band          |                437.95
        Lower Band          |                358.35
        Open                |                408.00
        High                |                419.25
        Low                 |                407.45
        Close               |                413.20
        52 Wk High          |                526.00
        52 Wk Low           |                152.20
        Quantity Traded     |             82,37,480
        Total Traded Volume |             82,37,480
        Total Traded Value  |             34,138.59
        Delivery Volume     |             17,43,202
        % Delivery          |                 21.16
        
        
                     Bid Price Offer Quantity Offer Price
        Bid Quantity                                     
        2,981           302.80            472      302.90
        200             302.70          1,739      302.95
        391             302.65         13,936      303.00
        4,368           302.60          3,471      303.05
        5,469           302.55            767      303.10
        ```
        - Scan live quotes of securities
        ```python
        nseta scan -S HDFC,ABB
                      Updated Symbol     Close       LTP
         30-DEC-2020 16:00:00   HDFC  2,518.95  2,521.70
         30-DEC-2020 16:00:00    ABB  1,203.05  1,205.30
        ```
        - Scan live quotes of a bunch of securities listed in a resource file(stocks.py)
        ```python
        nseta scan -l
                      Updated      Symbol     Close       LTP
         30-DEC-2020 16:00:00         ABB  1,203.05  1,205.30
         30-DEC-2020 16:00:00         ACC  1,616.75  1,651.00
         30-DEC-2020 16:00:00    ADANIENT    489.20    480.00
         30-DEC-2020 16:00:00  APOLLOTYRE    180.75    180.05
         30-DEC-2020 16:00:00      ASHOKA     90.85     90.05
         30-DEC-2020 16:00:00    ASHOKLEY     95.00     94.80
         30-DEC-2020 16:00:00   AMBUJACEM    244.40    252.50
         30-DEC-2020 16:00:00      ARVIND     45.60     46.85
         30-DEC-2020 16:00:00  ASIANPAINT  2,696.80  2,735.30
         30-DEC-2020 16:00:00        ATUL  6,389.55  6,500.00
         30-DEC-2020 16:00:00  AUROPHARMA    905.05    907.85
         30-DEC-2020 16:00:00    AXISBANK    630.20    623.60
         30-DEC-2020 16:00:00  BAJFINANCE  5,200.50  5,335.00
         30-DEC-2020 16:00:00  BANDHANBNK    413.20    406.50
         30-DEC-2020 16:00:00   BANKINDIA     49.40     49.15
         30-DEC-2020 16:00:00  BANKBARODA     62.35     62.05
         30-DEC-2020 16:00:00   BATAINDIA  1,584.40  1,581.70
         30-DEC-2020 16:00:00         BEL    114.75    114.70
         30-DEC-2020 16:00:00        BEML    993.90    964.10
         30-DEC-2020 16:00:00  BERGEPAINT    744.05    753.10
         30-DEC-2020 16:00:00  BHARATFORG    523.45    516.30
         30-DEC-2020 16:00:00        BHEL     35.40     35.30
         30-DEC-2020 16:00:00   BOMDYEING     74.85     76.85
         30-DEC-2020 16:00:00        BPCL    381.50    382.00
         30-DEC-2020 16:00:00   BRITANNIA  3,593.30  3,588.65
         30-DEC-2020 16:00:00    CADILAHC    479.35    479.80
         30-DEC-2020 16:00:00  CASTROLIND    121.95    123.55
         30-DEC-2020 16:00:00  CENTURYTEX    396.25    394.50
         30-DEC-2020 16:00:00  CHAMBLFERT    235.90    231.80
         30-DEC-2020 16:00:00       CIPLA    827.95    823.40
         30-DEC-2020 16:00:00    CROMPTON    369.15    370.15
         30-DEC-2020 16:00:00  CUMMINSIND    569.00    572.00
         30-DEC-2020 16:00:00       DABUR    528.95    538.60
         30-DEC-2020 16:00:00     DCBBANK    117.90    120.10
         30-DEC-2020 16:00:00         DLF    231.45    235.30
         30-DEC-2020 16:00:00     DRREDDY  5,165.60  5,165.00
         30-DEC-2020 16:00:00   EICHERMOT  2,460.55  2,521.00
         30-DEC-2020 16:00:00     ESCORTS  1,268.90  1,261.25
         30-DEC-2020 16:00:00    EVEREADY    204.95    208.70
         30-DEC-2020 16:00:00    EXIDEIND    191.35    192.50
         30-DEC-2020 16:00:00         FCL     66.05     63.75
         30-DEC-2020 16:00:00  FEDERALBNK     67.40     67.00
         30-DEC-2020 16:00:00      FORTIS    156.30    154.90
         30-DEC-2020 16:00:00         FSL    104.00     98.80
         30-DEC-2020 16:00:00        GAIL    123.75    122.90
         30-DEC-2020 16:00:00       GLAND  2,348.60  2,351.00
         30-DEC-2020 16:00:00    GLENMARK    497.90    496.50
         30-DEC-2020 16:00:00    GMRINFRA     26.40     27.30
         30-DEC-2020 16:00:00    GODREJCP    741.15    750.00
         30-DEC-2020 16:00:00   GODREJIND    431.10    423.90
         30-DEC-2020 16:00:00  GODREJPROP  1,405.05  1,390.20
         30-DEC-2020 16:00:00    GOODYEAR    969.80    958.20
         30-DEC-2020 16:00:00         HAL    836.30    830.00
         30-DEC-2020 16:00:00     HCLTECH    935.90    941.15
         30-DEC-2020 16:00:00        HDFC  2,518.95  2,521.70
         30-DEC-2020 16:00:00    HDFCBANK  1,427.20  1,432.05
        ```
        
        ## Signals and Scanners
        
        ### Scanners
        - When RSI(14) > 75
        - When RSI(14) < 25
        - When LTP > SMA(10)
        - When LTP < SMA(10)
        - When LTP > EMA(9)
        - When LTP < SMA(9)
        - When LTP < lower BBand
        - When LTP > higher BBand
        
        - Scanning options
        ```python
        nseta scan --help
        Usage: nseta scan [OPTIONS]
        
          Scan live and intraday for prices and signals.
        
        Options:
          -S, --stocks TEXT               Comma separated security codes(Optional.
                                          When skipped, all stocks configured in
                                          stocks.py will be scanned.)
          -l, --live                      Scans (every min.) the live-quote and lists
                                          those that meet the signal criteria. Works
                                          best with --background.
          -i, --intraday                  Scans (every 10 sec) the intraday price
                                          history and lists those that meet the signal
                                          criteria
          -s, --swing                     Scans (every 10 sec) the past 365 days price
                                          history and lists those that meet the signal
                                          criteria
          -t, --indicator [rsi|sma10|sma50|ema|macd|bbands|all]
                                          rsi, sma10, sma50, ema, macd, bbands, all.
                                          Choose one.
          -r, --background                Keep running the process in the background
                                          (Optional)
          --help                          Show this message and exit.
        ```
        
        For example:
        - Scanning based on Bollinger bands
        ```python
        nseta scan -i -t bbands
        INFO - tiscanner.py(scan_intraday - 123)
        This run of intraday scan took 10.7 sec
        
        INFO - livecli.py(scan_intraday_results - 150)
        Saved to: Scan_Results.csv
        
        Saved to: Scan_Results.csv
        INFO - livecli.py(scan_intraday_results - 155)
        
        We recommend taking the following BUY/SELL positions immediately for day trading. Intraday Signals:
                       Date      Symbol     BBands-U     BBands-L      LTP                   Signal
        2021-01-04 14:30:04      ASHOKA    93.434693    93.170307    93.40  (SELL) [LTP ~ BBands-U]
        2021-01-04 14:30:00   BANKINDIA    50.440129    50.294871    50.40  (SELL) [LTP ~ BBands-U]
        2021-01-04 14:30:01        BHEL    40.154087    39.935913    40.10  (SELL) [LTP ~ BBands-U]
        2021-01-04 14:30:00  JINDALSTEL   284.777021   282.052979   284.80  (SELL) [LTP > BBands-U]
        2021-01-04 14:30:00        NTPC    99.039443    98.860557    99.00  (SELL) [LTP ~ BBands-U]
        2021-01-04 14:30:00        SAIL    77.886024    77.103976    77.85  (SELL) [LTP ~ BBands-U]
        2021-01-04 14:30:00  ULTRACEMCO  5313.098214  5301.341786  5313.15  (SELL) [LTP > BBands-U]
        ```
        ### Signals
        - SELL : When RSI(14) > 75
        - BUY : When RSI(14) < 25
        - BUY : When LTP > SMA(10) and SMA(10) is upstrending
        - SELL : When LTP < SMA(10) and SMA(10) is downtrending
        - BUY : When LTP > EMA(9) and EMA(9) is upstrending
        - SELL : When LTP < EMA(9) and EMA(9) is downtrending
        - BUY : When LTP < lower BBand
        - SELL : When LTP > higher BBand
        
        ### Submit patches
        
        If you have fixed an issue or added a new feature, please fork this repository, make your changes and submit a pull request. [Here's good article on how to do this.](https://code.tutsplus.com/tutorials/how-to-collaborate-on-github--net-34267) 
        
        ## License
        [MIT License](https://github.com/pkjmesra/nseta/blob/main/LICENSE)
        
        ## Inspirations (Thank you so much!)
        -  [nsepy](https://github.com/swapniljariwala/nsepy)
        -  [fastquant](https://github.com/enzoampil/fastquant)
        -  [fbprophet](https://github.com/facebook/prophet)
        -  [nsetools](https://github.com/vsjha18/nsetools)
        -  [ta-lib](https://github.com/mrjbq7/ta-lib)
        -  [medium](https://github.com/CanerIrfanoglu/medium)
        
Keywords: NSE,Technical Indicators,Backtesting,Forecasting
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
