markets-insights 0.0.4

This package retrieves and handles financial market data from the National Stock Exchange (NSE) in India.

Markets Insights 📝

This package fetches and processes capital markets data from NSE (National Stock Exchange, India). Following data can be retrieved

1. Index (Nifty, Bank Nifty, NiftyIT)
2. Stocks
3. Derivatives (Futures and Options)

The package can perform technical functions on price of Index and Stocks. Following functions are supported.

1. Simple Moving Averages (SMA)
2. Relative Strength Index (RSI)
3. Stochastic RSI
4. Bollinger Bands (with standard deviations)

The calculation pipeline is quite extensible and more functions can be added externally.

Getting Started 🚀

Installation

!pip install markets_insights
import markets_insights

Get Index data for date range

from markets_insights.datareader import data_reader
import datetime

reader = data_reader.NseIndicesReader()

daterange_reader = data_reader.DateRangeDataReader(reader)

from_date = datetime.date(2023, 1, 1)
to_date = datetime.date.today() + datetime.timedelta(days=-1)
result = daterange_reader.read(from_date = from_date, to_date = to_date)

Calculation pipeline for RSI

Below example demonstrates calculating RSI using the calculation pipeline. The datepart calculation is pre-requisite for RSI calculation.

# import classes & setup options
import datetime
from markets_insights.datareader.data_reader import BhavCopyReader
from markets_insights.dataprocess.data_processor import HistoricalDataProcessor, MultiDataCalculationPipelines, CalculationPipelineBuilder, HistoricalDataProcessOptions
from markets_insights.calculations.base import DatePartsCalculationWorker

reader = BhavCopyReader()
options = HistoricalDataProcessOptions()
options.include_monthly_data = False
options.include_annual_data = False
histDataProcessor = HistoricalDataProcessor(options)

# Fetch the data
year_start = datetime.date(2023, 1, 1)
to_date = datetime.date(2023, 12, 31)
result = histDataProcessor.process(reader, {'from_date': year_start, 'to_date': to_date})

# Prepare calculation pipeline
pipelines = MultiDataCalculationPipelines()
pipelines.set_item('date_parts', CalculationPipelineBuilder.create_pipeline_for_worker(DatePartsCalculationWorker()))
pipelines.set_item('rsi', CalculationPipelineBuilder.create_rsi_calculation_pipeline())
histDataProcessor.set_calculation_pipelines(pipelines)

# Run the pipeline
histDataProcessor.run_calculation_pipelines()

A real use case: Understand the affect of RSI on price

In this use case, we understand the affect of RSI on the price of equity/stock.

Preparing the data

We perform below steps to prepare our analysis data

• Calculate RSI for each day for all the stocks.
• Add a flag for whenever the RSI crosses the control limits (eg: above 75 and below 30)
• Calculate the highest and lowest price change in the next 1, 3, 5, 7 & 10 trading sessions.
• Find the median for highest price change and lowest price change whenever the RSI crosses the control limits.

# prepare calculation pipeline
periods = [1, 3, 5, 7, 10]

pipelines = MultiDataCalculationPipelines()
pipelines.set_item('date_parts', CalculationPipelineBuilder.create_pipeline_for_worker(DatePartsCalculationWorker()))
pipelines.set_item('rsi', CalculationPipelineBuilder.create_rsi_calculation_pipeline(crossing_above_flag_value = 75, crossing_below_flag_value = 30, window = 14))
pipelines.set_item('foward_looking_fall', CalculationPipelineBuilder.create_forward_looking_price_fall_pipeline(periods))
pipelines.set_item('foward_looking_rise', CalculationPipelineBuilder.create_forward_looking_price_rise_pipeline(periods))
histDataProcessor.set_calculation_pipelines(pipelines=pipelines)

# run the pipeline and show results
histDataProcessor.run_calculation_pipelines()

daily_data = result.get_daily_data()

# Import constants so its easier to refer to column names
from markets_insights.core.column_definition import CalculatedColumns

# get names of fwd looking price column names. Since, these column names are auto-generated there no constants for them
fwd_looking_price_fall_cols, fwd_looking_price_rise_cols = [x for x in daily_data.columns if 'HighestPercFallInNext' in x], \
  [x for x in daily_data.columns if 'HighestPercRiseInNext' in x]

Show the median price change % for highest price fall whenever the RSI crosses above

daily_data[
(daily_data[CalculatedColumns.RsiCrossedAbove])
][fwd_looking_price_fall_cols].median()

Output

HighestPercFallInNext1Days     3.245288
HighestPercFallInNext3Days     4.623437
HighestPercFallInNext5Days     5.228839
HighestPercFallInNext7Days     5.719615
HighestPercFallInNext10Days    6.158358
dtype: float64

Show the median price change % for highest price rise whenever the RSI crosses below

daily_data[
(daily_data[CalculatedColumns.RsiCrossedAbove])
][fwd_looking_price_rise_cols].median()

Output

HighestPercRiseInNext1Days     0.985232
HighestPercRiseInNext3Days     1.550388
HighestPercRiseInNext5Days     2.071982
HighestPercRiseInNext7Days     2.640740
HighestPercRiseInNext10Days    3.314917
dtype: float64

Extending a new Data Reader

In this example we will create a new data reader to read data for Nasdaq listed equities. We will use yfinance python library for this.

Import classes

from markets_insights.datareader.data_reader import DateRangeDataReader
from markets_insights.core.core import Instrumentation
from markets_insights.core.column_definition import BaseColumns

import yfinance as yf
import pandas

Create reader class

We will create a class that extends the base reader. yfinance library can read data for a range. So, we will extend DateRangeDataReader class. With yfinance library, we have to specify which equity/tickers we want to download. For the sake of this example, we will download for top 7 companies of Nasdaq.

class NasdaqDataReader (DateRangeDataReader):
  def __init__(self, tickers: list = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'META', 'TSLA', 'NVDA']):
    super().__init__(reader=None)
    self.tickers = tickers
    self.name = "NasdaqDataReader"

  @Instrumentation.trace(name="NasdaqDataReader.read")
  def read(self, from_date, to_date):
    df_list = list()
    for ticker in self.tickers:
        data = yf.download(ticker, group_by="Ticker", start=from_date, end=to_date)
        data['ticker'] = ticker
        df_list.append(data)

    # combine all dataframes into a single dataframe
    df = pandas.concat(df_list)

    data = df.reset_index().rename(columns = self.get_column_name_mappings())
    data[BaseColumns.Date] = pandas.to_datetime(data[BaseColumns.Date])
    return data
  
  def get_column_name_mappings(self):
    return {
      'ticker': BaseColumns.Identifier,
      'OPEN': BaseColumns.Open,
      'HIGH': BaseColumns.High,
      'LOW': BaseColumns.Low,
      'CLOSE': BaseColumns.Close
    }

Notice here we are renaming the columns to standard column names so that the calculation pipeline can read them properly.

Running the calculation pipeline

The calculation pipeline will not be different except we will pass NasdaqDataReader instance.

# import classes & setup options
from markets_insights.dataprocess.data_processor import HistoricalDataProcessor, MultiDataCalculationPipelines, CalculationPipelineBuilder, HistoricalDataProcessOptions
from markets_insights.calculations.base import DatePartsCalculationWorker

reader = NasdaqDataReader()
options = HistoricalDataProcessOptions()
options.include_monthly_data = False
options.include_annual_data = False
histDataProcessor = HistoricalDataProcessor(options)

# Fetch the data
year_start = datetime.date(2023, 1, 1)
to_date = datetime.date(2023, 12, 31)
result = histDataProcessor.process(reader, {'from_date': year_start, 'to_date': to_date})

# Prepare calculation pipeline
pipelines = MultiDataCalculationPipelines()
pipelines.set_item('date_parts', CalculationPipelineBuilder.create_pipeline_for_worker(DatePartsCalculationWorker()))
pipelines.set_item('rsi', CalculationPipelineBuilder.create_rsi_calculation_pipeline())
histDataProcessor.set_calculation_pipelines(pipelines)

# Run the pipeline
histDataProcessor.run_calculation_pipelines()

Here's the code to display results

from markets_insights.core.column_definition import CalculatedColumns

result.get_daily_data() \
  .sort_values(
    [BaseColumns.Date, BaseColumns.Identifier]
  )[
    [BaseColumns.Identifier, BaseColumns.Date, BaseColumns.Close, 
     CalculatedColumns.RelativeStrengthIndex]
  ] \
  .tail(5)

Output

	Identifier	Date	Close	Rsi
248	AAPL	2023-12-28 00:00:00	193.58	54.4815
497	AMZN	2023-12-28 00:00:00	153.38	63.9387
746	GOOGL	2023-12-28 00:00:00	140.23	61.585
995	META	2023-12-28 00:00:00	358.32	70.2377
1244	MSFT	2023-12-28 00:00:00	375.28	56.909
1493	NVDA	2023-12-28 00:00:00	495.22	58.305
1742	TSLA	2023-12-28 00:00:00	253.18	55.9788