markets-insights 0.4.10
This package retrieves and handles financial market data from the National Stock Exchange (NSE) in India.
Requires Python: >=3.8


# 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)

*Support for additional markets and instruments can be added externally*

The package can perform technical functions on price of Index and Stocks. Following indicators 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 idicators can be added externally.

## Getting Started 🚀
### Installation
```python
!pip install markets_insights

from markets_insights.core.environment import Environment
Environment.setup(cache_data_base_path = './cache-data')
```

### Get Index data for date range
```python
from markets_insights.datareader import data_reader
import datetime

reader = data_reader.NseIndicesReader()

from_date = datetime.date(2023, 1, 1)
to_date = datetime.date(2023, 12, 31)
result = reader.read(data_reader.DateRangeCriteria(from_date, to_date))
result.head(3)
```

*Output*

|    | Identifier    | Index Date   |     Open |    High |     Low |    Close |   Points Change |   Change(%) |      Volume |    Turnover |   P/E |   P/B |   Div Yield | Date                |
|---:|:--------------|:-------------|---------:|--------:|--------:|---------:|----------------:|------------:|------------:|------------:|------:|------:|------------:|:--------------------|
|  0 | Nifty 50      | 02-01-2023   | 18131.7  | 18215.2 | 18086.5 | 18197.5  |           92.15 |        0.51 | 2.56074e+08 | 1.17931e+11 | 21.9  |  4.27 |        1.26 | 2023-01-02 00:00:00 |
|  1 | Nifty Next 50 | 02-01-2023   | 42321.1  | 42409.8 | 42117.7 | 42248.4  |           60.75 |        0.14 | 1.34929e+08 | 4.11122e+10 | 25.67 |  4.79 |        1.76 | 2023-01-02 00:00:00 |
|  2 | Nifty 100     | 02-01-2023   | 18290.2  | 18350.9 | 18237.5 | 18334.3  |           75.55 |        0.41 | 3.91003e+08 | 1.59043e+11 | 22.75 |  4.41 |        1.3  | 2023-01-02 00:00:00 |

### Get daily, monthly and annually aggregrated data
In this example we will use HistoricalDataProcessor class to get data between a date range. HistoricalDataProcessor will also do monthly and annual aggregation of data.

```python
# import classes & setup
# import classes & setup
from markets_insights.dataprocess.data_processor import HistoricalDataProcessor
from markets_insights.datareader.data_reader import NseIndicesReader, DateRangeCriteria
histDataProcessor = HistoricalDataProcessor()

# Fetch and process the data
year_start = datetime.date(2023, 12, 1)
year_end = datetime.date(2023, 12, 31)
result = histDataProcessor.process(NseIndicesReader(), DateRangeCriteria(year_start, year_end))
```

#### Displaying the output
We will call `get_monthly_data()` method to get monthly aggregated data. We can call `get_daily_data()` for *Daily* and `get_annual_data()` for *Annual* data.

```python
from markets_insights.core.column_definition import BaseColumns

result.get_monthly_data().sort_values(BaseColumns.Date).head(3)
```

*Output*
|      | Identifier                                  | Date                | Month   |      Volume |    Turnover |    Close |     High |        Low |       Open |
|-----:|:--------------------------------------------|:--------------------|:--------|------------:|------------:|---------:|---------:|-----------:|-----------:|
|    0 | INDIA VIX                                   | 2023-01-31 00:00:00 | 2023-01 | 0           | 0           |    16.88 |    19.39 |    11.6425 |    14.8675 |
| 1012 | NIFTY100 ENHANCED ESG                       | 2023-01-31 00:00:00 | 2023-01 | 8.12952e+09 | 4.99243e+12 |  3352.6  |  3490.75 |  3352.6    |  3472.29   |
|  228 | NIFTY ALPHA QUALITY VALUE LOW-VOLATILITY 30 | 2023-01-31 00:00:00 | 2023-01 | 2.32482e+09 | 1.3303e+12  | 11286.1  | 11599.4  | 11214.5    | 11456.8    |


### Calculating RSI using CalculationPipeline
Below example demonstrates calculating RSI using the calculation pipeline.

```python
# import classes & setup options
import datetime
from markets_insights.datareader.data_reader import BhavCopyReader, DateRangeCriteria
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, DateRangeCriteria(year_start, to_date))

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

# Run the pipeline
histDataProcessor.run_calculation_pipelines()
```

#### Displaying the output
For displaying the relevant columns in the output we will use column name constants from *BaseColumns* & *CalculatedColumns* classes.
```python
from markets_insights.core.column_definition import BaseColumns, CalculatedColumns

result.get_daily_data().sort_values(BaseColumns.Date).tail(3)[[BaseColumns.Identifier, BaseColumns.Date, BaseColumns.Close, CalculatedColumns.RelativeStrengthIndex]]
```

*Output*
|        | Identifier   | Date                |   Close |     Rsi |
|-------:|:-------------|:--------------------|--------:|--------:|
| 336063 | RKFORGE      | 2023-12-29 00:00:00 |  725.7  | 46.9257 |
| 329710 | RBL          | 2023-12-29 00:00:00 |  852.95 | 54.8479 |
| 446931 | ZYDUSWELL    | 2023-12-29 00:00:00 | 1681.1  | 72.0492 |

### 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.

```python
# import classes
import datetime
from markets_insights.datareader import data_reader
from markets_insights.dataprocess import data_processor

# Fetch the data
reader = data_reader.BhavCopyReader()
options = data_processor.HistoricalDataProcessOptions(include_monthly_data = False, include_annual_data=False)
histDataProcessor = data_processor.HistoricalDataProcessor(options)

from_date = datetime.date(2023, 12, 1)
to_date = datetime.date(2023, 12, 31)
result = histDataProcessor.process(data_reader.BhavCopyReader(), data_reader.DateRangeCriteria(from_date, to_date))
```

```python
# prepare calculation pipeline
periods = [1, 7, 15, 30, 45]

pipelines = data_processor.MultiDataCalculationPipelines()
pipelines.set_item('rsi', data_processor.CalculationPipelineBuilder.create_rsi_calculation_pipeline(crossing_above_flag_value = 75, crossing_below_flag_value = 30, window = 14))
pipelines.set_item('foward_looking_fall', data_processor.CalculationPipelineBuilder.create_forward_looking_price_fall_pipeline(periods))
pipelines.set_item('foward_looking_rise', data_processor.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 BaseColumns, 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 'Trough' in x], \
    [x for x in daily_data.columns if 'Peak' in x]
```

#### Show the median price change % for highest price fall whenever the RSI crosses above
```python
daily_data[
(daily_data[CalculatedColumns.RsiCrossedAbove])
][fwd_looking_price_fall_cols].median()
```
*Output*
```bat
TroughPercInNext1Sessions     1.417302
TroughPercInNext7Sessions     3.770465
TroughPercInNext15Sessions    4.783065
TroughPercInNext30Sessions    6.070147
TroughPercInNext45Sessions    6.783886
dtype: float64
```

#### Show the median price change % for highest price rise whenever the RSI crosses below
```python
daily_data[
(daily_data[CalculatedColumns.RsiCrossedBelow])
][fwd_looking_price_rise_cols].median()
```
*Output*
```bat
PeakPercInNext1Sessions      3.876428
PeakPercInNext7Sessions      7.600800
PeakPercInNext15Sessions     9.780681
PeakPercInNext30Sessions    13.269178
PeakPercInNext45Sessions    16.275638
dtype: float64
```

### Performing Arithmetic on Readers
```python
from markets_insights.datareader import data_reader
from markets_insights.core.core import IdentifierFilter

date_criteria = data_reader.DateRangeCriteria(datetime.date(2023, 1, 1), datetime.date(2023, 12, 31))

indices_reader = data_reader.NseIndicesReader()
vix_reader = data_reader.NseIndicesReader().set_filter(IdentifierFilter("India VIX"))
op_reader = indices_reader / vix_reader
data = op_reader.read(date_criteria).query(str(IdentifierFilter("Nifty 50 / India VIX")))
```

*Output*


### Extending the Framework: Creating a DataReader
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
```python
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.

```python
class NasdaqDataReader (DateRangeSourceDataReader):
  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, criteria: DateRangeCriteria):
    df_list = list()
    for ticker in self.tickers:
        data = yf.download(ticker, group_by="Ticker", start=criteria.from_date, end=criteria.to_date)
        data['ticker'] = ticker
        df_list.append(data)

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

    final_data = df.reset_index().rename(columns = self.get_column_name_mappings())
    final_data[BaseColumns.Date] = pandas.to_datetime(final_data[BaseColumns.Date])
    return final_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.

```python
# import classes & setup options
import datetime
from markets_insights.dataprocess.data_processor import HistoricalDataProcessor, MultiDataCalculationPipelines, CalculationPipelineBuilder, HistoricalDataProcessOptions, DateRangeCriteria
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, DateRangeCriteria(year_start, 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
```python
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 |

### Extending the Framework: Creating a CalculationWorker
In this example, we will create a CalculationWorker to calcualte the Fibonacci Retracement level for any equity or index. Finbonacci Retracement levels are based on a time window and a level (26.3%, 50% etc). So, these will become input to our CalculationWorker. Lets call this worker as **FibnocciRetracementCalculationWorker**

#### Implement the worker class. The important aspect here is to override the `add_calculated_columns()` method
```python
## import modules
from markets_insights.calculations.base import CalculationWorker
from markets_insights.core.core import Instrumentation
from markets_insights.calculations.base import BaseColumns
import pandas

class FibonacciRetracementCalculationWorker (CalculationWorker):
  def __init__(self, time_window: int, level_perct: float):
    self._time_window = time_window
    self._level = level_perct / 100
    self._column_name = 'Fbr' + str(level_perct)

  @Instrumentation.trace(name="FibnocciRetracementCalculationWorker")
  def add_calculated_columns(self, data: pandas.DataFrame):
    identifier_grouped_data: pandas.DataFrame = data.groupby(BaseColumns.Identifier)
    #Since, our dataframe may contain data for multiple symbols, we need to first group them by Identifier
    data[self._column_name] = identifier_grouped_data[BaseColumns.Close].transform(
        lambda x: 
          x.rolling(self._time_window).max() - 
          (
            (x.rolling(self._time_window).max() - x.rolling(self._time_window).min())  * self._level
          )
      )
```
#### Create pipline with the FibnocciRetracementCalculationWorker and run
Now, that our worker is created let us use it in a calculation pipeline. We can use it with any instrument (index, stock) that are supported. Event for the Nasdaq instruments that were supported in earlier examples. For this example, let us take NSE Indexes.
```python
# Create pipline with the FibnocciRetracementCalculationWorker and run 
from markets_insights.datareader.data_reader import NseIndicesReader, DateRangeCriteria
from markets_insights.dataprocess.data_processor import HistoricalDataProcessor, HistoricalDataProcessOptions, \
  MultiDataCalculationPipelines, CalculationPipeline
histDataProcessor = HistoricalDataProcessor(HistoricalDataProcessOptions(include_monthly_data=False, include_annual_data=False))

# Fetch the data
result = histDataProcessor.process(NseIndicesReader(), DateRangeCriteria(datetime.date(2023, 12, 1), datetime.date(2023, 12, 31)))

# Prepare calculation pipeline
fbr50_worker = FibonacciRetracementCalculationWorker(time_window=7, level_perct=50)
pipelines = MultiDataCalculationPipelines()
histDataProcessor.set_calculation_pipelines(
  CalculationPipeline(
    workers = [fbr50_worker]
  )
)

# Run the pipeline and get data
histDataProcessor.run_calculation_pipelines()
```

##### Display the results. 
Since our time window was 15 days. So, the calculation result for first 14 days will not be available. We will look at the last 10 records with `tail(5)`
```python
result.get_daily_data()[[
  BaseColumns.Identifier, BaseColumns.Date, BaseColumns.Close, fbr50_worker._column_name
]].tail(5)
```

*Output*
|      | Identifier                    | Date                |    Close |    Fbr50 |
|-----:|:------------------------------|:--------------------|---------:|---------:|
| 2141 | NIFTY COMPOSITE G-SEC INDEX   | 2023-12-29 00:00:00 |  2602.3  |  2599.48 |
| 2142 | NIFTY 10 YR BENCHMARK G-SEC   | 2023-12-29 00:00:00 |  2232.79 |  2230.24 |
| 2143 | NIFTY MIDCAP SELECT           | 2023-12-29 00:00:00 | 10397.5  | 10209.1  |
| 2144 | NIFTY ALPHA LOW-VOLATILITY 30 | 2023-12-29 00:00:00 | 23373.2  | 22886    |
| 2145 | NIFTY50 USD                   | 2023-12-29 00:00:00 |  9048.9  |  8941.77 |