Welcome to plutoPy’s documentation!¶

model¶

Fred¶

St. Louis Fed FRED cache

sources: https://fred.stlouisfed.org/

class plutoPy.model.Fred.Meta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs

Query the FRED meta-data cache

FREQUENCY¶: ‘D’ for day, ‘M’ for month, ‘Q’ for quarter, etc.

NAME¶: title

SEASON_ADJUST¶: ‘SA’ for seasonally adjusted, etc.

SERIES_ID¶: use this to query the time-series

TICKER¶: FRED id

UNITS¶: ‘Miles’, ‘Million of Dollars’, etc.

class plutoPy.model.Fred.TimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs

Query the FRED time-series cache

SERIES_ID¶

TIME_STAMP¶

VAL¶

World Bank GEM¶

World Bank Global Economic Monitor data

sources: https://worldbank.org

class plutoPy.model.WorldBank.Meta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs

Query to find the country and indicator id’s of the Global Economic Monitor data published by the World Bank

COUNTRY_ID¶

COUNTRY_KEY¶: COUNTRY_KEY and INDICATOR_KEY uniquely identify a time-series

COUNTRY_NAME¶

END_YEAR¶: ending year from which time-series is available

INDICATOR_ID¶

INDICATOR_KEY¶: COUNTRY_KEY and INDICATOR_KEY uniquely identify a time-series

INDICATOR_NAME¶

START_YEAR¶: starting year from which time-series is available

class plutoPy.model.WorldBank.TimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs

Query the Global Economic Monitor data published by the World Bank

COUNTRY_KEY¶: COUNTRY_KEY and INDICATOR_KEY uniquely identifies the time-series

INDICATOR_KEY¶: COUNTRY_KEY and INDICATOR_KEY uniquely identifies the time-series

VALUE¶

YEAR¶

InternationalMonetaryFund¶

International Monetary Fund data

sources: https://www.imf.org

class plutoPy.model.InternationalMonetaryFund.Meta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs

Query IMF meta-data based on geography/indicator. Then use the ID obtained here to query the time-series

AREA¶: India, Great Britain, United States

AREA_CODE¶: IN, GB, US…

DATA_DESCRIPTION¶: description of the data: ‘Primary Commodity Prices, Copper’, ‘Economic Activity, Industrial Production, Index’

DATA_KEY¶: IMF internal key: PCOPP, AIP_IX

END_YEAR¶: the year till which TimeSeries data is available

FREQ¶: A for Annual; Q for Quarterly; M for Monthly

ID¶: use to query TimeSeries

START_YEAR¶: the year from which TimeSeries data is available

UNIT_MEASURE¶: IX for Index; USD for Dollar

UNIT_MULT¶: 0, 3, 6 to multiply by none, thousands and millions

class plutoPy.model.InternationalMonetaryFund.TimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs

Query the IMF time-series data based on the Meta ID

AUTO_ID¶

ID¶: get this from Meta

MONTH¶

VALUE¶

YEAR¶

NASDAQOMX Indices¶

NASDAQOMX Indices

sources: https://indexes.nasdaqomx.com/ https://www.quandl.com/

class plutoPy.model.NasdaqOmx.Meta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query to find the time-series id of total-return indices published by the NasdaqOmx

CODE¶: NasdaqOmx index code

ID¶: time-series id to be used to query NasdaqOmxTimeSeries

NAME¶: name of the index

class plutoPy.model.NasdaqOmx.TimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the index time-series published by the NasdaqOmx

CLOSE¶

ID¶: time-series id

TIME_STAMP¶

Yield Curves¶

Yield curves

sources: https://www.ccilindia.com http://treasury.gov https://www.ecb.europa.eu

class plutoPy.model.YieldCurve.EuroArea(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query Euro area yield curves published by the ECB

CURVE_ID¶: G_N_A for AAA rated only. G_N_C for all (including AAA) rated.

TENOR_M¶: month

TENOR_Y¶: year

TIME_STAMP¶

VALUE¶: yield

class plutoPy.model.YieldCurve.IndiaZeroCoupon(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the Indian Zero Coupon Yield Curve published by CCIL

MATURITY¶

TIME_STAMP¶

YIELD¶

class plutoPy.model.YieldCurve.UsTreasury(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the Daily Treasury Yield Curve Rates published by US Treasury

M1¶: ‘M’ implies month

M3¶

M6¶

TIME_STAMP¶

Y1¶: ‘Y’ implies year

Y10¶

Y2¶

Y20¶

Y3¶

Y30¶

Y5¶

Y7¶

Indices¶

Indices

sources:: https://nseindia.com/ https://bseindia.com/ https://www.ccilindia.com https://finance.yahoo.com/ https://indices.barclays

class plutoPy.model.Indices.BarclaysMeta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Meta information about the BarclaysTimeSeries by TICKER

CURRENCY¶

DATE_BASE¶: Date from when the index is calculated

DATE_LIVE¶: Date when the index went live

FAMILY¶

NAME¶

RETURN_TYPE¶: Excess/Price/Total Return

TICKER¶

class plutoPy.model.Indices.BarclaysTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the index time-series published by Barclays

CLOSE¶

TICKER¶

TIME_STAMP¶

class plutoPy.model.Indices.BseConstituents(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the latest constituents of BSE indices

CODE¶: BSE security code

NAME¶: name of the index. Use this to query the constituents

SECURITY_NAME¶: name of the security

SYMBOL¶: NSE symbol, if the security is listed in the NSE

TIME_STAMP¶: date when the constituents of the index was updated

class plutoPy.model.Indices.BseTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the index time-series published by the BSE

CLOSE¶

HIGH¶

LOW¶

NAME¶

OPEN¶

TIME_STAMP¶

class plutoPy.model.Indices.IndiaGsecTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the Indian Government Soverign Bond index time-series published by the CCIL

COUPON¶: wavg coupon

DURATION¶: wavg duration

NAME¶: tenor bucket. possible values: 0_5, 5_10, 10_15, 15_20, 20_30

PRI¶: Principal Return Index. based on clean price

TIME_STAMP¶

TRI¶: Total Return Index. the absolute return that the tenor bucket offers. includes coupon accrued and capital gains (losses)

YTM¶: wavg yield-to-maturity

class plutoPy.model.Indices.IndiaVixTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the India VIX time-series data published by the NSE

CLOSE¶

HIGH¶

LOW¶

OPEN¶

TIME_STAMP¶

class plutoPy.model.Indices.NseConstituents(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the latest constituents of NSE indices

CAP_WEIGHT¶

INDUSTRY¶

NAME¶: name of the index. Use this to query the constituents

SYMBOL¶

TIME_STAMP¶: date when the constituents of the index was updated

class plutoPy.model.Indices.NseTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the index time-series published by the NSE

CLOSE¶

HIGH¶

LOW¶

NAME¶: name of the index. Use this to query the time-series

OPEN¶

TIME_STAMP¶

VOLUME¶

class plutoPy.model.Indices.YahooFinanceTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the index time-series published by Yahoo Finance

CLOSE¶

CLOSE_ADJ¶

HIGH¶

LOW¶

NAME¶: name of the index. Use this to query the time-series

OPEN¶

TIME_STAMP¶

VOLUME¶

US ETFs¶

ETFs listed in the US

class plutoPy.model.ETFsUs.Meta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the meta-data

ASSET_CLASS¶

AUM¶

EXPENSE_RATIO¶

ISSUER¶

LAUNCH_DATE¶

NAME¶

SEGMENT¶

SYMBOL¶

UNDERLYING_INDEX¶

MutualFundsIndia¶

Mutual Funds India

sources: https://www.amfiindia.com/

class plutoPy.model.MutualFundsIndia.AumFundwise(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the fund-wise assets under management (AUM) of different asset managers

AUTO_ID¶

AVG_AUM_FOFD¶: AUM held by Fund-of-Funds

AVG_AUM_WO_FOFD¶: excluding AUM held by Fund-of-Funds

FUND¶: name of the manager

PERIOD¶

class plutoPy.model.MutualFundsIndia.AumSchemewise(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the scheme-wise assets under management (AUM) of different asset managers and their ‘schemes’

AUTO_ID¶

AVG_AUM_FOFD¶: AUM held by Fund-of-Funds

AVG_AUM_WO_FOFD¶: excluding AUM held by Fund-of-Funds

PERIOD¶

SCHEME_CODE¶: code given by AMFI

SCHEME_NAME¶: name of the fund - can change multiple times over its life

class plutoPy.model.MutualFundsIndia.Meta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the meta-data

AS_OF¶: data capture date

BENCH_NOW¶: benchmark being used currently

BENCH_ORIG¶: original benchmark

CATEGORY¶: Mid-Cap, Index Funds, etc…

EXPENSE¶: Based on prospectus (%)

EXPENSE_RATIO¶: Based on what the fund actually charged (%)

SCHEME_CODE¶: code given by AMFI

TURNOVER_RATIO¶: churn

class plutoPy.model.MutualFundsIndia.NavTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query for the NAVs of different funds

NAV¶: before exit loads, STT, etc…

SCHEME_CODE¶: code given by AMFI

SCHEME_NAME¶: name of the fund - can change multiple times over its life

TIME_STAMP¶: mostly daily but some funds declare only twice a week. bond market holidays are different from equity market holidays.

class plutoPy.model.MutualFundsIndia.Portfolio(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query for the monthly reported portfolios of different funds

AUTO_ID¶

INDUSTRY_BSE¶: BSE’s SECTOR classification.

INSTRUMENT¶: EQUITY, BOND, OTHER… broad classification

INSTRUMENT_TYPE¶: E, BT, CR… clarifies INSTRUMENT

NAME¶: name of the position

PORTFOLIO_DATE¶: mostly declared once a month. available after the 10th of each month.

SCHEME_CODE¶: code given by AMFI

SECTOR¶: BASIC MATERIALS, FINANCIAL SERVICES, CONSUMER DEFENSIVE, etc…

SYMBOL¶: where available, the NSE ticker/symbol of the position

WEIGHTAGE¶: weightage of the position in the portfolio (%)

US Equities¶

Equities US

sources: https://iexcloud.io https://www.sec.gov/

class plutoPy.model.EquitiesUs.EodAdjustedTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query end-of-day price and volume time-series adjusted for splits, bonus and dividends for listed stocks

CLOSE¶

HIGH¶

LOW¶

OPEN¶

SYMBOL¶

TIME_STAMP¶

VOLUME¶

class plutoPy.model.EquitiesUs.SecFilings(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Links to SEC filings. Useful to keep track of M&A events in your portfolio

ACC_NO¶

FILING_DATE¶

FILING_TYPE¶: SC14D9C => acquisitions; DEFM14A => mergers

FILING_URL¶

FORM_NAME¶

SYMBOL¶

class plutoPy.model.EquitiesUs.SecMeta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Meta information of companies in the SEC EDGAR database. Useful for SIC.

CIK¶: Central Index Key

NAME¶

SIC¶: Standard Industrial Classification code

SIC_DESC¶: Standard Industrial Classification

SYMBOL¶

class plutoPy.model.EquitiesUs.Tickers(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query meta data of listed equity (common stock) tickers

EXCHANGE¶: listed exchange

MKT_CAP¶: market cap in USD

NAME¶: name of the company

SYMBOL¶: ticker/stock symbol. for eg: AAPL

EquitiesIndiaNse¶

Equities India NSE

sources: https://nseindia.com/

class plutoPy.model.EquitiesIndiaNse.CorporateActions(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the corporate actions for listed stocks

EX_DATE¶

PURPOSE¶

SERIES¶

SYMBOL¶

WHEN_UPDATED¶

class plutoPy.model.EquitiesIndiaNse.CorporateEvents(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the corporate events for listed stocks

AUTO_ID¶

DISC_DATE¶: when was the event disclosed?

EVENT_DATE¶: when was the event held?

PURPOSE¶

SYMBOL¶

class plutoPy.model.EquitiesIndiaNse.CorporateResults(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query to CorporateResults for a specific period and stock

AUTO_ID¶

H1¶: section 1

H2¶: section 2

H3¶: section 3

H4¶: section 4

KEY¶

REF_ID¶: lookup CorporateResultsMeta to get this value

VALUE¶

class plutoPy.model.EquitiesIndiaNse.CorporateResultsMeta(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query to obtain the REF_ID to lookup CorporateResults for a specific period

BROADCAST_DATE¶

IS_AUDITED¶

IS_CONSOLIDATED¶

IS_CUMULATIVE¶

PERIOD¶

PERIOD_BEGIN¶

PERIOD_END¶

REF_ID¶: use this to query CorporateResults

SYMBOL¶

class plutoPy.model.EquitiesIndiaNse.DailyReturns(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the percentage daily return (close-to-close) time-series for listed stocks

SYMBOL¶

TIME_STAMP¶

VALUE¶

class plutoPy.model.EquitiesIndiaNse.EodAdjustedTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizDyn

Query end-of-day price and volume time-series adjusted for splits, bonus and dividends for listed stocks

CLOSE¶

HIGH¶

LOW¶

OPEN¶

SYMBOL¶

TIME_STAMP¶

VOLUME¶

class plutoPy.model.EquitiesIndiaNse.EodTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query unadjusted end-of-day price and volume time-series for listed stocks

CLOSE¶: usually a weighted average of price x volume of the last 15-minutes of trading

HIGH¶

LAST¶: last traded price

LOW¶

OPEN¶

SERIES¶

SYMBOL¶

TIME_STAMP¶

VOLUME¶

class plutoPy.model.EquitiesIndiaNse.MarketCapDecile(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query market-cap decile of of listed equity tickers

DECILE¶

SYMBOL¶

TIME_STAMP¶

class plutoPy.model.EquitiesIndiaNse.MiscInfo(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query miscellaneous information of listed equity tickers

D2T_PCT¶: delivery percentage. % of shares which were not squared off the same day out of total shares traded

FF_MKT_CAP_CR¶: free-float market-cap in crores

LOWER_PX_BAND¶: trading is halted if the stock trades below this level

SYMBOL¶

TIME_STAMP¶

UPPER_PX_BAND¶: trading is halted if the stock trades above this level

class plutoPy.model.EquitiesIndiaNse.Tickers(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query meta data of listed equity tickers

DATE_LISTING¶: date of birth of the ticker

FACE¶: a stock’s face value is the initial cost of the stock, as indicated on the certificate

ISIN¶: unique identifier

MARKET_LOT¶: usually, you can trade just 1 stock. but sometimes, only in multiples of “market lot”

NAME¶: name of the security. for eg: Infosys Limited

PAID_UP¶: paid-up capital is the amount of money a company has received from shareholders in exchange for shares of stock

SERIES¶: EQ/BE/BZ as assigned by the NSE

SYMBOL¶: ticker/stock symbol. for eg: INFY

Equities Futures and Options traded on the NSE¶

Equities Futures and Options traded on the NSE

sources: https://www.nseindia.com/

class plutoPy.model.EquitiesFuturesAndOptionsIndiaNse.FuturesEodTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the end-of-day prices of equity futures contracts traded on the NSE

CLOSE¶: usually a weighted average of price x volume of the last 15-minutes of trading

CONTRACTS¶

EXPIRY¶

HIGH¶

LOW¶

OI¶

OPEN¶

SETTLE¶

SYMBOL¶

TIME_STAMP¶

VALUE¶

class plutoPy.model.EquitiesFuturesAndOptionsIndiaNse.LotSize(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the lot-size of equity futures and options contracts traded on the NSE

CONTRACT¶: expiry year and month. the ‘date’ part is always set to 1.

LOT_SIZE¶

SYMBOL¶

class plutoPy.model.EquitiesFuturesAndOptionsIndiaNse.OptionGreeks(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the end-of-day option greeks of equity options contracts traded on the NSE

DELTA¶

EXPIRY¶

GAMMA¶

IV¶

LAMBDA¶

MODEL_PRICE¶

OTYPE¶: CE - European calls; PE - European puts

RATE¶: the interest rate used in the calculation

RHO¶

SIGMA¶: the volatility assumption used in the calculation

STRIKE¶

SYMBOL¶

THETA¶

TIME_STAMP¶

TTM¶: time to maturity measured in years

VEGA¶

class plutoPy.model.EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the end-of-day prices of equity options contracts traded on the NSE

CLOSE¶: usually a weighted average of price x volume of the last 15-minutes of trading

CONTRACTS¶

EXPIRY¶

HIGH¶

LOW¶

OI¶

OPEN¶

OTYPE¶: CE - European calls; CA - American calls, etc…

SETTLE¶

STRIKE¶

SYMBOL¶

TIME_STAMP¶

VALUE¶

Commodity Futures And Options¶

Commodity Futures and Options

sources: https://www.mcxindia.com/ https://www.ncdex.com/ https://www.cmegroup.com/

class plutoPy.model.CommodityFuturesAndOptions.CmeEod(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the end-of-day prices of commodity futures contracts traded on the COMEX and NYMEX

CONTRACT¶

CONTRACT_DAY¶

CONTRACT_MONTH¶

CONTRACT_YEAR¶

HIGH¶

LAST¶

LOW¶

OPEN¶

PRODUCT_DESCRIPTION¶

PRODUCT_SYMBOL¶

SETTLE¶

TIME_STAMP¶

VOLUME¶

class plutoPy.model.CommodityFuturesAndOptions.McxEod(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the end-of-day prices of commodity contracts traded on the MCX

CLOSE¶

CONTRACT¶

EXPIRY¶

EXPIRY_SERIES¶: 0 implies current month contract, etc.

HIGH¶

LOW¶

OI¶: open interest

OPEN¶

OTYPE¶: CE - European calls; PE - European puts; XX , FUTCOM - futures

STRIKE¶: if OTYPE is CE or PE, then the strike

TIME_STAMP¶

VALUE¶: value of the contracts traded

VOL¶: quantity of the underlying commodity traded in VOL_UNITS

VOLUME¶: number of contracts traded

VOL_UNITS¶: BALES, KGS, BBL, etc…

class plutoPy.model.CommodityFuturesAndOptions.NcdexEod(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the end-of-day prices of commodity contracts traded on the NCDEX

CLOSE¶

COMMODITY¶: Crude palm oil, TURMERIC, etc…

CONTRACT¶

DELIVERY_CENTRE¶: Ahmedabad, Kandla, etc…

EXPIRY¶

EXPIRY_SERIES¶: 0 implies current month contract, etc.

HIGH¶

LAST_TRADE¶: day on which the last trade occurred

LOW¶

MEASURE¶: for VOLUME. MT, LOT, etc

OI¶: open interest

OPEN¶

PRICE_UNIT¶: RS/QUINTAL, RS/10KGS, RS/BALES, etc…

TIME_STAMP¶

TRADED_NUM¶: number of trades

TRADED_QTY¶: number of contracts traded

TRADED_VAL¶: traded value in Rs. lakhs

Currencies¶

Currencies

sources: https://www.alphavantage.co https://www.nseindia.com/

class plutoPy.model.Currencies.Av30minTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizBeka

Query the 30-min bar of currency USD-fx time-series published by AlphaVantage

CLOSE¶

HIGH¶

LOW¶

OPEN¶

SYMBOL¶

TIME_STAMP¶: in UTC

class plutoPy.model.Currencies.AvEodTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizBeka

Query the end-of-day currency USD-fx time-series published by AlphaVantage

CLOSE¶

HIGH¶

LOW¶

OPEN¶

SYMBOL¶

TIME_STAMP¶: in UTC

class plutoPy.model.Currencies.NseFuturesTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the currency futures time-series published by the NSE

CLOSE¶

CONTRACTS¶

EXPIRY¶

HIGH¶

LOW¶

OI¶

OPEN¶

SETTLE¶

SYMBOL¶

TIME_STAMP¶

VALUE¶

class plutoPy.model.Currencies.NseOptionsTimeSeries(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query the currency option time-series published by the NSE

CLOSE¶

CONTRACTS¶

EXPIRY¶

HIGH¶

LOW¶

OI¶

OPEN¶

OTYPE¶

SETTLE¶

STRIKE¶

SYMBOL¶

TIME_STAMP¶

VALUE¶

Investment Flows India¶

Investment Flows India

sources: http://www.cdslindia.com https://www.sebi.gov.in

class plutoPy.model.InvestmentFlowsIndia.DiiCashMarket(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query purchases and sales of debt and equity by Domestic Institutional Investors

BUY_VALUE¶: in Rs.

SECURITY_TYPE¶

SELL_VALUE¶: in Rs.

TIME_STAMP¶

class plutoPy.model.InvestmentFlowsIndia.DiiDerivativesMarket(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query purchases and sales of futures and options by Domestic Institutional Investors

BUY_CONTRACTS¶

BUY_VALUE¶: in Rs.

OI_CONTRACTS¶

OI_VALUE¶: in Rs.

SECURITY_TYPE¶

SELL_CONTRACTS¶

SELL_VALUE¶: in Rs.

TIME_STAMP¶

class plutoPy.model.InvestmentFlowsIndia.FiiCashMarket(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query purchases and sales of debt and equity by Foreign Institutional Investors

BUY_VALUE¶: in Rs.

CONVERSION_RATE_USDINR¶

NET_VALUE_DLR¶: net flow in USD

ROUTE¶: Stock Exchange OR Primary market

SECURITY_TYPE¶

SELL_VALUE¶: in Rs.

TIME_STAMP¶

class plutoPy.model.InvestmentFlowsIndia.FiiDerivativesMarket(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockViz

Query purchases and sales of futures and options by Foreign Institutional Investors

BUY_CONTRACTS¶

BUY_VALUE¶: in Rs.

OI_CONTRACTS¶

OI_VALUE¶: in Rs.

SECURITY_TYPE¶

SELL_CONTRACTS¶

SELL_VALUE¶: in Rs.

TIME_STAMP¶

Fama-French¶

Fama French Data-sets

source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/index.html

class plutoPy.model.FamaFrench.FiveFactor3x2Daily(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the Fama-French 5-factor daily returns (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_5_factors_2x3.html)

KEY_ID¶: SMB, RMW, RF, MKT-RF, HML, CMA

RET¶

TIME_STAMP¶

class plutoPy.model.FamaFrench.Industry49Daily(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the Fama-French daily returns of 49 different industries (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_49_ind_port.html)

KEY_ID¶: AGRIC, BANKS, CHEMS, etc…

RET¶

RET_TYPE¶: AVWRD - Average Value Weighted Returns Daily, AEWRD - Average Equal Weighted Returns Daily

TIME_STAMP¶

class plutoPy.model.FamaFrench.MomentumDaily(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the Fama-French daily returns of momentum factor and portfolios factor: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_mom_factor_daily.html portfolios: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_10_port_form_pr_12_2_daily.html

KEY_ID¶: MOM, HI_PRIOR, LO_PRIOR, PRIOR_[2..9]

RET¶

RET_TYPE¶: M for KEY_ID = MOM, AVWRD - Average Value Weighted Returns Daily, AEWRD - Average Equal Weighted Returns Daily

TIME_STAMP¶

class plutoPy.model.FamaFrench.MomentumMonthly(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the Fama-French daily returns of momentum factor and portfolios factor: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_mom_factor.html portfolios: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_10_port_form_pr_12_2.html

KEY_ID¶: MOM, HI_PRIOR, LO_PRIOR, PRIOR_[2..9]

RET¶

RET_TYPE¶: M for KEY_ID = MOM, AVWRD - Average Value Weighted Returns Daily, AEWRD - Average Equal Weighted Returns Daily

TIME_STAMP¶

Yale/Shiller¶

Yale/Shiller Data-sets

source: http://www.econ.yale.edu/~shiller/data.htm

class plutoPy.model.Yale.Confidence(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query Stock Market Confidence Indexes produced by the Yale School of Management. (https://svz.bz/2NzetU3)

NAME¶

STD_ERR¶

TIME_STAMP¶

VALUE¶

class plutoPy.model.Yale.SP500(**kwargs)¶

Bases: sqlalchemy.ext.declarative.api.Base, plutoPy.model.Db.StockVizUs2

Query the monthly stock price, dividends, and earnings data and the consumer price index.

CAPE¶

CLOSE¶

CLOSE_REAL¶

CPI¶

DIVIDEND¶

DIVIDEND_REAL¶

EARNINGS¶

EARNINGS_REAL¶

LONG_IR¶

TIME_STAMP¶

Using Fred¶

Notebooks for this code: python; R.

"""This example illustrates how to get a list of all Fred time-series that have 'India' in its title."""

import sys
sys.path.append("..")
    
from sqlalchemy import func
from plutoPy.model import RoutingSession, Fred

#a list of india focused indices mainted by the Fred
results = RoutingSession.session.query(Fred.Meta.SERIES_ID, Fred.Meta.TICKER, Fred.Meta.NAME, func.min(Fred.TimeSeries.TIME_STAMP).label("start_dt"), func.max(Fred.TimeSeries.TIME_STAMP).label("end_dt")).\
                        join(Fred.TimeSeries, Fred.Meta.SERIES_ID == Fred.TimeSeries.SERIES_ID).\
                        filter(Fred.Meta.NAME.ilike('%india %')).\
                        group_by(Fred.Meta.SERIES_ID, Fred.Meta.TICKER, Fred.Meta.NAME).all()

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

Using World Bank GEM¶

Notebooks for this code: python; R.

"""This example illustrates how to use the World Bank data-set"""

import sys
sys.path.append("..")
    
from sqlalchemy import func, and_
from plutoPy.model import RoutingSession, WorldBank

#fetch all India related meta data

results = (RoutingSession.session.query(WorldBank.Meta)
           .filter(WorldBank.Meta.COUNTRY_NAME == "India")).all()
           
print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    
#fetch cpi inflation for India

results = (RoutingSession.session.query(WorldBank.TimeSeries)
           .filter(and_(WorldBank.TimeSeries.COUNTRY_KEY == 135, WorldBank.TimeSeries.INDICATOR_KEY == 6))
           .order_by(WorldBank.TimeSeries.YEAR)).all()
           
print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

Using InternationalMonetaryFund¶

Notebooks for this code: python; R.

"""This example illustrates how to use the International Monetary Fund data-set"""

import sys
sys.path.append("..")
    
from sqlalchemy import func, and_
from plutoPy.model import RoutingSession, InternationalMonetaryFund
from sqlalchemy.orm import aliased

Meta = aliased(InternationalMonetaryFund.Meta)
TimeSeries = aliased(InternationalMonetaryFund.TimeSeries)

# get meta-data about monthly indicators pertaining to India currently maintained

results = (RoutingSession.session.query(Meta)
            .filter(and_(Meta.AREA == "India", Meta.END_YEAR == 2019, Meta.FREQ == 'M'))
            .all())
           
print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    
# get Indian IIP index (-2147472396)   

results = (RoutingSession.session.query(TimeSeries)
            .filter(TimeSeries.ID == -2147472396)
            .order_by(TimeSeries.YEAR, TimeSeries.MONTH)
            .all())
           
print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    

Using NasdaqOmx Indices¶

Notebooks for this code: python; R.

"""This example illustrates how to get a list of all NASDAQOMX TR Indices and their time-series"""

import sys
sys.path.append("..")
    
from sqlalchemy import func
from plutoPy.model import RoutingSession, NasdaqOmx

# fetch all "India" TR NASDAQOMX indices

results = (RoutingSession.session.query(NasdaqOmx.Meta.NAME,
                                       func.min(NasdaqOmx.TimeSeries.TIME_STAMP).label("start_dt"),
                                       func.max(NasdaqOmx.TimeSeries.TIME_STAMP).label("end_dt"))
            .join(NasdaqOmx.TimeSeries, NasdaqOmx.Meta.ID == NasdaqOmx.TimeSeries.ID)
            .filter(NasdaqOmx.Meta.NAME.ilike('% india %'))
            .group_by(NasdaqOmx.Meta.NAME).all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

Using Yield Curves¶

Notebooks for this code: python; R.

import sys
sys.path.append("..")
    
from sqlalchemy import func, and_
from sqlalchemy.orm import aliased
from plutoPy.model import RoutingSession, YieldCurve

#fetch the latest India Zero Coupon Bond yields

end_dt = RoutingSession.session.query(func.max(YieldCurve.IndiaZeroCoupon.TIME_STAMP)).scalar()

curve = (RoutingSession.session.query(YieldCurve.IndiaZeroCoupon)
         .filter(YieldCurve.IndiaZeroCoupon.TIME_STAMP == end_dt)
         .order_by(YieldCurve.IndiaZeroCoupon.MATURITY)
         .all())

print("the latest India Zero Coupon Bond yields")
for c in curve:
    print(c)
    
#fetch the latest US Treasury Yield Curve    

end_dt = RoutingSession.session.query(func.max(YieldCurve.UsTreasury.TIME_STAMP)).scalar()

curve = (RoutingSession.session.query(YieldCurve.UsTreasury)
         .filter(YieldCurve.UsTreasury.TIME_STAMP == end_dt)
         .all())

print("the latest US Treasury Yield Curve")
for c in curve:
    print(c)
    
#fetch the latest Euro area yield curve

end_dt = RoutingSession.session.query(func.max(YieldCurve.EuroArea.TIME_STAMP)).scalar()

alias1 = aliased(YieldCurve.EuroArea)
alias2 = aliased(YieldCurve.EuroArea)

curve = (RoutingSession.session.query(alias1.TENOR_Y, alias1.TENOR_M, alias1.VALUE.label('G_N_A'), alias2.VALUE.label('G_N_C'))
         .join(alias2, and_(alias1.TENOR_Y == alias2.TENOR_Y, alias1.TENOR_M == alias2.TENOR_M, alias1.TIME_STAMP == alias2.TIME_STAMP))
         .filter(and_(alias1.TIME_STAMP == end_dt, alias1.CURVE_ID == 'G_N_A', alias2.CURVE_ID == 'G_N_C'))
         .order_by(alias1.TENOR_Y, alias1.TENOR_M)
         .all())

print("the latest Euro Area Yield Curve")
for c in curve:
    print(c)    

Using Indices¶

Notebooks for this code: python; R.

"""This example illustrates how to get a list of all the latest NSE Indices, and
the constituents of a specific index."""

import sys
sys.path.append("..")
    
from sqlalchemy import func, text
from plutoPy.model import RoutingSession, Indices

# fetch the last 10 day India VIX levels

results = (RoutingSession.session.query(Indices.IndiaVixTimeSeries)
           .order_by(Indices.IndiaVixTimeSeries.TIME_STAMP.desc())
           .limit(10))

for instance in results:
    print(instance)
    
# fetch the latest rates across tenors

end_dt = RoutingSession.session.query(func.max(Indices.IndiaGsecTimeSeries.TIME_STAMP)).scalar()

results = (RoutingSession.session.query(Indices.IndiaGsecTimeSeries)
            .filter(Indices.IndiaGsecTimeSeries.TIME_STAMP == end_dt)
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

# a list of all current NSE indices

end_dt = RoutingSession.session.query(func.max(Indices.NseTimeSeries.TIME_STAMP)).scalar()

results = (RoutingSession.session.query(Indices.NseTimeSeries.NAME)
            .filter(Indices.NseTimeSeries.TIME_STAMP == end_dt)
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance.NAME)
    
# fetch the latest NSE NIFTY 50 constituents    
    
latest_dt = (RoutingSession.session.query(func.max(Indices.NseConstituents.TIME_STAMP))
            .filter(Indices.NseConstituents.NAME == "NIFTY 50")
            .scalar())    

results = (RoutingSession.session.query(Indices.NseConstituents.SYMBOL, Indices.NseConstituents.CAP_WEIGHT)
            .filter(Indices.NseConstituents.TIME_STAMP == latest_dt, Indices.NseConstituents.NAME == "NIFTY 50")
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

# fetch the latest BSE SENSEX constituents    
    
latest_dt = (RoutingSession.session.query(func.max(Indices.BseConstituents.TIME_STAMP))
             .filter(Indices.BseConstituents.NAME == "sp bse sensex")
             .scalar())    

results = (RoutingSession.session.query(Indices.BseConstituents.SYMBOL, Indices.BseConstituents.SECURITY_NAME)
            .filter(Indices.BseConstituents.TIME_STAMP == latest_dt, Indices.BseConstituents.NAME == "sp bse sensex")
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

# fetch index date ranges published on yahoo finance

results = (RoutingSession.session.query(Indices.YahooFinanceTimeSeries.NAME, 
                                        func.min(Indices.YahooFinanceTimeSeries.TIME_STAMP).label("start_dt"), 
                                        func.max(Indices.YahooFinanceTimeSeries.TIME_STAMP).label("end_dt"))
            .group_by(Indices.YahooFinanceTimeSeries.NAME)
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    
# fetch index date ranges published by Barclays

results = (RoutingSession.session.query(Indices.BarclaysMeta.FAMILY, Indices.BarclaysMeta.NAME, 
                                        func.min(Indices.BarclaysTimeSeries.TIME_STAMP).label("start_dt"), 
                                        func.max(Indices.BarclaysTimeSeries.TIME_STAMP).label("end_dt"))
            .join(Indices.BarclaysTimeSeries, Indices.BarclaysMeta.TICKER == Indices.BarclaysTimeSeries.TICKER)
            .group_by(Indices.BarclaysMeta.FAMILY, Indices.BarclaysMeta.NAME)
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    

Using ETFsUs¶

Notebooks for this code: python; R.

Using MutualFundsIndia¶

Notebooks for this code: python; R.

"""This example illustrates how to use the MutualFundsIndia data-set"""

import sys
sys.path.append("..")

import pandas as pd
from sqlalchemy import func, or_, and_
from plutoPy.model import RoutingSession, MutualFundsIndia

# who are the biggest 5?

lastAumDate = RoutingSession.session.query(func.max(MutualFundsIndia.AumFundwise.PERIOD)).scalar()
print(f"{lastAumDate}")

aums = (RoutingSession.session.query(MutualFundsIndia.AumFundwise.FUND, MutualFundsIndia.AumFundwise.AVG_AUM_WO_FOFD + MutualFundsIndia.AumFundwise.AVG_AUM_FOFD)
        .filter(MutualFundsIndia.AumFundwise.PERIOD == lastAumDate)
        .order_by((MutualFundsIndia.AumFundwise.AVG_AUM_WO_FOFD + MutualFundsIndia.AumFundwise.AVG_AUM_FOFD).desc())).all()

aumDf = pd.DataFrame(aums, columns=['FUND', 'AUM']) #analyze this!
print(aumDf[0:5])

# deep dive: largest 10 mid-cap funds by AUM

lastMetaDate = RoutingSession.session.query(func.max(MutualFundsIndia.Meta.AS_OF)).scalar()
lastSwDate = RoutingSession.session.query(func.max(MutualFundsIndia.AumSchemewise.PERIOD)).scalar()
print(f"{lastMetaDate}/{lastSwDate}")

# Meta to filter for the 'Mid-Cap' category
# AumSchemewise to sort funds by AUM - largest first
# NavTimeSeries to get the start and end dates for which NAVs are available

midCaps = (RoutingSession.session.query(MutualFundsIndia.Meta.SCHEME_CODE, 
                                       MutualFundsIndia.AumSchemewise.SCHEME_NAME,
                                       MutualFundsIndia.Meta.CATEGORY, 
                                       MutualFundsIndia.AumSchemewise.AVG_AUM_WO_FOFD,
                                       func.min(MutualFundsIndia.NavTimeSeries.TIME_STAMP).label("start_dt"), 
                                       func.max(MutualFundsIndia.NavTimeSeries.TIME_STAMP).label("end_dt"))
        .join(MutualFundsIndia.AumSchemewise, MutualFundsIndia.Meta.SCHEME_CODE == MutualFundsIndia.AumSchemewise.SCHEME_CODE)
        .join(MutualFundsIndia.NavTimeSeries, MutualFundsIndia.NavTimeSeries.SCHEME_CODE == MutualFundsIndia.AumSchemewise.SCHEME_CODE)
        .filter(and_(MutualFundsIndia.AumSchemewise.PERIOD == lastSwDate,
                    MutualFundsIndia.Meta.AS_OF == lastMetaDate, 
                    MutualFundsIndia.Meta.CATEGORY == 'Mid-Cap'))
        .group_by(MutualFundsIndia.Meta.SCHEME_CODE, 
            MutualFundsIndia.AumSchemewise.SCHEME_NAME,
            MutualFundsIndia.Meta.CATEGORY, 
            MutualFundsIndia.AumSchemewise.AVG_AUM_WO_FOFD)
        .order_by(MutualFundsIndia.AumSchemewise.AVG_AUM_WO_FOFD.desc())).all()

print(f"fetched: {len(midCaps)}")

# get a pandas dataframe out of the list of tuples
mcDf = pd.DataFrame(midCaps)

pd.set_option('display.max_columns', None)
print(mcDf[0:10])

# get the NAV time-series
scode = int(mcDf.iloc[2]['SCHEME_CODE'])
navs = (RoutingSession.session.query(MutualFundsIndia.NavTimeSeries.TIME_STAMP, MutualFundsIndia.NavTimeSeries.NAV)
        .filter(MutualFundsIndia.NavTimeSeries.SCHEME_CODE == scode)
        .order_by(MutualFundsIndia.NavTimeSeries.TIME_STAMP)).all()

navDf = pd.DataFrame(navs) #analyze this!
print(navDf[0:10])         

Using EquitiesUs¶

Notebooks for this code: python; R.

Using Equities India (NSE)¶

Notebooks for this code: python; R.

"""This example illustrates how to use the EquitiesIndiaNse data-set."""

import sys
sys.path.append("..")
    
from sqlalchemy import func, and_, or_
from plutoPy.model import RoutingSession, EquitiesIndiaNse
from datetime import date, datetime

# fetch the earliest 10 listed equity

results = (RoutingSession.session.query(EquitiesIndiaNse.Tickers)
           .order_by(EquitiesIndiaNse.Tickers.DATE_LISTING)
           .limit(10)
           .all())

for instance in results:
    print(instance)
    
# fetch some "misc" info for State Bank of India   

end_dt = RoutingSession.session.query(func.max(EquitiesIndiaNse.MiscInfo.TIME_STAMP)).scalar() 

results = (RoutingSession.session.query(EquitiesIndiaNse.MiscInfo)
            .filter(and_(EquitiesIndiaNse.MiscInfo.TIME_STAMP == end_dt, EquitiesIndiaNse.MiscInfo.SYMBOL == 'SBIN'))
            .all())

print("misc info for SBIN:")
for instance in results:
    print(instance)

# fetch the market-cap decile of DHFL since we started capturing the data-set

results = (RoutingSession.session.query(EquitiesIndiaNse.MarketCapDecile)
            .filter(EquitiesIndiaNse.MarketCapDecile.SYMBOL == 'DHFL')
            .all())

print("market-cap deciles for DHFL over time:")
for instance in results:
    print(instance)
    
# fetch the latest end-of-day prices for State Bank of India

end_dt = RoutingSession.session.query(func.max(EquitiesIndiaNse.EodTimeSeries.TIME_STAMP)).scalar()

results = (RoutingSession.session.query(EquitiesIndiaNse.EodTimeSeries)
            .filter(and_(EquitiesIndiaNse.EodTimeSeries.TIME_STAMP == end_dt, EquitiesIndiaNse.EodTimeSeries.SYMBOL == 'SBIN'))
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    
# fetch the last 10 day EOD prices for State Bank of India

results = (RoutingSession.session.query(EquitiesIndiaNse.EodTimeSeries)
            .filter(and_(or_(EquitiesIndiaNse.EodTimeSeries.SERIES == 'EQ', EquitiesIndiaNse.EodTimeSeries.SERIES == 'BE'), 
                         EquitiesIndiaNse.EodTimeSeries.SYMBOL == 'SBIN'))
            .order_by(EquitiesIndiaNse.EodTimeSeries.TIME_STAMP.desc())
            .limit(10)
            .all())

for instance in results:
    print(instance)    
    
    
# UPL did a 1:2 bonus on 2019-07-02. see unadjusted eod vs. adjusted eod

startDt = datetime(2019, 6, 15)    
endDt = datetime(2019, 7, 15)

print("unadjusted eod")
results = (RoutingSession.session.query(EquitiesIndiaNse.EodTimeSeries)
            .filter(and_(or_(EquitiesIndiaNse.EodTimeSeries.SERIES == 'EQ', EquitiesIndiaNse.EodTimeSeries.SERIES == 'BE'), 
                         EquitiesIndiaNse.EodTimeSeries.SYMBOL == 'UPL',
                         EquitiesIndiaNse.EodTimeSeries.TIME_STAMP >= startDt,
                         EquitiesIndiaNse.EodTimeSeries.TIME_STAMP <= endDt))
            .order_by(EquitiesIndiaNse.EodTimeSeries.TIME_STAMP)
            .all())

for instance in results:
    print(instance)

print("adjusted eod")    
results = (RoutingSession.session.query(EquitiesIndiaNse.EodAdjustedTimeSeries)
            .filter(and_(EquitiesIndiaNse.EodAdjustedTimeSeries.SYMBOL == 'UPL',
                         EquitiesIndiaNse.EodAdjustedTimeSeries.TIME_STAMP >= startDt,
                         EquitiesIndiaNse.EodAdjustedTimeSeries.TIME_STAMP <= endDt))
            .order_by(EquitiesIndiaNse.EodAdjustedTimeSeries.TIME_STAMP)
            .all())

for instance in results:
    print(instance)            

 
# fetch the last 10 day returns for State Bank of India

results = (RoutingSession.session.query(EquitiesIndiaNse.DailyReturns)
            .filter(EquitiesIndiaNse.DailyReturns.SYMBOL == 'SBIN')
            .order_by(EquitiesIndiaNse.DailyReturns.TIME_STAMP.desc())
            .limit(10)
            .all())

for instance in results:
    print(instance)        
    
# fetch the last 10 corporate actions for State Bank of India

results = (RoutingSession.session.query(EquitiesIndiaNse.CorporateActions)
            .filter(EquitiesIndiaNse.CorporateActions.SYMBOL == 'SBIN')
            .order_by(EquitiesIndiaNse.CorporateActions.EX_DATE.desc())
            .limit(10)
            .all())

for instance in results:
    print(instance)

# fetch the last 10 corporate events for State Bank of India

results = (RoutingSession.session.query(EquitiesIndiaNse.CorporateEvents)
            .filter(EquitiesIndiaNse.CorporateEvents.SYMBOL == 'SBIN')
            .order_by(EquitiesIndiaNse.CorporateEvents.EVENT_DATE.desc())
            .limit(10)
            .all())

for instance in results:
    print(instance)
        
    
# fetch the last 24 quarter EPS for State Bank of India

refIds = (RoutingSession.session.query(EquitiesIndiaNse.CorporateResultsMeta)
            .filter(and_(EquitiesIndiaNse.CorporateResultsMeta.SYMBOL == 'SBIN'),
                    EquitiesIndiaNse.CorporateResultsMeta.IS_CONSOLIDATED == False,
                    EquitiesIndiaNse.CorporateResultsMeta.PERIOD.ilike('%quarter'))
            .order_by(EquitiesIndiaNse.CorporateResultsMeta.PERIOD_END.desc())
            .limit(24)
            .all())

for instance in refIds:
    print(instance)
    
    results = (RoutingSession.session.query(EquitiesIndiaNse.CorporateResults)
               .filter(and_(EquitiesIndiaNse.CorporateResults.REF_ID == instance.REF_ID,
                            EquitiesIndiaNse.CorporateResults.KEY.ilike('%diluted%before%')))
               .all())
    
    for r in results:
        print(r)
        

Using Equities Futures and Options traded on the NSE¶

Notebooks for this code: python; R.

"""This example illustrates how to use the EquitiesFuturesAndOptionsIndiaNse data-set."""

import sys
sys.path.append("..")

import pandas as pd    
from sqlalchemy import func, and_, or_, text, Integer
from sqlalchemy.orm import aliased
from sqlalchemy.sql.expression import cast
from plutoPy.model import RoutingSession, EquitiesFuturesAndOptionsIndiaNse
from datetime import date, datetime, timedelta

# get all NIFTY futures contract traded right now

print("NIFTY futures contract traded right now")

end_dt = RoutingSession.session.query(func.max(EquitiesFuturesAndOptionsIndiaNse.FuturesEodTimeSeries.TIME_STAMP)).scalar() 

results = (RoutingSession.session.query(EquitiesFuturesAndOptionsIndiaNse.FuturesEodTimeSeries)
      .filter(and_(EquitiesFuturesAndOptionsIndiaNse.FuturesEodTimeSeries.SYMBOL == "NIFTY", 
                   EquitiesFuturesAndOptionsIndiaNse.FuturesEodTimeSeries.TIME_STAMP == end_dt))
      .all())

for instance in results:
    print(instance)
    
# get all NIFTY option contracts at the nearest expiry traded right now

print("NIFTY option contracts at the nearest expiry traded right now")

end_dt = RoutingSession.session.query(func.max(EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.TIME_STAMP)).scalar() 

expiry = (RoutingSession.session.query(func.min(EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.EXPIRY))
          .filter(and_(EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.SYMBOL == "NIFTY",
                       EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.TIME_STAMP == end_dt))
          .scalar())

results = (RoutingSession.session.query(EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries)
      .filter(and_(EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.SYMBOL == "NIFTY", 
                   EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.EXPIRY == expiry,
                   cast(EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.STRIKE, Integer) % 100 == 0,
                   EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.TIME_STAMP == end_dt))
      .order_by(EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.STRIKE, EquitiesFuturesAndOptionsIndiaNse.OptionsEodTimeSeries.OTYPE)
      .all())

for instance in results:
    print(instance)
    
# get all greeks for the NIFTY option contracts at the nearest expiry traded right now

print("greeks for the NIFTY option contracts at the nearest expiry traded right now")

end_dt = RoutingSession.session.query(func.max(EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.TIME_STAMP)).scalar() 

expiry = (RoutingSession.session.query(func.min(EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.EXPIRY))
          .filter(and_(EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.SYMBOL == "NIFTY",
                       EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.TIME_STAMP == end_dt))
          .scalar())

results = (RoutingSession.session.query(EquitiesFuturesAndOptionsIndiaNse.OptionGreeks)
      .filter(and_(EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.SYMBOL == "NIFTY", 
                   EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.EXPIRY == expiry,
                   cast(EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.STRIKE, Integer) % 100 == 0,
                   EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.TIME_STAMP == end_dt))
      .order_by(EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.STRIKE, EquitiesFuturesAndOptionsIndiaNse.OptionGreeks.OTYPE)
      .all())


for instance in results:
    print(instance)    
    
# get NIFTY's lot-sizes at different expiries

print("NIFTY's lot-sizes at different expiries")

futureDate = date.today() + timedelta(days=30*3)

results = (RoutingSession.session.query(EquitiesFuturesAndOptionsIndiaNse.LotSize)
      .filter(and_(EquitiesFuturesAndOptionsIndiaNse.LotSize.SYMBOL == "NIFTY", EquitiesFuturesAndOptionsIndiaNse.LotSize.CONTRACT <= futureDate))
      .order_by(EquitiesFuturesAndOptionsIndiaNse.LotSize.CONTRACT.desc())
      .limit(10)
      .all())

for instance in results:
    print(instance)

Using Commodity Futures And Options¶

Notebooks for this code: python; R.

"""This example illustrates how to use the CommodityFuturesAndOptions data-set."""

import sys
sys.path.append("..")

import pandas as pd    
from sqlalchemy import func, and_, or_, text
from sqlalchemy.orm import aliased
from plutoPy.model import RoutingSession, CommodityFuturesAndOptions
from datetime import date, datetime

pd.set_option('display.max_columns', 500)

# get all the commodities that are being traded in COMEX and NYMEX

print("commodity futures that are being traded in COMEX and NYMEX")

end_dt = RoutingSession.session.query(func.max(CommodityFuturesAndOptions.CmeEod.TIME_STAMP)).scalar() 

alias1 = aliased(CommodityFuturesAndOptions.CmeEod)
alias2 = aliased(CommodityFuturesAndOptions.CmeEod)

t1 = (RoutingSession.session.query(alias1.PRODUCT_SYMBOL, alias1.PRODUCT_DESCRIPTION, func.sum(alias1.VOLUME).label("total_volume"))
      .filter(alias1.TIME_STAMP == end_dt)
      .group_by(alias1.PRODUCT_SYMBOL, alias1.PRODUCT_DESCRIPTION)
      .order_by(text("total_volume desc"))
      .all())

t2 = (RoutingSession.session.query(alias1.PRODUCT_SYMBOL, func.min(alias1.TIME_STAMP).label("start_dt"))
      .group_by(alias1.PRODUCT_SYMBOL)
      .order_by(text("start_dt"))
      .all())

pd1 = pd.DataFrame(t1, columns=['SYMBOL', 'DESCRIPTION', 'VOLUME'])
pd2 = pd.DataFrame(t2, columns=['SYMBOL', 'LISTED_DATE'])
tradedContracts = pd.merge(pd1, pd2, on='SYMBOL')

print(tradedContracts[tradedContracts['VOLUME'] > 0])
    
# get all the commodity futures that are being traded in MCX

print("commodity futures that are being traded in MCX")

end_dt = RoutingSession.session.query(func.max(CommodityFuturesAndOptions.McxEod.TIME_STAMP)).scalar() 

alias1 = aliased(CommodityFuturesAndOptions.McxEod)
alias2 = aliased(CommodityFuturesAndOptions.McxEod)

t1 = (RoutingSession.session.query(alias1.CONTRACT, func.sum(alias1.OI).label("total_oi"))
      .filter(and_(alias1.TIME_STAMP == end_dt, 
                   or_(alias1.OTYPE == 'XX', alias1.OTYPE == 'FUTCOM')))
      .group_by(alias1.CONTRACT)
      .order_by(text("total_oi desc"))
      .all())

t2 = (RoutingSession.session.query(alias1.CONTRACT, func.min(alias1.TIME_STAMP).label("start_dt"))
      .group_by(alias1.CONTRACT)
      .filter(or_(alias1.OTYPE == 'XX', alias1.OTYPE == 'FUTCOM'))
      .order_by(text("start_dt"))
      .all())

pd1 = pd.DataFrame(t1, columns=['SYMBOL', 'OI'])
pd2 = pd.DataFrame(t2, columns=['SYMBOL', 'LISTED_DATE'])
tradedContracts = pd.merge(pd1, pd2, on='SYMBOL')

print(tradedContracts[tradedContracts['OI'] > 0])

# get all the commodity futures that are being traded in MCX

print("commodity futures that are being traded in NCDEX")

end_dt = RoutingSession.session.query(func.max(CommodityFuturesAndOptions.NcdexEod.TIME_STAMP)).scalar() 

alias1 = aliased(CommodityFuturesAndOptions.NcdexEod)
alias2 = aliased(CommodityFuturesAndOptions.NcdexEod)

t1 = (RoutingSession.session.query(alias1.COMMODITY, func.sum(alias1.OI).label("total_oi"))
      .filter(alias1.TIME_STAMP == end_dt)
      .group_by(alias1.COMMODITY)
      .order_by(text("total_oi desc"))
      .all())

t2 = (RoutingSession.session.query(alias1.COMMODITY, func.min(alias1.TIME_STAMP).label("start_dt"))
      .group_by(alias1.COMMODITY)
      .order_by(text("start_dt"))
      .all())

pd1 = pd.DataFrame(t1, columns=['COMMODITY', 'OI'])
pd2 = pd.DataFrame(t2, columns=['COMMODITY', 'LISTED_DATE'])
tradedContracts = pd.merge(pd1, pd2, on='COMMODITY')

print(tradedContracts[tradedContracts['OI'] > 0])

Using Currencies¶

Notebooks for this code: python; R.

"""This example illustrates how to use the Currencies data-set."""

import sys
sys.path.append("..")

import pandas as pd    
from sqlalchemy import func, and_, or_, text, Integer
from sqlalchemy.orm import aliased
from sqlalchemy.sql.expression import cast
from plutoPy.model import RoutingSession, Currencies
from datetime import date, datetime, timedelta

print("AlphaVantage end-of-day pairs:")

results = (RoutingSession.session.query(Currencies.AvEodTimeSeries.SYMBOL, 
                                        func.min(Currencies.AvEodTimeSeries.TIME_STAMP).label('start_dt'), 
                                        func.max(Currencies.AvEodTimeSeries.TIME_STAMP).label('end_dt'))
            .group_by(Currencies.AvEodTimeSeries.SYMBOL)
            .order_by(text('start_dt'))).all()

for instance in results:
    print(instance)

######################################################


# get traded futures pairs

print("traded futures pairs:")

results = (RoutingSession.session.query(Currencies.NseFuturesTimeSeries.SYMBOL, 
                                        func.min(Currencies.NseFuturesTimeSeries.TIME_STAMP).label('start_dt'), 
                                        func.max(Currencies.NseFuturesTimeSeries.TIME_STAMP).label('end_dt'))
            .group_by(Currencies.NseFuturesTimeSeries.SYMBOL)
            .order_by(text('start_dt'))
            .all())

for instance in results:
    print(instance)
    
# get the latest USDINR option chain for the nearest expiry    

print("latest USDINR option chain for the nearest expiry:")

end_dt = RoutingSession.session.query(func.max(Currencies.NseOptionsTimeSeries.TIME_STAMP)).scalar() 

expiry = (RoutingSession.session.query(func.min(Currencies.NseOptionsTimeSeries.EXPIRY))
          .filter(and_(Currencies.NseOptionsTimeSeries.SYMBOL == "USDINR",
                       Currencies.NseOptionsTimeSeries.TIME_STAMP == end_dt))
          .scalar())

results = (RoutingSession.session.query(Currencies.NseOptionsTimeSeries)
      .filter(and_(Currencies.NseOptionsTimeSeries.SYMBOL == "USDINR", 
                   Currencies.NseOptionsTimeSeries.EXPIRY == expiry,
                   Currencies.NseOptionsTimeSeries.TIME_STAMP == end_dt))
      .order_by(Currencies.NseOptionsTimeSeries.STRIKE, Currencies.NseOptionsTimeSeries.OTYPE)
      .all())

for instance in results:
    print(instance)
    
# get the currencies tracked by AlphaVantage end-of-day

print("AlphaVantage end-of-day pairs:")

results = (RoutingSession.session.query(Currencies.AvEodTimeSeries.SYMBOL, 
                                        func.min(Currencies.AvEodTimeSeries.TIME_STAMP).label('start_dt'), 
                                        func.max(Currencies.AvEodTimeSeries.TIME_STAMP).label('end_dt'))
            .group_by(Currencies.AvEodTimeSeries.SYMBOL)
            .order_by(text('start_dt'))
            .all())

for instance in results:
    print(instance)
    

# get the currencies tracked by AlphaVantage 30-min bars
    
print("AlphaVantage 30-min bars:")

results = (RoutingSession.session.query(Currencies.Av30minTimeSeries.SYMBOL, 
                                        func.min(Currencies.Av30minTimeSeries.TIME_STAMP).label('start_dt'), 
                                        func.max(Currencies.Av30minTimeSeries.TIME_STAMP).label('end_dt'),
                                        func.count(Currencies.Av30minTimeSeries.TIME_STAMP).label('count'))
            .group_by(Currencies.Av30minTimeSeries.SYMBOL)
            .order_by(text('count desc'))
            .all())

for instance in results:
    print(instance)            
    

Using InvestmentFlowsIndia¶

Notebooks for this code: python; R.

"""This example illustrates how to use the InvestmentFlowsIndia data-set."""

import sys
sys.path.append("..")

import pandas as pd    
from sqlalchemy import func, and_, or_, text, Integer
from sqlalchemy.orm import aliased
from sqlalchemy.sql.expression import cast
from plutoPy.model import RoutingSession, InvestmentFlowsIndia
from datetime import date, datetime, timedelta

# get daily DII and FII flows for the last 20 days for the cash market

print("daily DII and FII flows for the last 20 days for the cash market:")

results = (RoutingSession.session.query(InvestmentFlowsIndia.DiiCashMarket.TIME_STAMP, InvestmentFlowsIndia.DiiCashMarket.SECURITY_TYPE,
                                        func.sum(InvestmentFlowsIndia.DiiCashMarket.BUY_VALUE + InvestmentFlowsIndia.FiiCashMarket.BUY_VALUE).label('BUY'), 
                                        func.sum(InvestmentFlowsIndia.DiiCashMarket.SELL_VALUE + InvestmentFlowsIndia.FiiCashMarket.SELL_VALUE).label('SELL'),
                                        func.sum(InvestmentFlowsIndia.DiiCashMarket.BUY_VALUE + InvestmentFlowsIndia.FiiCashMarket.BUY_VALUE
                                         - InvestmentFlowsIndia.DiiCashMarket.SELL_VALUE - InvestmentFlowsIndia.FiiCashMarket.SELL_VALUE).label('NET'))
            .outerjoin(InvestmentFlowsIndia.FiiCashMarket, and_(InvestmentFlowsIndia.FiiCashMarket.TIME_STAMP == InvestmentFlowsIndia.DiiCashMarket.TIME_STAMP,
                                                                InvestmentFlowsIndia.FiiCashMarket.SECURITY_TYPE == InvestmentFlowsIndia.DiiCashMarket.SECURITY_TYPE))
            .group_by(InvestmentFlowsIndia.DiiCashMarket.TIME_STAMP, InvestmentFlowsIndia.DiiCashMarket.SECURITY_TYPE)
            .order_by(InvestmentFlowsIndia.DiiCashMarket.TIME_STAMP.desc())
            .limit(20)
            .all())

for instance in results:
    print(instance)
    
# get instruments traced for DIIs in the derivative market   

print("instruments traced for DIIs in the derivative market") 

results = (RoutingSession.session.query(InvestmentFlowsIndia.DiiDerivativesMarket.SECURITY_TYPE,
                                        func.min(InvestmentFlowsIndia.DiiDerivativesMarket.TIME_STAMP).label('start_dt'),
                                        func.max(InvestmentFlowsIndia.DiiDerivativesMarket.TIME_STAMP).label('end_dt'))
            .group_by(InvestmentFlowsIndia.DiiDerivativesMarket.SECURITY_TYPE)
            .order_by(text('start_dt'))
            .all())

for instance in results:
    print(instance)
    
# get instruments traced for FIIs in the derivative market   

print("instruments traced for FIIs in the derivative market") 

results = (RoutingSession.session.query(InvestmentFlowsIndia.FiiDerivativesMarket.SECURITY_TYPE,
                                        func.min(InvestmentFlowsIndia.FiiDerivativesMarket.TIME_STAMP).label('start_dt'),
                                        func.max(InvestmentFlowsIndia.FiiDerivativesMarket.TIME_STAMP).label('end_dt'))
            .group_by(InvestmentFlowsIndia.FiiDerivativesMarket.SECURITY_TYPE)
            .order_by(text('start_dt'))
            .all())

for instance in results:
    print(instance)
    

Using FamaFrench¶

Notebooks for this code: python; R.

"""This example illustrates how to use the Fama-French dataset."""

import sys
sys.path.append("..")
    
from sqlalchemy import func, text
from plutoPy.model import RoutingSession, FamaFrench

# show data-ranges for Fama-french factors

results = (RoutingSession.session.query(FamaFrench.FiveFactor3x2Daily.KEY_ID, 
                                        func.min(FamaFrench.FiveFactor3x2Daily.TIME_STAMP).label("start_dt"), 
                                        func.max(FamaFrench.FiveFactor3x2Daily.TIME_STAMP).label("end_dt"))
            .group_by(FamaFrench.FiveFactor3x2Daily.KEY_ID)
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

# show data-ranges for Fama-french industry daily returns

results = (RoutingSession.session.query(FamaFrench.Industry49Daily.KEY_ID,
                                        FamaFrench.Industry49Daily.RET_TYPE, 
                                        func.min(FamaFrench.Industry49Daily.TIME_STAMP).label("start_dt"), 
                                        func.max(FamaFrench.Industry49Daily.TIME_STAMP).label("end_dt"))
            .group_by(FamaFrench.Industry49Daily.KEY_ID, FamaFrench.Industry49Daily.RET_TYPE)
            .order_by(text("start_dt"))
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    
# show data-ranges for Fama-french momentum daily returns

results = (RoutingSession.session.query(FamaFrench.MomentumDaily.KEY_ID, 
                                        FamaFrench.MomentumDaily.RET_TYPE,
                                        func.min(FamaFrench.MomentumDaily.TIME_STAMP).label("start_dt"), 
                                        func.max(FamaFrench.MomentumDaily.TIME_STAMP).label("end_dt"))
            .group_by(FamaFrench.MomentumDaily.KEY_ID, FamaFrench.MomentumDaily.RET_TYPE)
            .order_by(text("start_dt"))
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)  
    
    
# show data-ranges for Fama-french momentum daily returns

results = (RoutingSession.session.query(FamaFrench.MomentumMonthly.KEY_ID, 
                                        FamaFrench.MomentumMonthly.RET_TYPE,
                                        func.min(FamaFrench.MomentumMonthly.TIME_STAMP).label("start_dt"), 
                                        func.max(FamaFrench.MomentumMonthly.TIME_STAMP).label("end_dt"))
            .group_by(FamaFrench.MomentumMonthly.KEY_ID, FamaFrench.MomentumMonthly.RET_TYPE)
            .order_by(text("start_dt"))
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)   

Using Yale/Shiller¶

Notebooks for this code: python; R.

"""This example illustrates how to use the Yale/Shiller dataset."""

import sys
sys.path.append("..")
    
from sqlalchemy import func, text, extract, and_
from plutoPy.model import RoutingSession, Yale
from datetime import date

# start and end dates of confidence indices

results = (RoutingSession.session.query(Yale.Confidence.NAME,
                                        func.min(Yale.Confidence.TIME_STAMP).label("start_dt"),
                                        func.max(Yale.Confidence.TIME_STAMP).label("end_dt"))
            .group_by(Yale.Confidence.NAME)
            .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)
    
# SP500 close and CAPE at the end of each year since 1995

startDate = date(1995, 12, 1)    
results = (RoutingSession.session.query(extract('year', Yale.SP500.TIME_STAMP).label('Y'), Yale.SP500.CLOSE, Yale.SP500.CAPE)
           .filter(and_(extract('month', Yale.SP500.TIME_STAMP) == 12, Yale.SP500.TIME_STAMP >= startDate))
           .order_by(Yale.SP500.TIME_STAMP)
           .all())

print(f"fetched: {len(results)}")
for instance in results:
    print(instance)

Welcome to plutoPy’s documentation!¶

model¶

Fred¶

World Bank GEM¶

InternationalMonetaryFund¶

NASDAQOMX Indices¶

Yield Curves¶

Indices¶

US ETFs¶

MutualFundsIndia¶

US Equities¶

EquitiesIndiaNse¶

Equities Futures and Options traded on the NSE¶

Commodity Futures And Options¶

Currencies¶

Investment Flows India¶

Fama-French¶

Yale/Shiller¶

Using Fred¶

Using World Bank GEM¶

Using InternationalMonetaryFund¶

Using NasdaqOmx Indices¶

Using Yield Curves¶

Using Indices¶

Using ETFsUs¶

Using MutualFundsIndia¶

Using EquitiesUs¶

Using Equities India (NSE)¶

Using Equities Futures and Options traded on the NSE¶

Using Commodity Futures And Options¶

Using Currencies¶

Using InvestmentFlowsIndia¶

Using FamaFrench¶

Using Yale/Shiller¶

Indices and tables¶