stock-pandas 0.17.0

The wrapper of `pandas.DataFrame` with stock statistics and indicators support.

stock-pandas

stock-pandas inherits and extends pandas.DataFrame to support:

• Stock Statistics
• Stock Indicators, including:
  • Trend-following momentum indicators, such as MA, EMA, MACD, BBI
  • Dynamic support and resistance indicators, such as BOLL
  • Over-bought / over-sold indicators, such as KDJ, RSI

stock-pandas makes automatical trading much easier. stock-pandas requires Python >= 3.6

With the help of stock-pandas and mplfinance, we could easily draw something like:

The code example is available at here.

Install

pip install stock-pandas

Usage

from stock_pandas import StockDataFrame

# or
import stock_pandas as spd

We also have some examples with annotations in the example directory, you could use JupyterLab or Jupyter notebook to play with them.

StockDataFrame

StockDataFrame inherits from pandas.DataFrame, so if you are familiar with pandas.DataFrame, you are already ready to use stock-pandas

import pandas as pd
stock = StockDataFrame(pd.read_csv('stock.csv'))

As we know, we could use [], which called pandas indexing (a.k.a. __getitem__ in python) to select out lower-dimensional slices. In addition to indexing with colname (column name of the DataFrame), we could also do indexing by directives.

stock[directive] # Gets a pandas.Series

stock[[directive0, directive1]] # Gets a StockDataFrame

We have an example to show the most basic indexing using [directive]

stock = StockDataFrame({
    'open' : ...,
    'high' : ...,
    'low'  : ...,
    'close': [5, 6, 7, 8, 9]
})

stock['ma:2']

# 0    NaN
# 1    5.5
# 2    6.5
# 3    7.5
# 4    8.5
# Name: ma:2,close, dtype: float64

Which prints the 2-period simple moving average on column "close".

stock.exec(directive: str, create_column: bool=False) -> np.ndarray

Executes the given directive and returns a numpy ndarray according to the directive.

stock['ma:5'] # returns a Series

stock.exec('ma:5', create_column=True) # returns a numpy ndarray

# This will only calculate without creating a new column in the dataframe
stock.exec('ma:20')

stock.alias(alias: str, name: str) -> None

Defines column alias or directive alias

• alias str the alias name
• name str the name of an existing column or the directive string

# Some plot library such as `mplfinance` requires a column named capitalized `Open`,
# but it is ok, we could create an alias.
stock.alias('Open', 'open')

stock.alias('buy_point', 'kdj.j < 0')

stock.append(other, *args, **kwargs) -> StockDataFrame

Appends rows of other to the end of caller, returning a new object.

This method has nearly the same hehavior of pandas.DataFrame.append(), but instead it returns an instance of StockDataFrame

Syntax of directive

directive := command | command operator expression
operator := '/' | '\' | '><' | '<' | '<=' | '==' | '>=' | '>'
expression := float | command

command := command_name | command_name : arguments
command_name := main_command_name | main_command_name.sub_command_name
main_command_name := alphabets
sub_command_name := alphabets

arguments := argument | argument , arguments
argument := empty_string | string | ( directive )

directive Example

Here lists several use cases of column names

# The middle band of bollinger bands
#   which is actually a 20-period (default) moving average
stock['boll']

# kdj j less than 0
# This returns a series of bool type
stock['kdj.j < 0']

# 9-period kdj k cross up 3-day kdj d
stock['kdj.k:9 / kdj.d:3']

# 5-period simple moving average
stock['ma:5']

# 10-period simple moving average on open prices
stock['ma:10,open']

# Dataframe of 5-period, 10-period, 30-period ma
stock[[
    'ma:5',
    'ma:10',
    'ma:30'
]]

# Which means we use the default values of the first and the second parameters,
# and specify the third parameter
stock['macd:,,10']

# We must wrap a parameter which is a nested command or directive
stock['increase:(ma:20,close),3']

# stock-pandas has a powerful directive parser,
# so we could even write directives like this:
stock['''
repeat
    :
        (
            column:close > boll.upper
        ),
        5
''']

Built-in Commands of Indicators

Document syntax explanation:

• param0 int which means param0 is a required parameter of type int.
• param1? str='close' which means parameter param1 is optional with default value 'close'.

Actually, all parameters of a command are of string type, so the int here means an interger-like string.

ma, simple Moving Averages

ma:<period>,<column>

Gets the period-period simple moving average on column named column.

SMA is often confused between simple moving average and smoothed moving average.

So stock-pandas will use ma for simple moving average and smma for smoothed moving average.

• period int (required)
• column? enum<'open'|'high'|'low'|'close'>='close' Which column should the calculation based on. Defaults to 'close'

# which is equivalent to `stock['ma:5,close']`
stock['ma:5']

stock['ma:10,open']

ema, Exponential Moving Average

ema:<period>,<column>

Gets the Exponential Moving Average, also known as the Exponential Weighted Moving Average.

The arguments of this command is the same as ma.

macd, Moving Average Convergence Divergence

macd:<fast_period>,<slow_period>
macd.signal:<fast_period>,<slow_period>,<signal_period>
macd.histogram:<fast_period>,<slow_period>,<signal_period>

• fast_period? int=12 fast period (short period). Defaults to 12.
• slow_period? int=26 slow period (long period). Defaults to 26
• signal_period? int=9 signal period. Defaults to 9

# macd
stock['macd']
stock['macd.dif']

# macd signal band, which is a shortcut for stock['macd.signal']
stock['macd.s']
stock['macd.signal']
stock['macd.dea']

# macd histogram band, which is equivalent to stock['macd.h']
stock['macd.histogram']
stock['macd.h']
stock['macd.macd']

boll, BOLLinger bands

boll:<period>,<column>
boll.upper:<period>,<times>,<column>
boll.lower:<period>,<times>,<column>

• period? int=20
• times? int=2
• column? str='close'

# boll
stock['boll']

# bollinger upper band, a shortcut for stock['boll.upper']
stock['boll.u']
stock['boll.upper]

# bollinger lower band, which is equivalent to stock['boll.l']
stock['boll.lower']
stock['boll.l']

rsv, Raw Stochastic Value

rsv:<period>

Calculates the raw stochastic value which is often used to calculate KDJ

kdj, stochastic oscillator

kdj.k:<param_k>
kdj.d:<param_d>
kdj.j:<param_j>

• param_k? int=9
• param_d? int=9
• param_j? int=9

# The k series of KDJ 999
stock['kdj.k']

# The KDJ serieses of with parameters 9, 3, and 3
stock[['kdj.k', 'kdj.d:3', 'kdj.j:3']]

rsi, Relative Strength Index

rsi:<period>

Calculates the N-period RSI (Relative Strength Index)

• period int The period to calculate RSI. period should be an int which is larger than 1

bbi, Bull and Bear Index

bbi:<a>,<b>,<c>,<d>

Calculates indicator BBI (Bull and Bear Index) which is the average of ma:3, ma:6, ma:12, ma:24 by default

• a? int=3
• b? int=6
• c? int=12
• d? int=24

Built-in Commands for Statistics

column

column:<name>

Just gets the series of a column. This command is designed to be used together with an operator to compare with another command or as a parameter of some statistics command.

• name str the name of the column

increase

increase:<on_what>,<repeat>,<step>

Gets a bool-type series each item of which is True if the value of indicator on_what increases in the last period-period.

• on_what str the command name of an indicator.
• repeat? int=1
• direction? 1 | -1 the direction of "increase". -1 means decreasing

For example:

# Which means whether the `ma:20,close` line
# (a.k.a. 20-period simple moving average on column `'close'`)
# has been increasing repeatedly for 3 times (maybe 3 days)
stock['increase:(ma:20,close),3']

# If the close price has been decreasing repeatedly for 5 times (maybe 5 days)
stock['increase:close,5,-1']

style

style:<style>

Gets a bool-type series whether the candlestick of a period is of style style

• style 'bullish' | 'bearish'

stock['style:bullish']

repeat

repeat:(<bool_directive>),<repeat>

The repeat command first gets the result of directive bool_directive, and detect whether True is repeated for repeat times

• bool_directive str the directive which should returns a series of bools. PAY ATTENTION, that the directive should be wrapped with parantheses as a parameter.
• repeat? int=1 which should be larger than 0

# Whether the bullish candlestick repeats for 3 periods (maybe 3 days)
stock['repeat:(style:bullish),3']

Operators

left operator right

Operator: /

whether left crosses through right from the down side of right to the upper side which we call it as "cross up".

Operator: \

whether left crosses down right.

# Which we call them __dead crosses__
stock['macd \\ macd.signal']

PAY ATTENTION, in the example above, we should escape the backslash, so we've got double backslashes '\\'

Operator: ><

whether left crosses right, either up or down.

Operator: < | <= | == | >= | >

For a certain record of the same time, whether the value of left is less than / less than or equal to / equal to / larger than or equal to / larger than the value of right.

Errors

from stock_pandas import (
    DirectiveSyntaxError,
    DirectiveValueError
)

DirectiveSyntaxError

Raises if there is a syntax error in the given directive.

stock['''
repeat
    :
        (
            column:close >> boll.upper
        ),
        5
''']

DirectiveSyntaxError might print some messages like this:

File "<string>", line 5, column 26

   repeat
       :
           (
>              column:close >> boll.upper
           ),
           5

                            ^
DirectiveSyntaxError: ">>" is an invalid operator

DirectiveValueError

Raises if

• there is an unknown command name
• something is wrong about the command arguments
• etc.

---

Advanced Sections