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[GH-112] Fix minor errors in readme. #113

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Jan 7, 2022
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[GH-112] Fix minor errors in readme. #113

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91 changes: 44 additions & 47 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ Supported statistics/indicators are:

* change (in percent)
* delta
* permutation (zero based)
* permutation (zero-based)
* log return
* max in range
* min in range
Expand Down Expand Up @@ -61,7 +61,7 @@ Supported statistics/indicators are:

## Compatibility

The build checks the compatibility for the last two major release of python3 and
The build checks the compatibility for the last two major releases of python3 and
the last release of python2.

## License
Expand All @@ -73,14 +73,14 @@ the last release of python2.
### Initialization

`StockDataFrame` works as a wrapper for the `pandas.DataFrame`. You need to
Initialize the `StockDataFrame` with `wrap` or `retype`.
Initialize the `StockDataFrame` with `wrap` or `StockDataFrame.retype`.

``` python
import pandas as pd
from stockstats import StockDataFrame
from stockstats import wrap

df = pd.read_csv('stock.csv')
stock = StockDataFrame.wrap(df)
data = pd.read_csv('stock.csv')
df = wrap(data)
```

Formalize your data. This package takes for granted that your data is sorted by
Expand Down Expand Up @@ -137,56 +137,54 @@ of `pandas.DataFrame` should work the same as before.
#### Retrieve the data with symbol

We allow the user to access the statistics directly with some specified column
name, such as: `kdjk`, `macd`, `rsi`.
name, such as `kdjk`, `macd`, `rsi`.

Note that the value of these columns are calculated the first time you access
them from the data frame. You need to delete those columns first if you want the
lib to re-evaluate the value.
The values of these columns are calculated the first time you access
them from the data frame. Please delete those columns first if you want the
lib to re-evaluate them.

#### Retrieve the Series

If you need the `Series`, you can use `macd = stock['macd']`
or `rsi = stock.get('rsi')`.
Use `macd = stock['macd']` or `rsi = stock.get('rsi')` to retrieve the `Series`.

#### Retrieve the symbol with 2 arguments

For some statistics, we allow the user to supply the column name and the window,
such as: delta, shift, simple moving average, etc. You can use the following
patter to calculate them: `<columnName>_<windowSize>_<statistics>`
Some statistics need the column name and the window size,
such as delta, shift, simple moving average, etc. Use this patter to retrieve
them: `<columnName>_<windowSize>_<statistics>`

Here are some examples for the pattern:
Examples:

* 5 periods simple moving average of the high price: `high_5_sma`
* 10 periods exponential moving average of the close: `close_10_ema`
* 1 period delta of the high price: `high_-1_d`
The `-` symbol stands for looking backwards.
* 1 period delta of the high price: `high_-1_d`.
The minus symbol means looking backward.

#### Retrieve the symbol with 1 arguments
#### Retrieve the symbol with 1 argument

Some statistics allows the user to specify the window but not the column. Use
following patter to specify your window: `<statistics>_<windowSize>`
Some statistics require the window size but not the column name. Use
this patter to specify your window: `<statistics>_<windowSize>`

For example:
Examples:

* 6 periods RSI: `rsi_6`
* 10 periods CCI: `cci_10`
* 13 periods ATR: `atr_13`

Normally, these statistics have default windows.
Check their document for detail.
Some of them have default windows. Check their document for detail.

#### Initialize all indicators with shortcuts

Some indicators, such as: KDJ, BOLL, MFI, have shortcuts. Use `df.init_all()`
to initialize the series of all these indicators.
Some indicators, such as KDJ, BOLL, MFI, have shortcuts. Use `df.init_all()`
to initialize all these indicators.

This operation generates lots of columns. Please use it with caution.

### Statistics/Indicators

Some statistics has configurable parameters. They are class level fields. Change
of these fields are global. And they won't affect the existing results. Removing
existing results to trigger the re-calculation of these columns.
Some statistics have configurable parameters. They are class-level fields. Change
of these fields is global. And they won't affect the existing results. Removing
existing columns so that they will be re-evaluated the next time you access them.

#### Change of the Close

Expand All @@ -198,10 +196,9 @@ Using pattern `<column>_<window>_d` to retrieve the delta between different peri

You can also use `<column>_delta` as a shortcut to `<column>_-1_d`


For example:
Examples:
* `df['close_-1_d']` retrieves the close price delta between current and prev. period.
* `df['close_delta']` is the save as `df['close_-1_d']`
* `df['close_delta']` is the same as `df['close_-1_d']`
* `df['high_2_d']` retrieves the high price delta between current and 2 days later

#### Shift Periods
Expand Down Expand Up @@ -255,11 +252,11 @@ Use `df['log-ret']` to access this column.

#### Count of Non-Zero Value

Count non-zero value of a specific range. It requires a column and a window.
Count non-zero values of a specific range. It requires a column and a window.

Examples:

* count how many typical price are larger than close in the past 10 periods
* Count how many typical prices are larger than close in the past 10 periods

``` python
In [22]: tp = df['middle']
Expand Down Expand Up @@ -358,7 +355,7 @@ Wave trend uses two parameters. You can tune them with

#### SMMA - Smoothed Moving Average

It takes two parameters, column and window.
It requires column and window.

For example, use `df['close_7_smma']` to retrieve the 7 periods smoothed moving
average of the close price.
Expand All @@ -376,7 +373,7 @@ TripleEMA = EMA of EMA of EMA
LastTripleEMA = TripleEMA of the last period
```

It takes two parameters, column and window. By default, the column is `close`,
It requires column and window. By default, the column is `close`,
the window is 12.

Use `StockDataFrame.TRIX_EMA_WINDOW` to change the default window.
Expand Down Expand Up @@ -406,7 +403,7 @@ Examples:

#### [VR - Volume Variation Index](https://help.eaglesmarkets.com/hc/en-us/articles/900002867026-Summary-of-volume-variation-index)

It is the strength index of trading volume.
It is the strength index of the trading volume.

It has a default window of 26. Change it with `StockDataFrame.VR`.

Expand Down Expand Up @@ -442,7 +439,7 @@ Examples:

#### TR - True Range of Trading

TR is a measure of volatility of a High-Low-Close series. It is used for
TR is a measure of the volatility of a High-Low-Close series. It is used for
calculating the ATR.

#### [ATR - Average True Range](https://en.wikipedia.org/wiki/Average_true_range)
Expand Down Expand Up @@ -473,7 +470,7 @@ It has 2 parameters:

#### DMA - Difference of Moving Average

`df['dma']` retreives the difference of 10 periods SMA of the close price and
`df['dma']` retrieves the difference of 10 periods SMA of the close price and
the 50 periods SMA of the close price.

#### [DMI - Directional Movement Index](https://www.investopedia.com/terms/d/dmi.asp)
Expand Down Expand Up @@ -510,7 +507,7 @@ It includes three lines:
The default window is 9. Use `StockDataFrame.KDJ_WINDOW` to change it.
Use `df['kdjk_6']` to retrieve the K series of 6 periods.

KDJ also has two configurable parameter named `StockDataFrame.KDJ_PARAM`.
KDJ also has two configurable parameters named `StockDataFrame.KDJ_PARAM`.
The default value is `(2.0/3.0, 1.0/3.0)`

#### [CR - Energy Index](https://support.futunn.com/en/topic167/?lang=en-us)
Expand Down Expand Up @@ -545,7 +542,7 @@ The default period of the Bollinger Band can be changed with
#### [MACD - Moving Average Convergence Divergence](https://en.wikipedia.org/wiki/MACD)

We use the close price to calculate the MACD lines.
* `df['macd']` is the difference between two exponential moving average.
* `df['macd']` is the difference between two exponential moving averages.
* `df['macds]` is the signal line.
* `df['macdh']` is he histogram line.

Expand Down Expand Up @@ -573,7 +570,7 @@ The period of the signal line can be tuned with

#### [Simple Moving Average](https://www.investopedia.com/terms/m/mean.asp)

Follow the pattern `<columnName>_<window>_sma` to retrieve simple moving average.
Follow the pattern `<columnName>_<window>_sma` to retrieve a simple moving average.

#### [Moving Standard Deviation](https://www.investopedia.com/terms/s/standarddeviation.asp)

Expand Down Expand Up @@ -622,7 +619,7 @@ Examples:
Kaufman's Adaptive Moving Average is designed to account for market noise or
volatility.

It has 2 optional parameter and 2 required parameter
It has 2 optional parameters and 2 required parameters
* fast - optional, the parameter for fast EMA smoothing, default to 5
* slow - optional, the parameter for slow EMA smoothing, default to 34
* column - required, the column to calculate
Expand All @@ -645,9 +642,9 @@ Use the pattern `<A>_xd_<B>` to check when A crosses down B.
Use the pattern `<A>_x_<B>` to check when A crosses B.

Examples:
* `kdjk_x_kdjd` returns a series marks the cross of KDJK and KDJD
* `kdjk_xu_kdjd` returns a series marks where KDJK crosses up KDJD
* `kdjk_xd_kdjd` returns a series marks where KDJD crosses down KDJD
* `kdjk_x_kdjd` returns a series that marks the cross of KDJK and KDJD
* `kdjk_xu_kdjd` returns a series that marks where KDJK crosses up KDJD
* `kdjk_xd_kdjd` returns a series that marks where KDJD crosses down KDJD

## Issues

Expand All @@ -664,4 +661,4 @@ cryptowatch, tradingview, etc.

## Contact author:

- Cedric Zhuang <[email protected]>
* Cedric Zhuang <[email protected]>