Convenient script for trading with python.

Overview

quick_trade

Downloads Downloads wakatime

image

Dependencies:
 ├──ta by Darío López Padial (Bukosabino   https://github.com/bukosabino/ta)
 ├──plotly (https://github.com/plotly/plotly.py)
 ├──pandas (https://github.com/pandas-dev/pandas)
 ├──numpy (https://github.com/numpy/numpy)
 ├──tqdm (https://github.com/tqdm/tqdm)
 └──ccxt (https://github.com/ccxt/ccxt)

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Installation:

Quick install:

$ pip3 install quick-trade

For development:

$ git clone https://github.com/VladKochetov007/quick_trade.git
$ pip3 install -r quick_trade/requirements.txt
$ cd quick_trade
$ python3 setup.py install
$ cd ..

Customize your strategy!

import quick_trade.trading_sys as qtr
from quick_trade import brokers
from quick_trade.plots import QuickTradeGraph, make_figure
import yfinance as yf
import ccxt


class MyTrader(qtr.Trader):
    def strategy_sell_and_hold(self):
        ret = []
        for i in self.df['Close'].values:
            ret.append(qtr.utils.SELL)
        self.returns = ret
        self.set_credit_leverages(1.0)
        self.set_open_stop_and_take()
        return ret


a = MyTrader('MSFT/USD', df=yf.download('MSFT', start='2019-01-01'))
a.connect_graph(QuickTradeGraph(make_figure()))
a.set_client(brokers.TradingClient(ccxt.ftx()))
a.strategy_sell_and_hold()
a.backtest()

Find the best strategy!

import quick_trade.trading_sys as qtr
import ccxt
from quick_trade.quick_trade_tuner import *
from quick_trade.brokers import TradingClient


class Test(qtr.ExampleStrategies):  # examples of strategies
    def strategy_supertrend1(self, plot: bool = False, *st_args, **st_kwargs):
        self.strategy_supertrend(plot=plot, *st_args, **st_kwargs)
        self.set_credit_leverages()
        self.convert_signal()
        return self.returns

    def macd(self, histogram=False, **kwargs):
        if not histogram:
            self.strategy_macd(**kwargs)
        else:
            self.strategy_macd_histogram_diff(**kwargs)
        self.set_credit_leverages()
        self.convert_signal()
        return self.returns

    def psar(self, **kwargs):
        self.strategy_parabolic_SAR(plot=False, **kwargs)
        self.set_credit_leverages()
        self.convert_signal()
        return self.returns


params = {
    'strategy_supertrend1':
        [
            {
                'multiplier': Linspace(0.5, 22, 5)
            }
        ],
    'macd':
        [
            {
                'slow': Linspace(10, 100, 3),
                'fast': Linspace(3, 60, 3),
                'histogram': Choise([False, True])
            }
        ],
    'psar':
        [
            {
                'step': 0.01,
                'max_step': 0.1
            },
            {
                'step': 0.02,
                'max_step': 0.2
            }
        ]

}

tuner = QuickTradeTuner(
    TradingClient(ccxt.binance()),
    ['BTC/USDT', 'OMG/USDT', 'XRP/USDT'],
    ['15m', '5m'],
    [1000, 700, 800, 500],
    params
)

tuner.tune(Test)
print(tuner.sort_tunes())
tuner.save_tunes('quick-trade-tunes.json')  # save tunes as JSON

You can also set rules for arranging arguments for each strategy by using _RULES_ and kwargs to access the values of the arguments:

kwargs["mid"] > kwargs["fast"]' ) ], } ">
params = {
    'strategy_3_sma':
        [
            dict(
                plot=False,
                slow=Choise([2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597]),
                fast=Choise([2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597]),
                mid=Choise([2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597]),
                _RULES_='kwargs["slow"] > kwargs["mid"] > kwargs["fast"]'
            )
        ],
}

User's code example (backtest)

from quick_trade import brokers
from quick_trade import trading_sys as qtr
from quick_trade.plots import *
import ccxt
from numpy import inf


client = brokers.TradingClient(ccxt.binance())
df = client.get_data_historical('BTC/USDT', '15m', 1000)
trader = qtr.ExampleStrategies('BTC/USDT', df=df, interval='15m')
trader.set_client(client)
trader.connect_graph(QuickTradeGraph(make_figure(height=731, width=1440, row_heights=[10, 5, 2])))
trader.strategy_2_sma(55, 21)
trader.backtest(deposit=1000, commission=0.075, bet=inf)  # backtest on one pair

Output plotly chart:

image

Output print

losses: 12
trades: 20
profits: 8
mean year percentage profit: 215.1878652911773%
winrate: 40.0%
mean deviation: 2.917382949881604%
Sharpe ratio: 0.02203412259055281
Sortino ratio: 0.02774402450236864
calmar ratio: 21.321078596349782
max drawdown: 10.092728860725552%

Run strategy

Use the strategy on real moneys. YES, IT'S FULLY AUTOMATED!

import datetime
from quick_trade.trading_sys import ExampleStrategies
from quick_trade.brokers import TradingClient
from quick_trade.plots import QuickTradeGraph, make_figure
import ccxt

ticker = 'MATIC/USDT'

start_time = datetime.datetime(2021,  # year
                               6,  # month
                               24,  # day

                               5,  # hour
                               16,  # minute
                               57)  # second (Leave a few seconds to download data from the exchange)


class MyTrade(ExampleStrategies):
    def strategy(self):
        self.strategy_supertrend(multiplier=2, length=1, plot=False)
        self.convert_signal()
        self.set_credit_leverages(1)
        self.sl_tp_adder(10)
        return self.returns


keys = {'apiKey': 'your api key',
        'secret': 'your secret key'}
client = TradingClient(ccxt.binance(config=keys))  # or any other exchange

trader = MyTrade(ticker=ticker,
                 interval='1m',
                 df=client.get_data_historical(ticker, limit=10),
                 trading_on_client=True)
fig = make_figure()
graph = QuickTradeGraph(figure=fig)
trader.connect_graph(graph)
trader.set_client(client)

trader.realtime_trading(
    strategy=trader.strategy,
    start_time=start_time,
    ticker=ticker,
    limit=100,
    wait_sl_tp_checking=5
)

image

Additional Resources

Old documentation (V3 doc): https://vladkochetov007.github.io/quick_trade.github.io

License

Creative Commons License
quick_trade by Vladyslav Kochetov is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Comments
  • Bump ccxt from 2.0.103 to 2.4.40

    Bump ccxt from 2.0.103 to 2.4.40

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  • Bump requests from 2.25.0 to 2.25.1

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    Changelog

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    2.25.1 (2020-12-16)

    Bugfixes

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    Dependencies

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    Commits
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    • b223291 Merge pull request #5688 from dan-blanchard/patch-1
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  • Bump tensorflow from 2.3.1 to 2.4.0

    Bump tensorflow from 2.3.1 to 2.4.0

    Bumps tensorflow from 2.3.1 to 2.4.0.

    Release notes

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    TensorFlow 2.4.0

    Release 2.4.0

    Major Features and Improvements

    • tf.distribute introduces experimental support for asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy API. Please see the tutorial to learn more.

    • MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. Some of the major improvements involve handling peer failure and many bug fixes. Please check out the detailed tutorial on Multi-worker training with Keras.

    • Introduces experimental support for a new module named tf.experimental.numpy which is a NumPy-compatible API for writing TF programs. See the detailed guide to learn more. Additional details below.

    • Adds Support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.

    • A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.

    • Keras mixed precision API tf.keras.mixed_precision is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. Please see below for additional details.

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    • TFLite Profiler for Android is available. See the detailed guide to learn more.

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    Breaking Changes

    • TF Core:

      • Certain float32 ops run in lower precsion on Ampere based GPUs, including matmuls and convolutions, due to the use of TensorFloat-32. Specifically, inputs to such ops are rounded from 23 bits of precision to 10 bits of precision. This is unlikely to cause issues in practice for deep learning models. In some cases, TensorFloat-32 is also used for complex64 ops. TensorFloat-32 can be disabled by running tf.config.experimental.enable_tensor_float_32_execution(False).
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      • XLA:CPU and XLA:GPU devices are no longer registered by default. Use TF_XLA_FLAGS=--tf_xla_enable_xla_devices if you really need them, but this flag will eventually be removed in subsequent releases.
    • tf.keras:

      • The steps_per_execution argument in model.compile() is no longer experimental; if you were passing experimental_steps_per_execution, rename it to steps_per_execution in your code. This argument controls the number of batches to run during each tf.function call when calling model.fit(). Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.
      • A major refactoring of the internals of the Keras Functional API may affect code that is relying on certain internal details:
        • Code that uses isinstance(x, tf.Tensor) instead of tf.is_tensor when checking Keras symbolic inputs/outputs should switch to using tf.is_tensor.
        • Code that is overly dependent on the exact names attached to symbolic tensors (e.g. assumes there will be ":0" at the end of the inputs, treats names as unique identifiers instead of using tensor.ref(), etc.) may break.
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        • Code that tries directly getting gradients with respect to symbolic Keras inputs/outputs. Use GradientTape on the actual Tensors passed to the already-constructed model instead.
        • Code that requires very tricky shape manipulation via converted op layers in order to work, where the Keras symbolic shape inference proves insufficient.
        • Code that tries manually walking a tf.keras.Model layer by layer and assumes layers only ever have one positional argument. This assumption doesn't hold true before TF 2.4 either, but is more likely to cause issues now.

    ... (truncated)

    Changelog

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      • Certain float32 ops run in lower precsion on Ampere based GPUs, including

    ... (truncated)

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  • Bump iexfinance from 0.4.3 to 0.5.0

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  • Bump plotly from 4.13.0 to 4.14.1

    Bump plotly from 4.13.0 to 4.14.1

    Bumps plotly from 4.13.0 to 4.14.1.

    Release notes

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    v4.14.1

    Updated

    • Updated Plotly.js to version 1.58.2. See the plotly.js CHANGELOG for more information. These changes are reflected in the auto-generated plotly.graph_objects module. Notable changes include:
      • fixes for new ticklabelposition attribute
      • fixes for a regression related to treemaps in the previous version

    v4.14.0

    Added

    • px.imshow now supports facet_col and animation_frame arguments for visualizing 3-d and 4-d images 2746
    • px.defaults now supports color_discrete_map, symbol_map, line_dash_map, labels and category_orders as well as a .reset() method 2957

    Fixed

    • axes will now auto-type numeric strings as categorical data rather than linear in the default templates 2951

    Updated

    • Updated Plotly.js to version 1.58.1. See the plotly.js CHANGELOG for more information. These changes are reflected in the auto-generated plotly.graph_objects module. Notable changes include:
      • a new ticklabelposition attribute to enable positioning tick labels inside the plotting area
      • better support for scaleanchor and matches on cartesian axes for matched square subplots
      • a new autotypenumbers attribute which is now set to strict in the default templates
      • various fixes relating to automargins for small figures
    Changelog

    Sourced from plotly's changelog.

    [4.14.1] - 2020-12-09

    Updated

    • Updated Plotly.js to version 1.58.2. See the plotly.js CHANGELOG for more information. These changes are reflected in the auto-generated plotly.graph_objects module. Notable changes include:
      • fixes for new ticklabelposition attribute
      • fixes for a regression related to treemaps in the previous version

    [4.14.0] - 2020-12-07

    Added

    • px.imshow now supports facet_col and animation_frame arguments for visualizing 3-d and 4-d images 2746
    • px.defaults now supports color_discrete_map, symbol_map, line_dash_map, labels and category_orders as well as a .reset() method 2957

    Fixed

    • axes will now auto-type numeric strings as categorical data rather than linear in the default templates 2951

    Updated

    • Updated Plotly.js to version 1.58.1. See the plotly.js CHANGELOG for more information. These changes are reflected in the auto-generated plotly.graph_objects module. Notable changes include:
      • a new ticklabelposition attribute to enable positioning tick labels inside the plotting area
      • better support for scaleanchor and matches on cartesian axes for matched square subplots
      • a new autotypenumbers attribute which is now set to strict in the default templates
      • various fixes relating to automargins for small figures
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  • Bump pandas from 1.1.4 to 1.1.5

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    Pandas 1.1.5

    This is a minor bug-fix release in the 1.1.x series and includes some regression fixes and bug fixes. We recommend that all users upgrade to this version.

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    Commits
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    • 039aaba Backport PR #38272: BUG: DataFrame.apply with axis=1 and EA dtype (#38295)
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  • Bump ccxt from 2.0.103 to 2.4.79

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    Bumps ccxt from 2.0.103 to 2.4.79.

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  • Bump ccxt from 2.0.103 to 2.4.71

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  • Bump ccxt from 2.0.103 to 2.4.70

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  • Bump ccxt from 2.0.103 to 2.4.61

    Bump ccxt from 2.0.103 to 2.4.61

    Bumps ccxt from 2.0.103 to 2.4.61.

    Commits
    • 2efbed6 2.4.61
    • f8a1630 Merge pull request #16214 from ttodua/cancel-order-php-example
    • 9f7d895 Merge pull request #16218 from frosty00/markets_conflict_derived_classes
    • f18c2a5 fix a bug
    • 5bdd8d9 remove bitstamp markets_by_id incorrect usage
    • 2af8a76 remove market_ids_bitget
    • ec8948e remove deprecated markets_by_id usage
    • 7841843 remove deprecated markets_by_id usage
    • 8d143d0 remove deprecated markets_by_id usage
    • 32c2900 remove markets_by_id from derived classes
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  • Bump ccxt from 2.0.103 to 2.4.59

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  • Bump ccxt from 2.0.103 to 2.5.10

    Bump ccxt from 2.0.103 to 2.5.10

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  • Bump numpy from 1.23.4 to 1.24.1

    Bump numpy from 1.23.4 to 1.24.1

    Bumps numpy from 1.23.4 to 1.24.1.

    Release notes

    Sourced from numpy's releases.

    v1.24.1

    NumPy 1.24.1 Release Notes

    NumPy 1.24.1 is a maintenance release that fixes bugs and regressions discovered after the 1.24.0 release. The Python versions supported by this release are 3.8-3.11.

    Contributors

    A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

    • Andrew Nelson
    • Ben Greiner +
    • Charles Harris
    • Clément Robert
    • Matteo Raso
    • Matti Picus
    • Melissa Weber Mendonça
    • Miles Cranmer
    • Ralf Gommers
    • Rohit Goswami
    • Sayed Adel
    • Sebastian Berg

    Pull requests merged

    A total of 18 pull requests were merged for this release.

    • #22820: BLD: add workaround in setup.py for newer setuptools
    • #22830: BLD: CIRRUS_TAG redux
    • #22831: DOC: fix a couple typos in 1.23 notes
    • #22832: BUG: Fix refcounting errors found using pytest-leaks
    • #22834: BUG, SIMD: Fix invalid value encountered in several ufuncs
    • #22837: TST: ignore more np.distutils.log imports
    • #22839: BUG: Do not use getdata() in np.ma.masked_invalid
    • #22847: BUG: Ensure correct behavior for rows ending in delimiter in...
    • #22848: BUG, SIMD: Fix the bitmask of the boolean comparison
    • #22857: BLD: Help raspian arm + clang 13 about __builtin_mul_overflow
    • #22858: API: Ensure a full mask is returned for masked_invalid
    • #22866: BUG: Polynomials now copy properly (#22669)
    • #22867: BUG, SIMD: Fix memory overlap in ufunc comparison loops
    • #22868: BUG: Fortify string casts against floating point warnings
    • #22875: TST: Ignore nan-warnings in randomized out tests
    • #22883: MAINT: restore npymath implementations needed for freebsd
    • #22884: BUG: Fix integer overflow in in1d for mixed integer dtypes #22877
    • #22887: BUG: Use whole file for encoding checks with charset_normalizer.

    Checksums

    ... (truncated)

    Commits
    • a28f4f2 Merge pull request #22888 from charris/prepare-1.24.1-release
    • f8fea39 REL: Prepare for the NumPY 1.24.1 release.
    • 6f491e0 Merge pull request #22887 from charris/backport-22872
    • 48f5fe4 BUG: Use whole file for encoding checks with charset_normalizer [f2py] (#22...
    • 0f3484a Merge pull request #22883 from charris/backport-22882
    • 002c60d Merge pull request #22884 from charris/backport-22878
    • 38ef9ce BUG: Fix integer overflow in in1d for mixed integer dtypes #22877 (#22878)
    • bb00c68 MAINT: restore npymath implementations needed for freebsd
    • 64e09c3 Merge pull request #22875 from charris/backport-22869
    • dc7bac6 TST: Ignore nan-warnings in randomized out tests
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  • Bump pandas from 1.5.1 to 1.5.2

    Bump pandas from 1.5.1 to 1.5.2

    Bumps pandas from 1.5.1 to 1.5.2.

    Release notes

    Sourced from pandas's releases.

    Pandas 1.5.2

    This is a patch release in the 1.5.x series and includes some regression and bug fixes. We recommend that all users upgrade to this version.

    See the full whatsnew for a list of all the changes.

    The release will be available on the defaults and conda-forge channels:

    conda install pandas
    

    Or via PyPI:

    python3 -m pip install --upgrade pandas
    

    Please report any issues with the release on the pandas issue tracker.

    Thanks to all the contributors who made this release possible.

    Commits
    • 8dab54d RLS: 1.5.2
    • d78c5e6 Backport PR #49806 on branch 1.5.x (DOC: Update what's new notes for 1.5.2 re...
    • 98c6139 Backport PR #49579 on Branch 1.5.x (BUG: Behaviour change in 1.5.0 when using...
    • 9196f8d Backport PR STYLE enable pylint: method-cache-max-size-none (#49784)
    • 8c4b559 Backport PR #49776 on branch 1.5.x (REGR: arithmetic ops recursion error with...
    • 1616fb3 Backport PR Revert "Add color and size to arguments (#44856)" (#49752)
    • 6f8e174 Backport PR #49720 on branch 1.5.x (Suppress spurious warning) (#49726)
    • 63a91d0 Backport PR #49676 on branch 1.5.x (REGR: Remove groupby's getattribute f...
    • 136271a Backport PR #49615 on branch 1.5.x (REGR: Better warning in pivot_table when ...
    • c9252cf Backport PR #49614 on branch 1.5.x (CI: Updating website sync to new server) ...
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  • Bump ta from 0.10.1 to 0.10.2

    Bump ta from 0.10.1 to 0.10.2

    Bumps ta from 0.10.1 to 0.10.2.

    Changelog

    Sourced from ta's changelog.

    Date Version Comment
    2022/08/23 0.10.2 Fixing bug on KAMA indicator bukosabino/ta#303
    2022/04/16 0.10.1 Fixing future warning bukosabino/ta#293
    2022/04/16 0.10.0 Customize Keltner Channel ATRs multiplier
    2022/01/09 0.9.0 Fixing wrong name version
    2022/01/09 0.8.2 Bumping pandas and numpy because of a external vulnerability issue
    2022/01/09 0.8.1 Fixing bug on name columns (PVO - PPO)
    2021/11/18 0.8.0 Add option to get just the most efficient indicators (vectorized), also solved some security dependency
    2020/11/27 0.7.0 Cleaning code (prospector, black) bukosabino/ta#209
    2020/11/17 0.6.1 Fixing Wrapper bukosabino/ta#204
    2020/11/09 0.6.0 Adding new indicators (Ulcer Index, Weighted Moving Average, Schaff Trend Cycle, Stochastic RSI, Percentage Price Oscillator, Percentage Volume Oscillator) bukosabino/ta#167; Update wrapper bukosabino/ta#166
    2020/05/12 0.5.25 fixing bug: min_periods when fillna=False bukosabino/ta#158
    2020/05/11 0.5.24 Adding extra methods for IchimokuIndicator bukosabino/ta#156
    2020/05/10 0.5.23 Fixing bug when dataset with timestamp as index bukosabino/ta#154
    2020/05/04 0.5.22 1. Keltner Channel: adding tests; adding n atr input parametr; fixing some minor bug; adding unittests for adx bukosabino/ta#148
    2. Refactor tests code and speed up the tests bukosabino/ta#149
    3. Refactor tests code bukosabino/ta#150
    4. Donchian Channel: Fixing bug bukosabino/ta#151
    2020/05/03 0.5.21 Donchian Channel: adding tests; adding some useful functions; fixing some minor bug bukosabino/ta#147 bukosabino/ta#133
    2020/04/20 0.5.20 remove fstring compatibility for python versions < 3.6
    2020/04/20 0.5.19 adding fstring compatibility for python versions < 3.6 bukosabino/ta#141
    2020/04/16 0.5.18 Adding VWAP indicator bukosabino/ta#130
    2020/03/29 0.5.17 Keltner Channels improvements and adding SMA Indicator bukosabino/ta#135
    2020/03/21 0.5.15 Keltner Channels improvements and volume indicators isolation. Based on: bukosabino/ta#131
    2020/03/12 0.5.13 fixing bug: hard-coded values. Based on: bukosabino/ta#114 and bukosabino/ta#115
    2020/03/11 0.5.12 Improve Bollinger Bands Indicator - BandWidth normalization and adding BandPercentage. Based on: bukosabino/ta#121
    Commits

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