PyPI 官网下载 | margot-0.9.tar.gz

[](https://pypi.org/project/margot/)   [](https://github.com/pymargot/margot/blob/master/LICENSE) [](https://travis-ci.org/github/pymargot/margot) [](https://margot.readthedocs.io/en/latest/?badge=latest) [](https://codecov.io/gh/pymargot/margot) # What is margot? Margot is a library of components that may be used together or separately. The first major component; *margot.data* is now available for public preview. It should be considered an early-beta. It works, but may still have sharp edges. # What is margot data? Margot data makes it super easy to create neat and tidy Pandas dataframes for time-series analysis. Margot data aims to manage data collection, caching, cleaning, feature generation, management and persistence using a clean, declarative API. If you've ever used Django you'll find this approach similar to the Django ORM. ## Columns The heart of any time-series dataframe is the original data. Margot can retrieve time-series data from external sources (currently AlphaVantage). To add a time- series from an original source, such as "closing_price" or "volume", we declare a *Column*: e.g. Let's get closing_price and volume from AlphaVantage: adjusted_close = av.Column(time_series='adjusted_close') daily_volume = av.Column(time_series='volume') ## Features Columns are useful, but we usually want to derive new time-series from them, such as "log_returns" or "SMA20". Margot does this for you; we've called these derived time-series, *Features*. simple_returns = finance.SimpleReturns(column='adjusted_close') log_returns = finance.LogReturns(column='adjusted_close') sma20 = finance.SimpleMovingAverage(column='adjusted_close', window=20) Features can be piled on top of one another. For example, to create a time-series of realised volatility based on log_returns with a lookback of 30 trading days, simply add the following feature: realised_vol = finance.RealisedVolatility(column='log_returns', window=30) Margot includes many common financial Features, and we'll be adding more soon. It's also very easy to add your own. ## Symbols Often, you want to make a dataframe combining a number of columns and features. Margot makes this very easy by providing the Symbol class e.g. class MyEquity(Symbol): adjusted_close = av.Column(time_series='adjusted_close') log_returns = finance.LogReturns(column='adjusted_close') realised_vol = finance.RealisedVolatility(column='log_returns', window=30) upper_band = finance.UpperBollingerBand(column='adjusted_close', window=20, width=2.0) sma20 = finance.SimpleMovingAverage(column='adjusted_close', window=20) lower_band = finance.LowerBollingerBand(column='adjusted_close', window=20, width=2.0) spy = MyEquity(symbol='SPY') ## MargotDataFrames You usually you want to look at more than one symbol. That's where MargotDataFrames come in. MargotDataFrames combine multiple Symbols with dataframe-wide Features and Ratios. For example: class MyEnsemble(MargotDataFrame): spy = MyEquity(symbol='SPY', trading_calendar='NYSE') iwm = MyEquity(symbol='IWM', trading_calendar='NYSE') spy_iwm_ratio = Ratio(numerator=spy.adjusted_close, denominator=iwm.adjusted_close, label='spy_iwm_ratio') my_df = MyEnsemble().to_pandas() The above code creates a Pandas DataFrame of two equities, and an additional feature that calculates a time-series of the ratio of their respective adjusted close prices. ## Margot's other parts **not yet released.** Margot aims to provide a simple framework for writing and backtesting trading signal generation algorithms using margot.data. Results from margot's trading algorithms can be analysed with pyfolio. # Getting Started pip install margot Next you need to make sure you have a couple of important environment variables set:: export ALPHAVANTAGE_API_KEY=YOUR_API_KEY export DATA_CACHE=PATH_TO_FOLDER_TO_STORE_HDF5_FILES Once you've done that, try running the code in the [notebook](notebook.margot.data). # Status This is still an early stage software project, and should not be used for live trading just yet. # Documentation For examples see the [notebook](notebook.margot.data). The main documentation is at [readthedocs](https://margot.readthedocs.io/en/latest/). # Contributing Feel free to make a pull request or chat about your idea first using [issues](https://github.com/atkinson/margot/issues). Dependencies are kept to a minimum. Generally if there's a way to do something in the standard library (or numpy / Pandas), let's do it that way rather than add another library. # License Margot is licensed for use under Apache 2.0. For details see [the License](https://github.com/atkinson/margot/blob/master/LICENSE).