Compatibility with Backtesting.py¤

While Minitrade is largely compatible with Backtesting.py for single asset strategy, some breaking changes still exist to support multi-asset strategies and to simplify usage.

Differences¤

Here are a list of things that are incompatible:

The data input for Backtest should be a dataframe with 2-level column index. For example,

$ print(self.data)

           AAPL                              GOOG 
           Open  High  Low   Close Volume    Open  High  Low   Close Volume
Date          
2018-01-02 40.39 40.90 40.18 40.89 102223600 52.42 53.35 52.26 53.25 24752000
2018-01-03 40.95 41.43 40.82 40.88 118071600 53.22 54.31 53.16 54.12 28604000
2018-01-04 40.95 41.18 40.85 41.07 89738400 54.40 54.68 54.20 54.32 20092000
2018-01-05 41.17 41.63 41.08 41.54 94640000 54.70 55.21 54.60 55.11 25582000
2018-01-08 41.38 41.68 41.28 41.38 82271200 55.11 55.56 55.08 55.35 20952000

For single asset, data, as a single level column DataFrame, still works. Since asset name is not specified, it's default to "Asset".

$ print(self.data)

           Open  High  Low   Close Volume 
Date          
2018-01-02 40.39 40.90 40.18 40.89 102223600 
2018-01-03 40.95 41.43 40.82 40.88 118071600 
2018-01-04 40.95 41.18 40.85 41.07 89738400 
2018-01-05 41.17 41.63 41.08 41.54 94640000 
2018-01-08 41.38 41.68 41.28 41.38 82271200

Minitrade expects Volume data to be always avaiable. data should be consisted of OHLCV.

Calling Strategy.next() starts on the first bar when all data and indicators become available, in contrast to on the 2nd bar as implemented in the original library. This will likely change backtest result.
Strategy.position is no longer a property, but a function of signature
```
def position(ticker=None):
```
ticker can be omitted in single asset strategies. self.position should be changed to self.position() in strategy code.
Strategy.trades is no longer a property, but a function of signature
```
def trades(ticker=None):
```
ticker can be omitted in single asset strategies. self.trades should be changed to self.trades() in strategy code.
Trade.value is no longer the absolute value of the trade. It's a signed value that can be negative for short trade.
Strategy.orders returns a plain List[Order]. If self.orders.cancel() is used with a semantic meaning of "Cancel all non-contingent (i.e. SL/TP) orders.", it should be replaced with the following explicitly.
```
for order in self.orders:
    if not order.is_contingent:
        order.cancel()
```
Pandas DataFrame and Series have richer APIs to work with, and therefore, are preferred over Numpy arrays when defining indicators in Strategy.init(). Strategy.I() returns Pandas objects. Strategy._indicators holds a list of Pandas objects rather than Numpy arrays. Variables derived from an indicator are no longer automatically added as indicators. Explicitly wrap in I() to define new indicators. For example,
```
self.sma = self.I(SMA, self.data.Close, 10)             # defines an indicator
self.not_a_indicator = np.cumsum(self.sma * 5 + 1)      # not an indicator
self.a_indicator = self.I(np.cumsum(self.sma * 5 + 1))  # another indicator
```
Strategy.I() takes both function and value arguments. With self.data.ta accessor, it's easier to precompute the indicator value then wrap it in I() than to present as a function call. For example, the following is equivalent, but the former is visually simpler.
```
self.sma = self.I(self.data.ta.sma(self.n_sma))         #1
self.sma = self.I(ta.SMA, self.data.Close, self.n_sma)  #2
```
Since indexing of Numpy array is much faster than that of Pandas objects, indicators in Strategy.next() context are returned as Numpy arrays by default. Use .df to access the Pandas value, either as DataFrame or Series, of the indicator. It's the caller's reponsibility to keep track of which exact type should be returned. .s accessor is also available but only as a syntax suger to return a Series. If the actual data is a DataFrame, .s throws a ValueError.

Comparing indicators directly is no longer supported. Be explicit of what you want to compare, for example,

if self.data.Close > self.sma:          # no longer work
if self.data.Close[-1] > self.sma[-1]:  # use indexing explicitly

Similarly, use indicators directly in a boolean context is not longer supported.

if self.doji:                              # no longer work
if bool(self.doji[-1]):                 # use indexing and conversion explicitly

Backtest now has an extra argument fail_fast default to True, which means backtest will abort whenever an error occurs, e.g. cash is not enough to cover intended orders. This is to detect issues early in a live trading environment. If it's not desired in backtesting, set it to False.
There are some changes to the default values for plotting. plot_volume and superimpose are False by default. Minitrade doesn't try to guess where to plot the indicators. So to overlay the indicators on the main chart, set overlay=True explicitly.

Examples¤

The following notebooks from Backtesting.py are adapted to show how to make them work with Minitrade. Only the code part is changed, the text content may or may not apply to Minitrade.