REF: Use Py3.6 f-strings

Fixes: #161
Closes: #166

Thanks @Alex-CodeLab

Squashed commit of the following:

commit 363641d
Merge: aad278c 773a85f
Author: Kernc <[email protected]>
Date:   Thu Nov 12 21:35:41 2020 +0100

    Merge branch 'master'

commit aad278c
Author: Kernc <[email protected]>
Date:   Thu Nov 12 20:42:30 2020 +0100

    add some more f-strings where useful

commit 4fd6ac5
Author: Kernc <[email protected]>
Date:   Thu Nov 12 20:24:08 2020 +0100

    address comments

commit 5ab6cb3
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 19:40:57 2020 +0100

    Update _plotting.py

commit 00bb39e
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 19:40:02 2020 +0100

    Update backtesting.py

commit 6c633bc
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 19:35:55 2020 +0100

    Update backtesting.py

commit 732068a
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 19:26:55 2020 +0100

    Update backtesting.py

commit 21fed39
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 19:19:25 2020 +0100

    Update backtesting.py

commit e0b929e
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 18:49:30 2020 +0100

    Update backtesting.py

commit 696669a
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 18:02:10 2020 +0100

    Update backtesting.py

commit e38b25f
Author: Alex ⚡ <[email protected]>
Date:   Sat Oct 31 17:42:43 2020 +0100

    Update backtesting.py

commit 6bd7492
Author: alex <[email protected]>
Date:   Sat Oct 31 16:29:38 2020 +0100

    Refactor f-string formating.

Author: kernc

backtesting/_plotting.py

@@ -101,8 +101,8 @@ def _maybe_resample_data(resample_rule, df, indicators, equity_data, trades):
         FREQS = ('1T', '5T', '10T', '15T', '30T', '1H', '2H', '4H', '8H', '1D', '1W', '1M')
         freq = next((f for f in FREQS[from_index:]
                      if len(df.resample(f)) <= _MAX_CANDLES), FREQS[-1])
-        warnings.warn("Data contains too many candlesticks to plot; downsampling to {!r}. "
-                      "See `Backtest.plot(resample=...)`".format(freq))
+        warnings.warn(f"Data contains too many candlesticks to plot; downsampling to {freq!r}. "
+                      "See `Backtest.plot(resample=...)`")
 
     from .lib import OHLCV_AGG, TRADES_AGG, _EQUITY_AGG
     df = df.resample(freq, label='right').agg(OHLCV_AGG).dropna()
@@ -354,7 +354,7 @@ def _plot_equity_section():
         dd_timedelta_label = df['datetime'].iloc[int(round(dd_end))] - df['datetime'].iloc[dd_start]
         fig.line([dd_start, dd_end], equity.iloc[dd_start],
                  line_color='red', line_width=2,
-                 legend_label='Max Dd Dur. ({})'.format(dd_timedelta_label)
+                 legend_label=f'Max Dd Dur. ({dd_timedelta_label})'
                  .replace(' 00:00:00', '')
                  .replace('(0 days ', '('))
 
@@ -424,8 +424,8 @@ def _plot_superimposed_ohlc():
                               millisecond='S').get(time_resolution))
         if not resample_rule:
             warnings.warn(
-                "'Can't superimpose OHLC data with rule '{}' (index datetime resolution: '{}'). "
-                "Skipping.".format(resample_rule, time_resolution),
+                f"'Can't superimpose OHLC data with rule '{resample_rule}'"
+                f"(index datetime resolution: '{time_resolution}'). Skipping.",
                 stacklevel=4)
             return
 
@@ -469,7 +469,7 @@ def _plot_ohlc_trades():
         trade_source.add(trades[['EntryPrice', 'ExitPrice']].values.tolist(), 'position_lines_ys')
         fig_ohlc.multi_line(xs='position_lines_xs', ys='position_lines_ys',
                             source=trade_source, line_color=trades_cmap,
-                            legend_label='Trades ({})'.format(len(trades)),
+                            legend_label=f'Trades ({len(trades)})',
                             line_width=8, line_alpha=1, line_dash='dotted')
 
     def _plot_indicators():
@@ -478,7 +478,7 @@ def _plot_indicators():
         def _too_many_dims(value):
             assert value.ndim >= 2
             if value.ndim > 2:
-                warnings.warn("Can't plot indicators with >2D ('{}')".format(value.name),
+                warnings.warn(f"Can't plot indicators with >2D ('{value.name}')",
                               stacklevel=5)
                 return True
             return False
@@ -517,11 +517,11 @@ def __eq__(self, other):
             legend_label = LegendStr(value.name)
             for j, arr in enumerate(value, 1):
                 color = next(colors)
-                source_name = '{}_{}_{}'.format(legend_label, i, j)
+                source_name = f'{legend_label}_{i}_{j}'
                 if arr.dtype == bool:
                     arr = arr.astype(int)
                 source.add(arr, source_name)
-                tooltips.append('@{{{}}}{{0,0.0[0000]}}'.format(source_name))
+                tooltips.append(f'@{{{source_name}}}{{0,0.0[0000]}}')
                 if is_overlay:
                     ohlc_extreme_values[source_name] = arr
                     if is_scatter:

backtesting/_util.py

@@ -111,7 +111,7 @@ def __getattr__(self, item):
         try:
             return self.__get_array(item)
         except KeyError:
-            raise AttributeError("Column '{}' not in data".format(item)) from None
+            raise AttributeError(f"Column '{item}' not in data") from None
 
     def _set_length(self, i):
         self.__i = i
@@ -125,9 +125,9 @@ def _update(self):
 
     def __repr__(self):
         i = min(self.__i, len(self.__df) - 1)
-        return '<Data i={} ({}) {}>'.format(i, self.__arrays['__index'][i],
-                                            ', '.join('{}={}'.format(k, v)
-                                                      for k, v in self.__df.iloc[i].items()))
+        index = self.__arrays['__index'][i]
+        items = ', '.join(f'{k}={v}' for k, v in self.__df.iloc[i].items())
+        return f'<Data i={i} ({index}) {items}>'
 
     def __len__(self):
         return self.__i

backtesting/backtesting.py

@@ -57,19 +57,19 @@ def __repr__(self):
         return '<Strategy ' + str(self) + '>'
 
     def __str__(self):
-        params = ','.join('{}={}'.format(*p) for p in zip(self._params.keys(),
-                                                          map(_as_str, self._params.values())))
+        params = ','.join(f'{i[0]}={i[1]}' for i in zip(self._params.keys(),
+                                                        map(_as_str, self._params.values())))
         if params:
             params = '(' + params + ')'
-        return '{}{}'.format(self.__class__.__name__, params)
+        return f'{self.__class__.__name__}{params}'
 
     def _check_params(self, params):
         for k, v in params.items():
             if not hasattr(self, k):
                 raise AttributeError(
-                    "Strategy '{}' is missing parameter '{}'. Strategy class "
-                    "should define parameters as class variables before they "
-                    "can be optimized or run with.".format(self.__class__.__name__, k))
+                    f"Strategy '{self.__class__.__name__}' is missing parameter '{k}'."
+                    "Strategy class should define parameters as class variables before they "
+                    "can be optimized or run with.")
             setattr(self, k, v)
         return params
 
@@ -116,15 +116,15 @@ def init():
         if name is None:
             params = ','.join(filter(None, map(_as_str, chain(args, kwargs.values()))))
             func_name = _as_str(func)
-            name = ('{}({})' if params else '{}').format(func_name, params)
+            name = (f'{func_name}({params})' if params else f'{func_name}')
         else:
             name = name.format(*map(_as_str, args),
                                **dict(zip(kwargs.keys(), map(_as_str, kwargs.values()))))
 
         try:
             value = func(*args, **kwargs)
         except Exception as e:
-            raise RuntimeError('Indicator "{}" errored with exception: {}'.format(name, e))
+            raise RuntimeError(f'Indicator "{name}" errored with exception: {e}')
 
         if isinstance(value, pd.DataFrame):
             value = value.values.T
@@ -140,8 +140,8 @@ def init():
         if not is_arraylike or not 1 <= value.ndim <= 2 or value.shape[-1] != len(self._data.Close):
             raise ValueError(
                 'Indicators must return (optionally a tuple of) numpy.arrays of same '
-                'length as `data` (data shape: {}; indicator "{}" shape: {}, returned value: {})'
-                .format(self._data.Close.shape, name, getattr(value, 'shape', ''), value))
+                f'length as `data` (data shape: {self._data.Close.shape}; indicator "{name}"'
+                f'shape: {getattr(value, "shape" , "")}, returned value: {value})')
 
         if plot and overlay is None and np.issubdtype(value.dtype, np.number):
             x = value / self._data.Close
@@ -288,10 +288,10 @@ def __getattr__(self, item):
         removed_attrs = ('entry', 'set_entry', 'is_long', 'is_short',
                          'sl', 'tp', 'set_sl', 'set_tp')
         if item in removed_attrs:
-            raise AttributeError('Strategy.orders.{} were removed in Backtesting 0.2.0. '
-                                 'Use `Order` API instead. See docs.'
-                                 .format('/.'.join(removed_attrs)))
-        raise AttributeError("'tuple' object has no attribute {!r}".format(item))
+            raise AttributeError(f'Strategy.orders.{"/.".join(removed_attrs)} were removed in'
+                                 'Backtesting 0.2.0. '
+                                 'Use `Order` API instead. See docs.')
+        raise AttributeError(f"'tuple' object has no attribute {item!r}")
 
 
 class Position:
@@ -346,7 +346,7 @@ def close(self, portion: float = 1.):
             trade.close(portion)
 
     def __repr__(self):
-        return '<Position: {} ({} trades)>'.format(self.size, len(self.__broker.trades))
+        return f'<Position: {self.size} ({len(self.__broker.trades)} trades)>'
 
 
 class _OutOfMoneyError(Exception):
@@ -386,11 +386,11 @@ def __init__(self, broker: '_Broker',
 
     def _replace(self, **kwargs):
         for k, v in kwargs.items():
-            setattr(self, '_{}__{}'.format(self.__class__.__qualname__, k), v)
+            setattr(self, f'_{self.__class__.__qualname__}__{k}', v)
         return self
 
     def __repr__(self):
-        return '<Order {}>'.format(', '.join('{}={}'.format(param, round(value, 5))
+        return '<Order {}>'.format(', '.join(f'{param}={round(value, 5)}'
                                              for param, value in (
                                                  ('size', self.__size),
                                                  ('limit', self.__limit_price),
@@ -513,13 +513,12 @@ def __init__(self, broker: '_Broker', size: int, entry_price: float, entry_bar):
         self.__tp_order: Optional[Order] = None
 
     def __repr__(self):
-        return '<Trade size={} time={}-{} price={}-{} pl={:.0f}>'.format(
-            self.__size, self.__entry_bar, self.__exit_bar or '',
-            self.__entry_price, self.__exit_price or '', self.pl)
+        return f'<Trade size={self.__size} time={self.__entry_bar}-{self.__exit_bar or ""} ' \
+               f'price={self.__entry_price}-{self.__exit_price or ""} pl={self.pl:.0f}>'
 
     def _replace(self, **kwargs):
         for k, v in kwargs.items():
-            setattr(self, '_{}__{}'.format(self.__class__.__qualname__, k), v)
+            setattr(self, f'_{self.__class__.__qualname__}__{k}', v)
         return self
 
     def _copy(self, **kwargs):
@@ -647,8 +646,8 @@ def tp(self, price: float):
     def __set_contingent(self, type, price):
         assert type in ('sl', 'tp')
         assert price is None or 0 < price < np.inf
-        attr = '_{}__{}_order'.format(self.__class__.__qualname__, type)
-        order: Order = getattr(self, attr)
+        attr = f'_{self.__class__.__qualname__}__{type}_order'
+        order: Order = getattr(self, attr)  # type: Order
         if order:
             order.cancel()
         if price:
@@ -660,9 +659,9 @@ def __set_contingent(self, type, price):
 class _Broker:
     def __init__(self, *, data, cash, commission, margin,
                  trade_on_close, hedging, exclusive_orders, index):
-        assert 0 < cash, "cash shosuld be >0, is {}".format(cash)
-        assert 0 <= commission < .1, "commission should be between 0-10%, is {}".format(commission)
-        assert 0 < margin <= 1, "margin should be between 0 and 1, is {}".format(margin)
+        assert 0 < cash, f"cash should be >0, is {cash}"
+        assert 0 <= commission < .1, f"commission should be between 0-10%, is {commission}"
+        assert 0 < margin <= 1, f"margin should be between 0 and 1, is {margin}"
         self._data: _Data = data
         self._cash = cash
         self._commission = commission
@@ -678,8 +677,7 @@ def __init__(self, *, data, cash, commission, margin,
         self.closed_trades: List[Trade] = []
 
     def __repr__(self):
-        return '<Broker: {:.0f}{:+.1f} ({} trades)>'.format(
-            self._cash, self.position.pl, len(self.trades))
+        return f'<Broker: {self._cash:.0f}{self.position.pl:+.1f} ({len(self.trades)} trades)>'
 
     def new_order(self,
                   size: float,
@@ -703,12 +701,14 @@ def new_order(self,
 
         if is_long:
             if not (sl or -np.inf) < (limit or stop or adjusted_price) < (tp or np.inf):
-                raise ValueError("Long orders require: SL ({}) < LIMIT ({}) < TP ({})".format(
-                    sl, limit or stop or adjusted_price, tp))
+                raise ValueError(
+                    "Long orders require: "
+                    f"SL ({sl}) < LIMIT ({limit or stop or adjusted_price}) < TP ({tp})")
         else:
             if not (tp or -np.inf) < (limit or stop or adjusted_price) < (sl or np.inf):
-                raise ValueError("Short orders require: TP ({}) < LIMIT ({}) < SL ({})".format(
-                    tp, limit or stop or adjusted_price, sl))
+                raise ValueError(
+                    "Short orders require: "
+                    f"TP ({tp}) < LIMIT ({limit or stop or adjusted_price}) < SL ({sl})")
 
         order = Order(self, size, limit, stop, sl, tp, trade)
         # Put the new order in the order queue,
@@ -1243,8 +1243,8 @@ def _tuple(x):
 
         for k, v in kwargs.items():
             if len(_tuple(v)) == 0:
-                raise ValueError("Optimization variable '{0}' is passed no "
-                                 "optimization values: {0}={1}".format(k, v))
+                raise ValueError(f"Optimization variable '{k}' is passed no "
+                                 f"optimization values: {k}={v}")
 
         class AttrDict(dict):
             def __getattr__(self, item):
@@ -1259,7 +1259,7 @@ def __getattr__(self, item):
             raise ValueError('No admissible parameter combinations to test')
 
         if len(param_combos) > 300:
-            warnings.warn('Searching for best of {} configurations.'.format(len(param_combos)),
+            warnings.warn(f'Searching for best of {len(param_combos)} configurations.',
                           stacklevel=2)
 
         heatmap = pd.Series(np.nan,