Merge pull request #3 from bsdz/plotly_strat_runner

Plotly strat runner

.github/workflows/branch.yml

@@ -38,7 +38,7 @@ jobs:
       - name: Run isort
         run: poetry run isort . --check-only --profile black
       - name: Run docformatter
-        run: poetry run docformatter . --recursive --check
+        run: poetry run docformatter . --recursive --check --diff --black --exclude _unittest_numpy_extensions.py
 #       - name: Run flake8
 #         run: poetry run flake8 .
 #       - name: Run bandit

README.md

@@ -111,5 +111,5 @@ Before commit run following format commands in project folder:
 ```bash
 poetry run black .
 poetry run isort . --profile black
-poetry run docformatter . --recursive --in-place
+poetry run docformatter . --recursive --in-place --black --exclude _unittest_numpy_extensions.py
 ```

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "yabte"
-version = "0.3.5"
+version = "0.3.6"
 description = "Yet another backtesting engine"
 authors = ["Blair Azzopardi <blairuk@gmail.com>"]
 license = "MIT"
@@ -39,6 +39,7 @@ optional = true
 
 [tool.poetry.group.notebooks.dependencies]
 matplotlib = "^3.6.2"
+plotly = "^5.10.0"
 ipykernel = "^6.20.2"
 pyfeng = "^0.2.5"
 nbconvert = "^7.2.9"

yabte/backtest/asset.py

@@ -65,8 +65,8 @@ def check_and_fix_data(self, data: pd.DataFrame) -> pd.DataFrame:
 @mypyc_attr(allow_interpreted_subclasses=True)
 @dataclass(kw_only=True)
 class Asset(AssetBase):
-    """Assets whose price history is represented by High, Low, Open, Close and
-    Volume fields."""
+    """Assets whose price history is represented by High, Low, Open, Close and Volume
+    fields."""
 
     @property
     def fields_available_at_open(self) -> Sequence[str]:

yabte/backtest/order.py

@@ -74,8 +74,8 @@ class OrderBase:
     key: Optional[str] = None
     """Unique key for this order.
 
-    If a key is set then only the newest order with this key is kept.
-    Older orders with the same key will be removed.
+    If a key is set then only the newest order with this key is kept. Older orders with
+    the same key will be removed.
     """
 
     def __post_init__(self):
@@ -94,8 +94,7 @@ def _book_trades(self, trades):
     def post_complete(self, trades: List[Trade]):
         """Called after and with trades that have been successfully booked.
 
-        It can append new orders to suborders for execution in the
-        following timestep.
+        It can append new orders to suborders for execution in the following timestep.
         """
         pass
 
@@ -149,8 +148,8 @@ def _calc_quantity_price(self, day_data, asset_map) -> Tuple[Decimal, Decimal]:
     def pre_execute_check(
         self, ts: pd.Timestamp, trade_price: Decimal
     ) -> Optional[OrderStatus]:
-        """Called with the current timestep and calculated trade price before
-        the trade is executed.
+        """Called with the current timestep and calculated trade price before the trade
+        is executed.
 
         If it returns `None`, the trade is executed as normal. It can
         return `OrderStatus.CANCELLED` to indicate the trade should be

yabte/backtest/strategy.py

@@ -49,8 +49,7 @@ def sort_by_priority(self):
     def remove_duplicate_keys(self) -> List[OrderBase]:
         """Remove older orders with same key.
 
-        Returns a list of orders than were removed with status set to
-        REPLACED.
+        Returns a list of orders than were removed with status set to REPLACED.
         """
         removed = []
         cntr = Counter(o.key for o in self.deque if o.key is not None)
@@ -210,11 +209,11 @@ class StrategyRunner:
     """
 
     data: pd.DataFrame = field()
-    """Dataframe of price data including columns High, Low, Open, Close, Volume
-    for each asset.
+    """Dataframe of price data including columns High, Low, Open, Close, Volume for each
+    asset.
 
-    Both asset name and field make a multiindex column. The index should
-    consist of order pandas timestamps.
+    Both asset name and field make a multiindex column. The index should consist of
+    order pandas timestamps.
     """
 
     assets: List[Asset]
@@ -232,8 +231,7 @@ class StrategyRunner:
     books: List[Book] = field(default_factory=list)
     """Books available to strategies.
 
-    If not supplied will be populated with single book named 'Main'
-    denominated in USD.
+    If not supplied will be populated with single book named 'Main' denominated in USD.
     """
 
     @property

yabte/tests/_unittest_numpy_extensions.py

@@ -6,13 +6,13 @@
 
 
 class NumpyTestCase(unittest.TestCase):
-    """Specialized TestCase which includes numpy test assertion functions and
-    maps them from assert_func_name to self.numpyAssertFuncName.
+    """Specialized TestCase which includes numpy test assertion functions and maps them
+    from assert_func_name to self.numpyAssertFuncName.
 
     class NumpyTest(NumpyTestCase):
-    def test_allclose_example(self):
-        a1 = np.array([1.,2.,3.])
-        self.numpyAssertAllclose(a1, np.array([1.,2.,3.1]))
+        def test_allclose_example(self):
+            a1 = np.array([1.,2.,3.])
+            self.numpyAssertAllclose(a1, np.array([1.,2.,3.1]))
     """
 
 

yabte/utilities/plot/matplotlib/strategy_runner.py

@@ -13,10 +13,9 @@
 def plot_strategy_runner(sr: StrategyRunner, settings: dict[str, Any] | None = None):
     """Display the results of a strategy run using matplotlib.
 
-    Plots a grid of charts with each column representing a book and rows
-    representing each asset's price series along with long/short
-    positioning and volumne series. A bottom row shows the value of each
-    book as a price series.
+    Plots a grid of charts with each column representing a book and rows representing
+    each asset's price series along with long/short positioning and volumne series. A
+    bottom row shows the value of each book as a price series.
     """
     default_settings = {
         "candle_body_width": 0.8,

yabte/utilities/plot/plotly/strategy_runner.py

@@ -0,0 +1,175 @@
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+from ....backtest import StrategyRunner
+
+
+def plot_strategy_runner(sr: StrategyRunner, settings: dict[str, Any] | None = None):
+    """Display the results of a strategy run using plotly.
+
+    Plots a grid of charts with each column representing a book and rows representing
+    each asset's price series along with long/short positioning and volumne series. A
+    bottom row shows the value of each book as a price series.
+    """
+    default_settings: dict[str, Any] = {}
+
+    if isinstance(settings, dict):
+        default_settings = default_settings | settings
+    s = pd.Series(default_settings, dtype=object)
+
+    traded_assets = [
+        a for a in sr.assets if a.name in sr.transaction_history.asset_name.unique()
+    ]
+
+    dpi = 100
+    col_width = 8 * dpi
+    row_unit_height = 3 * dpi
+    ncols = len(sr.books)
+    nrows = 1 + len(traded_assets)
+
+    subplot_titles = [a.name for a in (traded_assets)] + ["Book Value"]
+    row_heights = [10 for a in (traded_assets)] + [2]
+    specs = [[{"secondary_y": True} for c in range(ncols)] for r in range(nrows)]
+
+    fig = make_subplots(
+        rows=nrows,
+        cols=ncols,
+        shared_xaxes=True,
+        subplot_titles=subplot_titles,
+        row_heights=row_heights,
+        start_cell="top-left",
+        specs=specs,
+        vertical_spacing=0.05,
+    )
+
+    for col, book in enumerate(sr.books, start=1):
+        for row, asset in enumerate(traded_assets, start=1):
+            prices = sr.data[asset.data_label]
+
+            fig.add_trace(
+                go.Candlestick(
+                    x=prices.index,
+                    open=prices.Open,
+                    high=prices.High,
+                    low=prices.Low,
+                    close=prices.Close,
+                ),
+                row=row,
+                col=col,
+                secondary_y=True,
+            )
+
+            fig.add_trace(
+                go.Bar(
+                    x=prices.index,
+                    y=prices.Volume,
+                    marker_color="lightgrey",
+                ),
+                row=row,
+                col=col,
+                secondary_y=False,
+            )
+
+            fig.update_xaxes(rangeslider_visible=False, row=row, col=col)
+            fig.update_yaxes(
+                title=asset.denom, secondary_y=True, showgrid=True, row=row, col=col
+            )
+            fig.update_yaxes(
+                title="Volume", secondary_y=False, showgrid=False, row=row, col=col
+            )
+
+            # add some range selector buttons
+            fig.update_xaxes(
+                rangeselector=dict(
+                    buttons=[
+                        dict(count=1, label="1m", step="month", stepmode="backward"),
+                        dict(count=6, label="6m", step="month", stepmode="backward"),
+                        dict(count=1, label="YTD", step="year", stepmode="todate"),
+                        dict(count=1, label="1y", step="year", stepmode="backward"),
+                        dict(step="all"),
+                    ]
+                ),
+                row=1,
+                col=col,
+            )
+
+            trans_hist = sr.transaction_history.query(
+                "asset_name==@asset.name and book==@book.name"
+            )
+            pos_hist = (
+                trans_hist.groupby("ts")
+                .agg(
+                    quantity=("quantity", np.sum),
+                    labels=(
+                        "order_label",
+                        lambda L: " ".join(l for l in L if l is not None),
+                    ),
+                )
+                .reindex(prices.index)
+            )
+
+            shorts = prices[pos_hist.eval("quantity < 0")][["Low"]].join(
+                pos_hist.labels
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=shorts.index,
+                    y=shorts.Low,
+                    customdata=shorts.labels,
+                    mode="markers",
+                    marker_symbol="arrow-down",
+                    marker_color="red",
+                    marker_size=10,
+                    hovertemplate="%{x}<br>%{y}<br>%{customdata}<extra></extra>",
+                ),
+                row=row,
+                col=col,
+                secondary_y=True,
+            )
+
+            longs = prices[pos_hist.eval("quantity > 0")][["High"]].join(
+                pos_hist.labels
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=longs.index,
+                    y=longs.High,
+                    customdata=shorts.labels,
+                    mode="markers",
+                    marker_symbol="arrow-up",
+                    marker_color="green",
+                    marker_size=10,
+                    hovertemplate="%{x}<br>%{y}<br>%{customdata}<extra></extra>",
+                ),
+                row=row,
+                col=col,
+                secondary_y=True,
+            )
+
+    row = nrows
+    bh = sr.book_history.loc[:, book.name]
+    fig.add_trace(
+        go.Scatter(
+            x=bh.index,
+            y=bh.total,
+        ),
+        row=row,
+        col=col,
+    )
+
+    fig.update_xaxes(rangeslider_visible=True, row=row, col=col)
+    fig.update_yaxes(title=book.denom, showgrid=True, row=row, col=col)
+    fig.update_xaxes(title="Date", row=row, col=col)
+
+    fig.update_layout(
+        height=nrows * row_unit_height,
+        width=ncols * col_width,
+        showlegend=False,
+        title_text="Strategy Runner Report",
+    )
+
+    return fig

yabte/utilities/portopt/hierarchical_risk_parity.py

@@ -57,8 +57,8 @@ def _getRecBipart(cov, sortIx):
 
 
 def hrp(corr: pd.DataFrame, sigma: np.ndarray) -> np.ndarray:
-    """Calculate weights using hierarchical risk parity and scipy's
-    linkage/to_tree functions."""
+    """Calculate weights using hierarchical risk parity and scipy's linkage/to_tree
+    functions."""
     cov = np.diag(sigma) @ corr @ np.diag(sigma)
     cov.index, cov.columns = corr.index, corr.columns
     rho = corr.values

yabte/utilities/portopt/minimum_variance.py

@@ -33,8 +33,8 @@ def minimum_variance(Sigma: np.ndarray, mu: np.ndarray, r: float) -> np.ndarray:
 
 
 def minimum_variance_numeric(Sigma: np.ndarray, mu: np.ndarray, r: float) -> np.ndarray:
-    """Calculate weights using Lagrangian multipliers and numeric solution
-    (using scipy's root function)."""
+    """Calculate weights using Lagrangian multipliers and numeric solution (using
+    scipy's root function)."""
     m = len(mu)
     ones = np.ones(m)
 
@@ -52,8 +52,8 @@ def minimum_variance_numeric(Sigma: np.ndarray, mu: np.ndarray, r: float) -> np.
 def minimum_variance_numeric_slsqp(
     Sigma: np.ndarray, mu: np.ndarray, r: float
 ) -> np.ndarray:
-    """Calculate weights using Lagrangian multipliers and numeric solution
-    (using scipy's minimize function)."""
+    """Calculate weights using Lagrangian multipliers and numeric solution (using
+    scipy's minimize function)."""
     from scipy.optimize import minimize
 
     m = len(mu)