sma graph added

asset_backtester/backtester.py

@@ -9,6 +9,7 @@ class Backtest:
         self.schema = schema
         self._portfolio = None
         self._data = None
+        self.data_symbol = None
 
     @property
     def portfolio(self):
@@ -26,8 +27,9 @@ class Backtest:
     @data.setter
     def data(self, data):
         self._data = data
+        self.data_symbol = df_symbol(data)
 
-    def run(self, initial_capital=1_000_000, periods='1'):
+    def run(self, initial_capital=1_000_000, periods='1', sma_months=None):
         """Runs a backtest and returns a dataframe with the daily balance"""
         assert self._data is not None
         assert self._portfolio is not None
@@ -108,3 +110,29 @@ class Backtest:
             'capital': money_total,
         }, name=date)
         self.balance = self.balance.append(row)
+class df_symbol:
+
+    def __init__(self, data, sma_months = None):
+
+        self.columns = data['symbol'].drop_duplicates(keep = 'first')
+
+        cols  = pd.MultiIndex.from_product([self.columns.to_list(),data.columns[1:-1].to_list()]) 
+        df = pd.DataFrame(columns = cols, index =  data['date'].unique())
+
+        for col in cols:
+            symbol = data[data['symbol']==col[0]]
+            symbol = symbol.set_index('date')
+            df[col[0],col[1]] = symbol[col[1]]
+
+        self.data_symbol = df
+
+    def sma(self, sma_days):
+
+        df = pd.DataFrame(columns = self.columns)
+
+        for col in self.columns:
+            df[col] = self.data_symbol[col]['Adj Close'].rolling(sma_days, min_periods = 10).mean()
+        return df
+       
+            
+        

asset_backtester/stats/charts.py

@@ -0,0 +1,80 @@
+"""Generates charts from a portfolio report"""
+
+import altair as alt
+import pandas as pd
+
+def returns_chart(report):
+    # Time interval selector
+    time_interval = alt.selection(type='interval', encodings=['x'])
+
+    # Area plot
+    areas = alt.Chart().mark_area(opacity=0.7).encode(x='index:T',
+                                                      y=alt.Y('accumulated return:Q', axis=alt.Axis(format='%')))
+
+    # Nearest point selector
+    nearest = alt.selection(type='single', nearest=True, on='mouseover', fields=['index'], empty='none')
+
+    points = areas.mark_point().encode(opacity=alt.condition(nearest, alt.value(1), alt.value(0)))
+
+    # Transparent date selector
+    selectors = alt.Chart().mark_point().encode(
+        x='index:T',
+        opacity=alt.value(0),
+    ).add_selection(nearest)
+
+    text = areas.mark_text(
+        align='left', dx=5,
+        dy=-5).encode(text=alt.condition(nearest, 'accumulated return:Q', alt.value(' '), format='.2%'))
+
+    layered = alt.layer(selectors,
+                        points,
+                        text,
+                        areas.encode(
+                            alt.X('index:T', axis=alt.Axis(title='date'), scale=alt.Scale(domain=time_interval))),
+                        width=700,
+                        height=350,
+                        title='Wealth over time')
+
+    lower = areas.properties(width=700, height=70).add_selection(time_interval)
+
+    return alt.vconcat(layered, lower, data=report.reset_index())
+
+
+def returns_histogram(report):
+    bar = alt.Chart(report).mark_bar().encode(x=alt.X('% change:Q',
+                                                      bin=alt.BinParams(maxbins=100),
+                                                      axis=alt.Axis(format='%')),
+                                              y='count():Q')
+    return bar
+
+
+def monthly_returns_heatmap(report):
+    resample = report.resample('M')['capital'].last()
+    monthly_returns = resample.pct_change().reset_index()
+    monthly_returns['capital'].iat[0] = resample.iloc[0] / report.iloc[0]['capital'] - 1
+    monthly_returns.columns = ['date', 'capital']
+
+    chart = alt.Chart(monthly_returns).mark_rect().encode(
+        alt.X('year(date):O', title='Year'), alt.Y('month(date):O', title='Month'),
+        alt.Color('mean(capital)', title='Return', scale=alt.Scale(scheme='redyellowgreen')),
+        alt.Tooltip('mean(capital)', format='.2f')).properties(title='Monthly Returns')
+
+    return chart
+
+def historical_values(data_sma, data_symbol, asset_name):
+   
+    asset_sma = pd.DataFrame(data_sma[asset_name])
+    asset_sma = asset_sma.rename(columns = {asset_name:'value'})
+    asset_sma['id'] = ['sma value']*(len(asset_sma.index))
+    asset_sma = asset_sma.dropna()
+    asset_value = pd.DataFrame(data_symbol[asset_name]['Adj Close'])
+    asset_value= asset_value.rename(columns={'Adj Close' :'value'})
+    asset_value['id'] = ['Adj Close']*(len(asset_value.index))
+
+    asset_value = asset_value.append(asset_sma)
+    asset_value['index'] = asset_value.index
+    plot = alt.Chart(asset_value).mark_line().encode(x='index:T',
+                                                    y=alt.Y('value:Q'),
+                                                    color='id'
+)
+    return plot