sma example

asset_backtester/backtester.py

@@ -1,6 +1,6 @@
 import pandas as pd
 import pyprind
-
+import numpy as np
 from .portfolio import Portfolio
 
 
@@ -27,7 +27,7 @@ class Backtest:
     def data(self, data):
         self._data = data
 
-    def run(self, initial_capital=1_000_000, periods=1, sma_months=None):
+    def run(self, initial_capital=1_000_000, periods=1, sma_days=None):
         """Runs a backtest and returns a dataframe with the daily balance"""
         assert self._data is not None
         assert self._portfolio is not None
@@ -36,9 +36,9 @@ class Backtest:
         self.current_cash = initial_capital
         self.inventory = pd.DataFrame(columns=['symbol', 'cost', 'qty'])
         self.balance = pd.DataFrame()
-        if sma_months:
-            self._data.sma(sma_months)
-        
+        if sma_days:
+            self._data.sma(sma_days)
+
         data_iterator = self._data.iter_dates()
 
         first_day = self._data['date'].min()
@@ -55,11 +55,12 @@ class Backtest:
                                     index=[self._data.start_date - pd.Timedelta(1, unit='day')])
 
         for date, data in data_iterator:
-            if date == first_day: 
-                self._rebalance_portfolio(data)
+
+            if date == first_day:
+                self._rebalance_portfolio(data, sma_days)
             self._update_balance(date, data)
             if date in rebalancing_days:
-                self._rebalance_portfolio(data)
+                self._rebalance_portfolio(data, sma_days)
 
             bar.update()
 
@@ -68,23 +69,29 @@ class Backtest:
 
         return self.balance
 
-    def _rebalance_portfolio(self, data):
+    def _rebalance_portfolio(self, data, sma_days):
         """Rebalances the portfolio so that the total money is allocated according to the given percentages"""
         money_total = self.current_cash + self.current_capital
-
         for asset in self._portfolio.assets:
             query = '{} == "{}"'.format(self.schema['symbol'], asset.symbol)
-
             asset_current = data.query(query)
+
             asset_price = asset_current[self.schema['adjClose']].values[0]
 
-            qty = (money_total * asset.percentage) // asset_price
+            if sma_days is not None:
+                if asset_current['sma'].values[0] < asset_price:
+                    qty = (money_total * asset.percentage) // asset_price
+                else:
+                    qty = 0
+
+            else:
+                qty = (money_total * asset.percentage) // asset_price
+
             inventory_entry = self.inventory.query(query)
             self.inventory.drop(inventory_entry.index, inplace=True)
             updated_asset = pd.Series([asset.symbol, asset_price, qty])
             updated_asset.index = self.inventory.columns
             self.inventory = self.inventory.append(updated_asset, ignore_index=True)
-
         # Update current cash
         invested_capital = sum(self.inventory['cost'] * self.inventory['qty'])
         self.current_cash = money_total - invested_capital
@@ -112,5 +119,3 @@ class Backtest:
             'capital': money_total,
         }, name=date)
         self.balance = self.balance.append(row)
-
-

asset_backtester/charts.py

@@ -63,17 +63,16 @@ def monthly_returns_heatmap(report):
     return chart
 
 
-def historical_values(data_sma, data_symbol, asset_name):
+def sma_graph(data):
 
-    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
+    price_chart = alt.Chart(
+        data,
+        width=700,
+        height=350,
+    ).mark_line().encode(x='date:T', y=alt.Y('adjClose:Q'), color='symbol:N', opacity=alt.value(0.3))
+    sma_chart = alt.Chart(
+        data,
+        width=700,
+        height=350,
+    ).mark_line(strokeDash=[1, 1]).encode(x='date:T', y=alt.Y('sma:Q'), color='symbol:N')
+    return price_chart + sma_chart

asset_backtester/datahandler/historical_asset_data.py

@@ -70,7 +70,10 @@ class HistoricalAssetData:
         """Returns default schema for Historical Asset Data"""
         schema = Schema.canonical()
         return schema
-    def sma(self,months):
+
+    def sma(self, months):
         sma = self._data.groupby('symbol').rolling(months)['adjClose'].mean()
         sma = sma.reset_index('symbol').sort_index()
+        sma = sma.fillna(0)
         self._data['sma'] = sma['adjClose']
+        self.schema.update({'sma': 'sma'})