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Fixed exit costs being calculated before imputing missing contracts

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Javier Rodríguez Chatruc
2020-01-13 10:40:21 -03:00
parent 89c18c6bd8
commit d1298903fc
2 changed files with 14 additions and 11 deletions
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backtester/demos/data_cleanup.ipynb
+1 -1
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@@ -3125,7 +3125,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.5"
}
},
"nbformat": 4,
backtester/strategy/strategy.py
+13 -10
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@@ -102,8 +102,6 @@ class Strategy:
"""
leg_candidates = [self._exit_candidates(l.direction, inventory[l.name], options) for l in self.legs]
total_costs = sum([l['cost'] for l in leg_candidates])
threshold_exits = self._filter_thresholds(inventory['totals']['cost'], total_costs)
filter_mask = []
for i, leg in enumerate(self.legs):
@@ -118,20 +116,25 @@ class Strategy:
leg_candidates[i].columns = pd.MultiIndex.from_product([["leg_{}".format(i + 1)],
leg_candidates[i].columns])
qtys = inventory['totals']['qty']
dates = [date] * len(inventory)
totals = pd.DataFrame.from_dict({"cost": total_costs, "qty": qtys, "date": dates})
totals.columns = pd.MultiIndex.from_product([["totals"], totals.columns])
leg_candidates.append(totals)
filter_mask = reduce(lambda x, y: x | y, filter_mask)
exits_mask = threshold_exits | filter_mask
candidates = pd.concat(leg_candidates, axis=1)
# If a contract is missing we replace the NaN values with those of the inventory
# except for cost, which we imput as zero.
imputed_inventory = self._imput_missing_data(inventory)
candidates = candidates.fillna(imputed_inventory)
total_costs = sum([candidates[l.name]['cost'] for l in self.legs])
# Append the 'totals' column to candidates
qtys = inventory['totals']['qty']
dates = [date] * len(inventory)
totals = pd.DataFrame.from_dict({"cost": total_costs, "qty": qtys, "date": dates})
totals.columns = pd.MultiIndex.from_product([["totals"], totals.columns])
candidates = pd.concat([candidates, totals], axis=1)
# Compute which contracts need to exit, either because of price thresholds or user exit filters
threshold_exits = self._filter_thresholds(inventory['totals']['cost'], total_costs)
filter_mask = reduce(lambda x, y: x | y, filter_mask)
exits_mask = threshold_exits | filter_mask
exits = candidates[exits_mask]
total_costs = total_costs[exits_mask] * exits['totals']['qty']