🏗️ feat(strategy): optimize portfolio 🏗️ (#247)

* progress

* add test

* try robin-stocks branch and python 3.10

* Revert "try robin-stocks branch and python 3.10"

This reverts commit 99e557d.

* restore robin-stocks branch install

* try again

* one more time

* update robinhood

* use ltc for tests for 24hr market

* missed one

* add optimize portfolio test

* fix crypto time_in_force

* fix optimize port test

* increase order size for test

* dont wait if test

* try this

README.md

@@ -144,9 +144,9 @@ timeframe = '1y'
 
 df = md.get_ohlc(symbol=symbol, timeframe=timeframe)
 
-holding = hist.buy_and_hold(df[C.CLOSE])
+holding = hist.from_holding(df[C.CLOSE])
 signals = hist.get_optimal_signals(df[C.CLOSE])
-my_strat = hist.create_portfolio(df[C.CLOSE], signals)
+my_strat = hist.from_signals(df[C.CLOSE], signals)
 
 metrics = [
     'Total Return [%]', 'Benchmark Return [%]',

encrypted/optimize_portfolio.py.encrypted

@@ -1 +1 @@
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hyperdrive/Constants.py

@@ -237,6 +237,7 @@ def get_orders_path(self):
     def get_new_orders_path(self, provider):
         return os.path.join(
             DATA_DIR,
+            'orders',
             f'{provider}.csv'
         )
 

hyperdrive/Exchange.py

@@ -67,7 +67,9 @@ def create_order(self, symbol, side, notional):
             'side': side.lower(),
             'type': 'market',
             'notional': str(notional),
-            'time_in_force': 'day'
+            'time_in_force': (
+                'gtc' if symbol in C.ALPC_CRYPTO_SYMBOLS else 'day'
+            )
         }
         return self.make_request('POST', 'orders', payload)
 

hyperdrive/History.py

@@ -17,6 +17,8 @@
 from sklearn.metrics import classification_report
 from imblearn.over_sampling import SMOTE
 from Calculus import Calculator
+from collections.abc import Callable
+import Constants as C
 
 
 class Historian:
@@ -26,19 +28,86 @@ def __init__(self):
     # add fx to perform calculations on columns
     # takes calc.fx, df, and column names as args, fx args
 
-    def buy_and_hold(self, close, init_cash=1000):
+    def from_holding(self, close, init_cash=1000):
         # returns a portfolio based on buy and hold strategy
         portfolio = vbt.Portfolio.from_holding(
             close, init_cash=init_cash, freq='D')
         return portfolio
 
-    def create_portfolio(self, close, signals, init_cash=1000, fee=0):
+    def from_signals(self, close, signals, init_cash=1000, fee=0):
         # returns a portfolio based on signals
         portfolio = vbt.Portfolio.from_signals(
             close, signals, ~signals, init_cash=init_cash, freq='D', fees=fee
         )
         return portfolio
 
+    def optimize_portfolio(
+            self,
+            close: pd.DataFrame,
+            indicator: Callable,
+            top_n: int,
+            period: str,
+            init_cash: float,
+            **kwargs: dict[str, any]
+    ) -> vbt.Portfolio:
+        if C.CLOSE in close.columns:
+            close = close.set_index(C.CLOSE)
+        signals = close.apply(indicator, **kwargs)
+        close = close.dropna()
+        positions = pd.DataFrame(
+            0, index=close.index, columns=close.columns)
+        holdings = {"cash": init_cash}
+        prev_period = None
+        prev_symbols = set()
+        for day in close.index:
+            curr_period = getattr(day, period)
+            # if is first of the period
+            if prev_period != curr_period:
+                # Rank symbols by indicator and select top_n
+                top_symbols = set(signals.loc[day].nlargest(top_n).index)
+                # Sell old positions for the top symbols
+                minus = prev_symbols.difference(top_symbols)
+                for symbol in minus:
+                    size = holdings[symbol]
+                    positions.loc[day, symbol] = - size
+                    holdings["cash"] += close.loc[day][symbol] * size
+                    del holdings[symbol]
+                # Buy new positions for the top symbols
+                plus = top_symbols.difference(prev_symbols)
+                notional = holdings["cash"] / len(plus)
+                for symbol in plus:
+                    size = notional / close.loc[day][symbol]
+                    positions.loc[day, symbol] = size
+                    holdings[symbol] = size
+                    holdings["cash"] -= notional
+                # Update prev values
+                prev_period = curr_period
+                prev_symbols = top_symbols
+
+        # Forward fill positions to maintain holdings
+        positions = positions.ffill().fillna(0)
+
+        # Convert to orders format
+        portfolio = vbt.Portfolio.from_orders(
+            close=close,
+            size=positions,
+            freq='D',
+            init_cash=0,
+            group_by=True
+        )
+        return portfolio
+
+    def from_orders(
+            self,
+            close: pd.DataFrame,
+            size: pd.DataFrame,
+            fee: float = 0
+    ) -> vbt.Portfolio:
+        portfolio = vbt.Portfolio.from_orders(
+            close, size, freq='D', fees=fee, init_cash=0, group_by=True
+        )
+        return portfolio
+
     def fill(self, arr, method='ffill', type='bool'):
         # forward fills or nearest fills an array
         df = pd.DataFrame(arr)

requirements.txt

@@ -1,16 +1,16 @@
-python-dotenv == 1.0.0
+python-dotenv == 1.0.1
 pandas == 1.5.3
-robin-stocks == 3.3.0
-boto3 == 1.26.165
+git+https://github.com/bhyman67/robin_stocks_bh67.git@0d4574fb11ba4f1ff0acc8046174924bfd7ec532
+boto3 == 1.36.5
 polygon-api-client == 1.10.1
-pytz == 2023.3
+pytz == 2024.2
 vectorbt == 0.25.4
 scipy == 1.11.1
 scikit-learn == 0.24.2
 auto-sklearn == 0.15.0
 cryptography == 41.0.1
 ta == 0.10.2
-python-binance == 1.0.17
+python-binance == 1.0.27
 imbalanced-learn == 0.8.1
 icosphere == 0.1.3
 pynisher == 0.6.4

scripts/update_api.py

@@ -49,15 +49,15 @@ def create_portfolio_preview(close, signals, invert):
 
     holding_signals = np.full(len(signals), not invert)
 
-    holding_pf = hist.create_portfolio(close, holding_signals, init_cash)
+    holding_pf = hist.from_signals(close, holding_signals, init_cash)
     if C.PREF_EXCHANGE == C.BINANCE:
         fee = C.BINANCE_FEE
     else:
         base = C.KRAKEN_SYMBOLS['BTC']
         quote = C.KRAKEN_SYMBOLS['USD']
         pair = kr.create_pair(base, quote)
         fee = kr.get_fee(pair) / 100
-    hyper_pf = hist.create_portfolio(
+    hyper_pf = hist.from_signals(
         close, ~signals if invert else signals, init_cash, fee)
 
     holding_values = holding_pf.value()

test/test_Exchange.py

@@ -33,8 +33,8 @@ def test_get_positions(self):
 
     def test_close_position(self):
         positions = alpc.get_positions()
-        if any([position['symbol'] == 'QQQ' for position in positions]):
-            order = alpc.close_position('QQQ')
+        if any([position['symbol'] == 'LTC/USD' for position in positions]):
+            order = alpc.close_position('LTC/USD')
             assert 'id' in order
 
     def test_get_order(self):
@@ -48,9 +48,9 @@ def test_get_account(self):
     def test_create_order(self):
         positions = alpc.get_positions()
         side = 'buy'
-        if 'QQQ' in [position['symbol'] for position in positions]:
+        if 'LTC/USD' in [position['symbol'] for position in positions]:
             side = 'sell'
-        order = alpc.create_order('QQQ', side, 1)
+        order = alpc.create_order('LTC/USD', side, 10)
         assert 'id' in order
 
 

test/test_History.py

@@ -3,6 +3,7 @@
 import pandas as pd
 sys.path.append('hyperdrive')
 from History import Historian  # noqa autopep8
+import Constants as C  # noqa autopep8
 
 
 hist = Historian()
@@ -27,14 +28,35 @@
 X = pd.DataFrame({'i': data, 'j': data})
 y = np.array([True] * majority + [False] * minority)
 
+orders_index = pd.to_datetime(
+    pd.Series(['2025-01-01', '2025-01-02'], name=C.TIME))
+orders_close = pd.DataFrame({
+    'AAPL': [200, 100],
+    'META': [25, 50]
+}, index=orders_index)
+
 
 class TestHistorian:
-    def test_buy_and_hold(self):
-        stats = hist.buy_and_hold(close).stats()
+    def test_from_holding(self):
+        stats = hist.from_holding(close).stats()
+        assert 'Sortino Ratio' in stats
+
+    def test_from_signals(self):
+        stats = hist.from_signals(close, test_ffill).stats()
+        assert 'Sortino Ratio' in stats
+
+    def test_from_orders(self):
+        size = pd.DataFrame({
+            'AAPL': [1, 0],
+            'META': [0, 1]
+        }, index=orders_index)
+        stats = hist.from_orders(orders_close, size).stats()
         assert 'Sortino Ratio' in stats
 
-    def test_create_portfolio(self):
-        stats = hist.create_portfolio(close, test_ffill).stats()
+    def test_optimize_portfolio(self):
+        indicator = pd.Series.diff
+        stats = hist.optimize_portfolio(
+            orders_close, indicator, 1, 'day', 225).stats()
         assert 'Sortino Ratio' in stats
 
     def test_fill(self):