Remove for-loop style code from templates and currently submitted str…

data/leaderboard.json

@@ -18,7 +18,7 @@
                 "max_drawdown": 0.2941198645359705,
                 "n_trades": 19,
                 "win_rate": 0.21052631578947367
-            },
+            }
         },
         {
             "author_name": "Ben",

src/core/strategy.py

@@ -43,19 +43,6 @@ def validate(self) -> bool:
             raise ValueError("Strategy must have a description")
         return True
 
-    def process_bar(self, bar: Dict[str, Any]):
-        """
-        Called for each bar (dict with keys: 'time', 'close', 'volume').
-        Should update internal state and decide on signal.
-        """
-        self.current_bar = bar
-
-    def get_signal(self) -> str:
-        """
-        Return 'buy', 'sell', or 'hold'.
-        """
-        return 'hold'
-
     def get_signals(self, df: pd.DataFrame) -> pd.Series:
         """
         Vectorized version of signal generation.

src/strategies/athon_buy-and-hold.py

@@ -14,16 +14,6 @@ def __init__(self, initial_capital=10000):
             strategy_name="Buy and Hold",
             description="Buys Bitcoin at the first bar and holds the position for the entire backtest period."
         )
-        self.has_bought = False
-
-    def process_bar(self, bar):
-        self.current_bar = bar
-
-    def get_signal(self):
-        if not self.has_bought:
-            self.has_bought = True
-            return 'buy'
-        return 'hold'
 
     def get_signals(self, df: pd.DataFrame) -> pd.Series:
         signals = pd.Series('hold', index=df.index)

src/strategies/athon_sma-v1.py

@@ -17,27 +17,6 @@ def __init__(self, initial_capital=10000, fast=20, slow=100):
         self.prices = []
         self.fast = fast
         self.slow = slow
-        self.last_signal = 'hold'
-
-    def process_bar(self, bar):
-        self.current_bar = bar
-        self.prices.append(bar['close'])
-        if len(self.prices) < self.slow:
-            self.last_signal = 'hold'
-            return
-
-        fast_ma = pd.Series(self.prices).rolling(self.fast).mean().iloc[-1]
-        slow_ma = pd.Series(self.prices).rolling(self.slow).mean().iloc[-1]
-
-        if fast_ma > slow_ma and self.position == 0:
-            self.last_signal = 'buy'
-        elif fast_ma < slow_ma and self.position == 1:
-            self.last_signal = 'sell'
-        else:
-            self.last_signal = 'hold'
-
-    def get_signal(self):
-        return self.last_signal
 
     def get_signals(self, df: pd.DataFrame) -> pd.Series:
         fast_ma = df['close'].rolling(self.fast).mean()

src/strategies/ben_sma-v1.py

@@ -15,30 +15,8 @@ def __init__(self, initial_capital=10000, fast=30, slow=120):
             strategy_name="SMA Crossover 1 - f30s120",
             description="Goes long when the 30-period SMA crosses above the 120-period SMA, and exits when it crosses below."
         )
-        self.prices = []
         self.fast = fast
         self.slow = slow
-        self.last_signal = 'hold'
-
-    def process_bar(self, bar):
-        self.current_bar = bar
-        self.prices.append(bar['close'])
-        if len(self.prices) < self.slow:
-            self.last_signal = 'hold'
-            return
-
-        fast_ma = pd.Series(self.prices).rolling(self.fast).mean().iloc[-1]
-        slow_ma = pd.Series(self.prices).rolling(self.slow).mean().iloc[-1]
-
-        if fast_ma > slow_ma and self.position == 0:
-            self.last_signal = 'buy'
-        elif fast_ma < slow_ma and self.position == 1:
-            self.last_signal = 'sell'
-        else:
-            self.last_signal = 'hold'
-
-    def get_signal(self):
-        return self.last_signal
 
     def get_signals(self, df: pd.DataFrame) -> pd.Series:
         fast_ma = df['close'].rolling(self.fast).mean()

src/strategies/lucien_sma-v1.py

@@ -14,30 +14,8 @@ def __init__(self, initial_capital=10000, fast=32, slow=140):
             strategy_name="SMA Crossover 1",
             description="Goes long when the 32-period SMA crosses above the 140-period SMA, and exits when it crosses below."
         )
-        self.prices = []
         self.fast = fast
         self.slow = slow
-        self.last_signal = 'hold'
-
-    def process_bar(self, bar):
-        self.current_bar = bar
-        self.prices.append(bar['close'])
-        if len(self.prices) < self.slow:
-            self.last_signal = 'hold'
-            return
-
-        fast_ma = pd.Series(self.prices).rolling(self.fast).mean().iloc[-1]
-        slow_ma = pd.Series(self.prices).rolling(self.slow).mean().iloc[-1]
-
-        if fast_ma > slow_ma and self.position == 0:
-            self.last_signal = 'buy'
-        elif fast_ma < slow_ma and self.position == 1:
-            self.last_signal = 'sell'
-        else:
-            self.last_signal = 'hold'
-
-    def get_signal(self):
-        return self.last_signal
 
     def get_signals(self, df: pd.DataFrame) -> pd.Series:
         fast_ma = df['close'].rolling(self.fast).mean()

src/strategies/template.py

@@ -19,33 +19,6 @@ def __init__(self, initial_capital=10000):
         # self.lookback_period = 20
         # self.threshold = 0.02
 
-    def process_bar(self, bar):
-        """
-        Process each bar of data.
-        This is where you implement your strategy logic.
-
-        Args:
-            bar: Dictionary containing 'time', 'close', and 'volume' data
-        """
-        self.current_bar = bar
-
-        # Add your strategy logic here
-        # For example:
-        # if self.current_bar['close'] > self.previous_close * (1 + self.threshold):
-        #     self.last_signal = 'buy'
-        # elif self.current_bar['close'] < self.previous_close * (1 - self.threshold):
-        #     self.last_signal = 'sell'
-        # else:
-        #     self.last_signal = 'hold'
-
-    def get_signal(self):
-        """
-        Return the current trading signal.
-        Must return one of: 'buy', 'sell', 'hold'
-        """
-        # Add your signal generation logic here
-        return 'hold'
-
     def get_signals(self, df):
         """
         Vectorized version of signal generation.