Initial commit: Freqtrade BTC 1h trading bot

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-07 14:38:00 +08:00
commit 88e3bf8584
6 changed files with 563 additions and 0 deletions
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+from functools import reduce
+
+from freqtrade.strategy import IStrategy, IntParameter
+from pandas import DataFrame
+import talib.abstract as ta
+
+
+class BTC1h(IStrategy):
+    # 1-hour timeframe
+    timeframe = "1h"
+
+    # Higher timeframe for trend filter
+    informative_timeframe = "4h"
+
+    # ROI table (0 = latest candle)
+    minimal_roi = {
+        "0": 0.10,
+        "120": 0.05,
+        "360": 0.03,
+        "720": 0,
+    }
+
+    stoploss = -0.05
+
+    trailing_stop = False
+    trailing_stop_positive = 0.01
+    trailing_stop_positive_offset = 0.03
+    trailing_only_offset_is_reached = True
+
+    use_exit_signal = True
+    exit_profit_only = False
+
+    startup_candle_count = 200
+
+    order_types = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+
+    # --- Hyperoptable parameters ---
+    ema_short = IntParameter(20, 50, default=34, space="buy")
+    ema_long = IntParameter(100, 200, default=144, space="buy")
+    rsi_entry = IntParameter(25, 45, default=35, space="buy")
+    rsi_exit = IntParameter(60, 80, default=70, space="sell")
+
+    def informative_pairs(self):
+        pairs = self.dp.current_whitelist()
+        return [(pair, self.informative_timeframe) for pair in pairs]
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # --- Higher timeframe trend filter ---
+        if self.dp:
+            informative = self.dp.get_pair_dataframe(
+                pair=metadata["pair"], timeframe=self.informative_timeframe
+            )
+            informative["ema_200"] = ta.EMA(informative, timeperiod=200)
+            informative["htf_bull"] = (
+                informative["close"] > informative["ema_200"]
+            ).astype(int)
+
+            # Merge HTF data into 1h dataframe
+            dataframe = dataframe.merge(
+                informative[["date", "htf_bull"]], on="date", how="left"
+            )
+            dataframe["htf_bull"] = dataframe["htf_bull"].ffill().fillna(0)
+        else:
+            dataframe["htf_bull"] = 1
+
+        # --- EMAs ---
+        dataframe["ema_short"] = ta.EMA(dataframe, timeperiod=self.ema_short.value)
+        dataframe["ema_long"] = ta.EMA(dataframe, timeperiod=self.ema_long.value)
+
+        # --- RSI ---
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+
+        # --- MACD ---
+        macd = ta.MACD(dataframe)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macd_signal"] = macd["macdsignal"]
+        dataframe["macd_hist"] = macd["macdhist"]
+
+        # --- Volume ---
+        dataframe["volume_ma"] = ta.SMA(dataframe, timeperiod=20)
+
+        # --- ATR ---
+        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = [
+            # 4h trend is bullish
+            dataframe["htf_bull"] == 1,
+            # Price above long-term EMA
+            dataframe["close"] > dataframe["ema_long"],
+            # Pullback near short-term EMA
+            dataframe["close"] < dataframe["ema_short"] * 1.02,
+            # RSI dip
+            dataframe["rsi"] < self.rsi_entry.value,
+            # MACD turning up
+            dataframe["macd_hist"] > dataframe["macd_hist"].shift(1),
+            # Volume confirmation
+            dataframe["volume"] > dataframe["volume_ma"],
+        ]
+
+        dataframe.loc[reduce(lambda a, b: a & b, conditions), "enter_long"] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = [
+            # RSI overbought
+            dataframe["rsi"] > self.rsi_exit.value,
+            # MACD bearish cross
+            (
+                (dataframe["macd"] < dataframe["macd_signal"])
+                & (dataframe["macd"].shift(1) > dataframe["macd_signal"].shift(1))
+            ),
+        ]
+
+        dataframe.loc[reduce(lambda a, b: a | b, conditions), "exit_long"] = 1
+
+        return dataframe