Initial commit: Freqtrade BTC 1h trading bot

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

.gitignore

@@ -0,0 +1,5 @@
+user_data/backtest_results/
+user_data/data/
+*.feather
+__pycache__/
+*.pyc

CLAUDE.md

@@ -0,0 +1,74 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Freqtrade BTC 1h trading bot. Strategy buys BTC/USDT pullbacks in uptrends using EMA, RSI, and MACD signals on the 1-hour timeframe, with a 4h trend filter.
+
+## File Structure
+
+```
+├── docker-compose.yml          # Defines freqtrade + download-data services
+├── config.json                 # Exchange, pairs, stake, API server settings
+├── CLAUDE.md
+└── user_data/
+    └── strategies/
+        └── BTC_1h.py           # The trading strategy class
+```
+
+## Commands
+
+### Start trading (dry-run, default)
+```bash
+docker compose up -d
+```
+
+### Start live trading (after configuring API keys in config.json)
+```bash
+docker compose up -d
+```
+
+### Download historical data
+```bash
+docker compose run --rm download-data
+```
+
+### Download data for custom pairs/timeframes
+```bash
+docker compose run --rm freqtrade download-data --exchange binance --pairs BTC/USDT ETH/USDT --timeframe 1h 4h --days 365
+```
+
+### Backtest
+```bash
+docker compose run --rm freqtrade backtesting --strategy BTC1h --timeframe 1h
+```
+
+### Hyperopt
+```bash
+docker compose run --rm freqtrade hyperopt --strategy BTC1h --timeframe 1h --epochs 200 --spaces buy sell roi stoploss
+```
+
+### View logs
+```bash
+docker compose logs -f
+```
+
+### Stop
+```bash
+docker compose down
+```
+
+## Configuration Notes
+
+- Dry-run is enabled by default (`dry_run: true`). Set to `false` and add exchange API key/secret to trade live.
+- The API server runs on `127.0.0.1:8080` (not exposed externally).
+- Data persists in `user_data/` across container restarts.
+- Update `pair_whitelist` in `config.json` to trade additional pairs.
+
+## Strategy (BTC1h)
+
+- **Timeframe**: 1h with 4h trend filter
+- **Entry**: 4h bullish + pullback to short EMA + RSI dip + MACD turning up + volume confirmation
+- **Exit**: RSI overbought or MACD bearish cross
+- **Hyperoptable**: EMA periods, RSI thresholds

config.json

@@ -0,0 +1,57 @@
+{
+    "max_open_trades": 3,
+    "stake_currency": "USDT",
+    "stake_amount": 50,
+    "tradable_balance_ratio": 0.99,
+    "dry_run": true,
+    "dry_run_wallet": 1000,
+    "cancel_timeout_on_new_position": true,
+    "timeframe": "1h",
+    "fiat_display_currency": "USD",
+    "trading_mode": "spot",
+    "margin_mode": "",
+    "exchange": {
+        "name": "binance",
+        "key": "",
+        "secret": "",
+        "ccxt_config": {
+            "rateLimit": 50
+        },
+        "pair_whitelist": [
+            "BTC/USDT"
+        ],
+        "pair_blacklist": []
+    },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "telegram": {
+        "enabled": false,
+        "chat_id": "",
+        "token": ""
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8080,
+        "username": "freqtrader",
+        "password": "changeme"
+    },
+    "initial_state": "running",
+    "force_entry_enable": true,
+    "internals": {
+        "process_throttle_secs": 5
+    },
+    "bot_name": "btc_1h"
+}

docker-compose.yml

@@ -0,0 +1,26 @@
+services:
+  freqtrade:
+    image: freqtradeorg/freqtrade:stable
+    container_name: btc_1h
+    restart: unless-stopped
+    volumes:
+      - ./config.json:/freqtrade/config.json:ro
+      - ./user_data:/freqtrade/user_data
+    ports:
+      - "127.0.0.1:8080:8080"
+    command: >
+      trade
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+
+  download-data:
+    image: freqtradeorg/freqtrade:stable
+    profiles: ["utils"]
+    volumes:
+      - ./config.json:/freqtrade/config.json:ro
+      - ./user_data:/freqtrade/user_data
+    command: >
+      download-data
+      --exchange binance
+      --pairs BTC/USDT
+      --timeframe 1h
+      --days 365

user_data/strategies/BTC_1h.py

@@ -0,0 +1,125 @@
+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

user_data/strategies/TrendStructureExecutor.py

@@ -0,0 +1,276 @@
+from functools import reduce
+
+import talib.abstract as ta
+from pandas import DataFrame
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from freqtrade.persistence import Trade
+from datetime import datetime
+
+
+class TrendStructureExecutor(IStrategy):
+    """
+    TrendStructureExecutor — Trend-continuation strategy (spot/futures).
+
+    Core concept:
+    Identify established trends on the 1h chart (EMA52 + MACD + EMA200),
+    then trade 5m continuation entries when the MACD histogram pulls back
+    to zero and resumes in the trend direction.  Skip low-volatility
+    ranging markets.  Partial take-profit on momentum weakening.
+    """
+
+    INTERFACE_VERSION = 3
+
+    # =========================================================================
+    # CONFIGURATION
+    # =========================================================================
+
+    timeframe = "5m"
+    informative_timeframe = "1h"
+
+    # Futures support (long + short)
+    # Set can_short = True and switch config to futures mode (BTC/USDT:USDT)
+    # to enable short trading.
+    can_short = False
+    # trading_mode = "futures"
+    # margin_mode = "isolated"
+
+    # Risk management — fixed 0.8% stoploss (tighter than the 1% ROI target)
+    stoploss = -0.008
+
+    # Trailing stop to protect profits
+    trailing_stop = True
+    trailing_stop_positive = 0.004
+    trailing_stop_positive_offset = 0.012
+    trailing_only_offset_is_reached = True
+
+    # Position adjustment for partial take-profits
+    position_adjustment_enable = True
+
+    # ROI disabled — exits managed by trailing stop + partial TP + EMA52 breach
+    minimal_roi = {"0": 0.99}
+
+    # General settings
+    use_exit_signal = True
+    exit_profit_only = False
+    startup_candle_count = 200
+    process_only_new_candles = True
+
+    order_types = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+
+    # =========================================================================
+    # INFORMATIVE PAIRS
+    # =========================================================================
+
+    def informative_pairs(self):
+        pairs = self.dp.current_whitelist()
+        return [(pair, self.informative_timeframe) for pair in pairs]
+
+    # =========================================================================
+    # INDICATORS
+    # =========================================================================
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        1h: EMA52, EMA200, MACD, slope, range/consolidation, trend flags.
+        5m: MACD, histogram direction helpers.
+        """
+        if self.dp:
+            informative = self.dp.get_pair_dataframe(
+                pair=metadata["pair"], timeframe=self.informative_timeframe
+            )
+
+            # --- EMA 52 ---
+            informative["ema_52"] = ta.EMA(informative, timeperiod=52)
+
+            # --- EMA 200 (super-trend filter) ---
+            informative["ema_200"] = ta.EMA(informative, timeperiod=200)
+
+            # EMA 52 slope (3-period ROC for noise reduction)
+            informative["ema_52_slope"] = (
+                informative["ema_52"] - informative["ema_52"].shift(3)
+            )
+
+            # --- MACD (12, 26, 9) ---
+            macd_1h = ta.MACD(informative)
+            informative["macd_hist_1h"] = macd_1h["macdhist"]
+            informative["macd_hist_1h_delta"] = (
+                informative["macd_hist_1h"] - informative["macd_hist_1h"].shift(1)
+            )
+
+            # --- Range / consolidation filter ---
+            # If the 20-candle price range is less than 1.5 %, the market is
+            # considered to be ranging and no entries are allowed.
+            informative["range_high_20"] = informative["high"].rolling(20).max()
+            informative["range_low_20"] = informative["low"].rolling(20).min()
+            informative["range_pct"] = (
+                (informative["range_high_20"] - informative["range_low_20"])
+                / informative["range_low_20"]
+            )
+            informative["is_ranging"] = (informative["range_pct"] < 0.015).astype(int)
+
+            # --- LONG trend confirmation ---
+            # Price above EMA52  +  EMA52 sloping up  +  MACD histogram positive
+            # + histogram not shrinking significantly (delta > -0.5 * rolling std)
+            informative["trend_bull"] = (
+                (informative["close"] > informative["ema_52"])
+                & (informative["close"] > informative["ema_200"])
+                & (informative["ema_52_slope"] > 0)
+                & (informative["macd_hist_1h"] > 0)
+                & (
+                    informative["macd_hist_1h_delta"]
+                    > -informative["macd_hist_1h"].rolling(20).std() * 0.5
+                )
+            ).astype(int)
+
+            # --- SHORT trend confirmation ---
+            # Price below EMA52  +  EMA52 sloping down  +  MACD histogram negative
+            # + histogram not expanding upward (delta < +0.5 * rolling std)
+            informative["trend_bear"] = (
+                (informative["close"] < informative["ema_52"])
+                & (informative["close"] < informative["ema_200"])
+                & (informative["ema_52_slope"] < 0)
+                & (informative["macd_hist_1h"] < 0)
+                & (
+                    informative["macd_hist_1h_delta"]
+                    < informative["macd_hist_1h"].rolling(20).std() * 0.5
+                )
+            ).astype(int)
+
+            # Merge 1h → 5m (merge_informative_pair handles lookahead protection
+            # by shifting the higher-timeframe data by one candle)
+            dataframe = merge_informative_pair(
+                dataframe,
+                informative,
+                self.timeframe,
+                self.informative_timeframe,
+                ffill=True,
+            )
+
+        # --- 5m MACD ---
+        macd_5m = ta.MACD(dataframe)
+        dataframe["macd_hist_5m"] = macd_5m["macdhist"]
+
+        # Direction helpers (avoids repeating shift logic in entry/exit methods)
+        dataframe["macd_hist_5m_up"] = (
+            dataframe["macd_hist_5m"] > dataframe["macd_hist_5m"].shift(1)
+        )
+        dataframe["macd_hist_5m_down"] = (
+            dataframe["macd_hist_5m"] < dataframe["macd_hist_5m"].shift(1)
+        )
+
+        return dataframe
+
+    # =========================================================================
+    # ENTRY LOGIC
+    # =========================================================================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        LONG:  1h bullish  +  5m MACD hist pullback-then-resumption  +  recent reset.
+        SHORT: 1h bearish  +  5m MACD hist pullback-then-resumption  +  recent reset.
+        Both skip ranging markets.
+        """
+        # Columns from merge_informative_pair carry the _1h suffix
+        trend_bull = dataframe["trend_bull_1h"]
+        trend_bear = dataframe["trend_bear_1h"]
+        is_ranging = dataframe["is_ranging_1h"]
+
+        # ── LONG ──────────────────────────────────────────────────────────────
+
+        long_conditions = [
+            trend_bull == 1,
+            is_ranging == 0,
+            dataframe["macd_hist_5m_down"].shift(1) == True,
+            dataframe["macd_hist_5m_up"] == True,
+            dataframe["macd_hist_5m"] > 0,
+            dataframe["macd_hist_5m"].rolling(3).min() < 0,
+        ]
+
+        dataframe.loc[
+            reduce(lambda a, b: a & b, long_conditions),
+            ["enter_long", "enter_tag"],
+        ] = (1, "long_continuation")
+
+        # ── SHORT ─────────────────────────────────────────────────────────────
+
+        short_conditions = [
+            trend_bear == 1,
+            is_ranging == 0,
+            dataframe["macd_hist_5m_up"].shift(1) == True,
+            dataframe["macd_hist_5m_down"] == True,
+            dataframe["macd_hist_5m"] < 0,
+            dataframe["macd_hist_5m"].rolling(3).max() > 0,
+        ]
+
+        dataframe.loc[
+            reduce(lambda a, b: a & b, short_conditions),
+            ["enter_short", "enter_tag"],
+        ] = (1, "short_continuation")
+
+        return dataframe
+
+    # =========================================================================
+    # EXIT LOGIC
+    # =========================================================================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        LONG: exit on 1h EMA52 breach (trend reversal).
+        SHORT (futures only): exit on 1h EMA52 breach.
+        """
+        long_cond = dataframe["close"] < dataframe["ema_52_1h"]
+        dataframe.loc[long_cond, "exit_long"] = 1
+        dataframe.loc[long_cond, "exit_tag"] = "long_exit"
+
+        if self.can_short:
+            short_cond = dataframe["close"] > dataframe["ema_52_1h"]
+            dataframe.loc[short_cond, "exit_short"] = 1
+            dataframe.loc[short_cond, "exit_tag"] = "short_exit"
+
+        return dataframe
+
+    # =========================================================================
+    # POSITION ADJUSTMENT (Partial Take-Profit)
+    # =========================================================================
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float,
+                              min_stake: float | None, max_stake: float,
+                              current_entry_rate: float, current_exit_rate: float,
+                              current_entry_profit: float, current_exit_profit: float,
+                              **kwargs) -> float | None:
+        """
+        Sell 50% when MACD momentum weakens while in profit.
+
+        Fires once per trade (guarded by filled_exits).  Exits half the
+        position when the 5m MACD histogram starts declining toward zero
+        while we are still above +0.5% profit.
+        """
+        if current_profit <= 0.005:
+            return None
+
+        # Only one partial exit per trade
+        filled_exits = trade.select_filled_orders(trade.exit_side)
+        if filled_exits:
+            return None
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        if dataframe is None or len(dataframe) < 2:
+            return None
+
+        last = dataframe.iloc[-1]
+        prev = dataframe.iloc[-2]
+
+        if trade.is_short:
+            if last["macd_hist_5m"] < 0 and last["macd_hist_5m"] > prev["macd_hist_5m"]:
+                return -(trade.stake_amount / 2)
+        else:
+            if last["macd_hist_5m"] > 0 and last["macd_hist_5m"] < prev["macd_hist_5m"]:
+                return -(trade.stake_amount / 2)
+
+        return None