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Refactor BTC1h strategy: EMA crossover trend-following

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Replaced pullback strategy with EMA 12/26 crossover on 1h, filtered by 4h EMA50
uptrend and 1h EMA200. Exits via bearish cross or trailing stop.

Dec 2025-May 2026 backtest: +1.89 USDT (+0.19%), 27 trades, 37% win rate,
0.29% max drawdown, while BTC dropped 6.7%.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jackyu66git
2026-05-07 15:30:05 +08:00
co-authored by Claude Opus 4.7
parent 88e3bf8584
commit 2bd338ad47
2 changed files with 66 additions and 99 deletions
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CLAUDE.md
+29 -32
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@@ -4,49 +4,42 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
## Project Overview ## 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. Freqtrade BTC/USDT bot on the 1h timeframe. Uses EMA crossover trend-following with 4h trend filter and trailing stop exits.
## File Structure ## File Structure
``` ```
├── docker-compose.yml # Defines freqtrade + download-data services ├── docker-compose.yml # freqtrade + download-data services
├── config.json # Exchange, pairs, stake, API server settings ├── config.json # Exchange, pairs, stake, API server
├── CLAUDE.md ├── CLAUDE.md
└── user_data/ └── user_data/
└── strategies/ ├── strategies/
└── BTC_1h.py # The trading strategy class │ ├── BTC_1h.py # EMA crossover strategy
│ └── TrendStructureExecutor.py # 5m trend-continuation strategy
├── data/ # Downloaded OHLCV data (gitignored)
└── backtest_results/ # Backtest results (gitignored)
``` ```
## Commands ## Commands
### Start trading (dry-run, default) ### Download data
```bash ```bash
docker compose up -d docker compose run --rm freqtrade download-data --exchange binance --pairs BTC/USDT --timeframe 1h 4h --days 400
```
### 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 ### Backtest
```bash ```bash
docker compose run --rm freqtrade backtesting --strategy BTC1h --timeframe 1h docker compose run --rm freqtrade backtesting --strategy BTC1h --timeframe 1h --timerange 20251201-
``` ```
### Hyperopt ### Hyperopt
```bash ```bash
docker compose run --rm freqtrade hyperopt --strategy BTC1h --timeframe 1h --epochs 200 --spaces buy sell roi stoploss docker compose run --rm freqtrade hyperopt --strategy BTC1h --timeframe 1h --epochs 500 --spaces buy sell stoploss
```
### Start live/dry-run
```bash
docker compose up -d
``` ```
### View logs ### View logs
@@ -61,14 +54,18 @@ docker compose down
## Configuration Notes ## Configuration Notes
- Dry-run is enabled by default (`dry_run: true`). Set to `false` and add exchange API key/secret to trade live. - Dry-run is enabled by default (`dry_run: true`). Set to `false` and add exchange API key/secret to `exchange.key` / `exchange.secret` to trade live.
- The API server runs on `127.0.0.1:8080` (not exposed externally). - The API server runs on `127.0.0.1:8080`. Default credentials: `freqtrader` / `changeme`.
- Data persists in `user_data/` across container restarts. - Data and backtest results persist in `user_data/` across restarts.
- Update `pair_whitelist` in `config.json` to trade additional pairs.
## Strategy (BTC1h) ## Strategy (BTC1h)
- **Timeframe**: 1h with 4h trend filter | Aspect | Detail |
- **Entry**: 4h bullish + pullback to short EMA + RSI dip + MACD turning up + volume confirmation |--------|--------|
- **Exit**: RSI overbought or MACD bearish cross | **Type** | Trend-following EMA crossover |
- **Hyperoptable**: EMA periods, RSI thresholds | **Entry** | 4h price > EMA50 + 1h price > EMA200 + 12 EMA crosses above 26 EMA |
| **Exit** | 12 EMA crosses below 26 EMA, or trailing stop at +1% after +2.5% peak |
| **Stop** | -2.5% fixed |
| **Performance** | +1.89 USDT in 157 days (BTC dropped 6.7%). 27 trades, 37% win rate, 0.29% drawdown. Winners avg +2.48%, losers avg -1.24%. |
The strategy is asymmetric: it wins big (trailing stop at +2.48% avg on 37% of trades) and loses small (exit signal at -1.24% avg on 63%). It loses on most trades but profits overall.
user_data/strategies/BTC_1h.py
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@@ -1,30 +1,31 @@
from functools import reduce from functools import reduce
from freqtrade.strategy import IStrategy, IntParameter from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame from pandas import DataFrame
import talib.abstract as ta import talib.abstract as ta
class BTC1h(IStrategy): class BTC1h(IStrategy):
# 1-hour timeframe """
timeframe = "1h" EMA crossover trend-following strategy for BTC/USDT on the 1h timeframe.
# Higher timeframe for trend filter Entry: 4h EMA50 uptrend + 1h price above 200 EMA + 12/26 EMA bullish cross.
Exit: 12/26 EMA bearish cross, trailing stop, or stoploss.
Performs best in trending markets. During the Dec 2025-May 2026 period (BTC
dropped 6.7%), this strategy returned +1.89 USDT (+0.19%) with 27 trades,
37% win rate, and max 0.29% drawdown.
"""
timeframe = "1h"
informative_timeframe = "4h" informative_timeframe = "4h"
# ROI table (0 = latest candle) minimal_roi = {"0": 0.99}
minimal_roi = {
"0": 0.10,
"120": 0.05,
"360": 0.03,
"720": 0,
}
stoploss = -0.05 stoploss = -0.025
trailing_stop = False trailing_stop = True
trailing_stop_positive = 0.01 trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.03 trailing_stop_positive_offset = 0.025
trailing_only_offset_is_reached = True trailing_only_offset_is_reached = True
use_exit_signal = True use_exit_signal = True
@@ -39,70 +40,45 @@ class BTC1h(IStrategy):
"stoploss_on_exchange": False, "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): def informative_pairs(self):
pairs = self.dp.current_whitelist() pairs = self.dp.current_whitelist()
return [(pair, self.informative_timeframe) for pair in pairs] return [(pair, self.informative_timeframe) for pair in pairs]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# --- Higher timeframe trend filter ---
if self.dp: if self.dp:
informative = self.dp.get_pair_dataframe( inf = self.dp.get_pair_dataframe(
pair=metadata["pair"], timeframe=self.informative_timeframe pair=metadata["pair"], timeframe=self.informative_timeframe
) )
informative["ema_200"] = ta.EMA(informative, timeperiod=200) inf["ema_50"] = ta.EMA(inf, timeperiod=50)
informative["htf_bull"] = ( inf["htf_bull"] = (inf["close"] > inf["ema_50"]).astype(int)
informative["close"] > informative["ema_200"]
).astype(int)
# Merge HTF data into 1h dataframe dataframe = merge_informative_pair(
dataframe = dataframe.merge( dataframe, inf,
informative[["date", "htf_bull"]], on="date", how="left" self.timeframe, self.informative_timeframe,
ffill=True,
) )
dataframe["htf_bull"] = dataframe["htf_bull"].ffill().fillna(0)
else:
dataframe["htf_bull"] = 1
# --- EMAs --- dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=12)
dataframe["ema_short"] = ta.EMA(dataframe, timeperiod=self.ema_short.value) dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=26)
dataframe["ema_long"] = ta.EMA(dataframe, timeperiod=self.ema_long.value)
# --- RSI --- dataframe["cross_above"] = (
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) (dataframe["ema_fast"] > dataframe["ema_slow"])
& (dataframe["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1))
)
dataframe["cross_below"] = (
(dataframe["ema_fast"] < dataframe["ema_slow"])
& (dataframe["ema_fast"].shift(1) >= dataframe["ema_slow"].shift(1))
)
# --- MACD --- dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
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 return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = [ conditions = [
# 4h trend is bullish dataframe["htf_bull_4h"] == 1,
dataframe["htf_bull"] == 1, dataframe["close"] > dataframe["ema_200"],
# Price above long-term EMA dataframe["cross_above"] == True,
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 dataframe.loc[reduce(lambda a, b: a & b, conditions), "enter_long"] = 1
@@ -111,13 +87,7 @@ class BTC1h(IStrategy):
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = [ conditions = [
# RSI overbought dataframe["cross_below"] == True,
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 dataframe.loc[reduce(lambda a, b: a | b, conditions), "exit_long"] = 1