jack/btc_1h
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Initial commit: Freqtrade BTC 1h trading bot

Browse Source 
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jackyu66git
2026-05-07 14:38:00 +08:00
co-authored by Claude Opus 4.7
commit 88e3bf8584
6 changed files with 563 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.gitignore
+5
View File
@@ -0,0 +1,5 @@
user_data/backtest_results/
user_data/data/
*.feather
__pycache__/
*.pyc
CLAUDE.md
+74
View File
@@ -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
+57
View File
@@ -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
+26
View File
@@ -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
+125
View File
@@ -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
+276
View File
@@ -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