btc update
This commit is contained in:
crypto_rahino
parent
ef354cb6b9
commit
153ecf2ab5
@ -15,18 +15,18 @@ import matplotlib
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# # Initialize empty configuration object
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from freqtrade.strategy import IStrategy
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config = Configuration.from_files(["user_data/config_ltcusdt_1h.json"])
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config = Configuration.from_files(["user_data/config_btcusdt_1m.json"])
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# # Optionally, use existing configuration file
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# config = Configuration.from_files(["config.json"])
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#
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# # Define some constants
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# config["timeframe"] = "1m"
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# # Name of the strategy class
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config["strategy"] = "ltcusdt_1h"
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config["strategy"] = "quick_btcusdt_1m"
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# # Location of the data
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data_location = Path(config['user_data_dir'], 'data', 'binance')
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# # Pair to analyze - Only use one pair here
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pair = "LTC/USDT"
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pair = "BTC/USDT"
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#
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# # Load data using values set above
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#
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@ -102,7 +102,7 @@ def plot_db(path):
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graph = generate_candlestick_graph(pair=pair,
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data=data_red,
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trades=trades_red,
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indicators1=['tsf_mid'],
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indicators1=['tsf_mid', 'match_low'],
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indicators2=['correl_tsf_mid_close', 'correl_angle_short_close',
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'correl_angle_long_close', 'correl_hist_close']
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)
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@ -148,12 +148,12 @@ def plot_backtest(start_date, stop_date):
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graph = generate_candlestick_graph(pair=pair,
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data=data_red,
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trades=trades,
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indicators1=['sar', 'tsf_mid'],
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indicators2=['sine', 'leadsine', 'angle_trend_mid', 'inphase', 'quadrature' ]
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indicators1=['tsf_mid', 'fib', 'kama', 'fib_1h'],
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indicators2=['macd', 'bop_1h', 'macdsignal', 'mode', 'period_1h', 'sine', 'leadsine', 'period']
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)
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graph.show()
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plot_backtest(start_date='2020-01-01', stop_date='2021-03-18')
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plot_backtest(start_date='2021-03-01', stop_date='2021-03-26')
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@ -1,10 +1,10 @@
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{
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"max_open_trades": 3,
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"max_open_trades": 10,
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"stake_currency": "USDT",
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"stake_amount": 0.05,
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"stake_amount": 100,
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"tradable_balance_ratio": 0.99,
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"fiat_display_currency": "USD",
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"timeframe": "1m",
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"timeframe": "1h",
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"dry_run": true,
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"cancel_open_orders_on_exit": false,
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"unfilledtimeout": {
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@ -44,7 +44,7 @@
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"DOGE/USDT",
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"BTC/USDT",
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"ADA/USDT",
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"TRX/XRP"
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"LTC/USDT"
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],
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"pair_blacklist": [
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"BNB/BTC"
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@ -1,18 +1,19 @@
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{
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"max_open_trades": 3,
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"stake_currency": "USDT",
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"stake_amount": 1000.0,
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"stake_amount": 990.0,
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"tradable_balance_ratio": 0.99,
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"fiat_display_currency": "USD",
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"timeframe": "1m",
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"dry_run": true,
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"position_stacking": true,
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"position_stacking": false,
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"cancel_open_orders_on_exit": true,
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"unfilledtimeout": {
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"buy": 10,
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"sell": 30
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},
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"bid_strategy": {
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"price_side": "ask",
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"use_order_book": false,
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"ask_last_balance": 0.0,
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"order_book_top": 1,
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@ -22,6 +23,7 @@
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}
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},
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"ask_strategy":{
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"price_side": "bid",
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"use_order_book": false,
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"order_book_min": 1,
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"order_book_max": 1,
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@ -13,6 +13,7 @@
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"sell": 30
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},
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"bid_strategy": {
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"price_side": "ask",
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"use_order_book": false,
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"ask_last_balance": 0.0,
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"order_book_top": 1,
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@ -22,6 +23,7 @@
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}
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},
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"ask_strategy":{
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"price_side": "bid",
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"use_order_book": false,
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"order_book_min": 1,
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"order_book_max": 1,
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@ -1,18 +1,19 @@
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{
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"max_open_trades": 3,
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"max_open_trades": 2,
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"stake_currency": "USDT",
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"stake_amount": 100.0,
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"stake_amount": 990,
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"tradable_balance_ratio": 0.99,
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"fiat_display_currency": "USD",
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"timeframe": "1h",
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"dry_run": true,
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"position_stacking": true,
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"position_stacking": false,
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"cancel_open_orders_on_exit": false,
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"unfilledtimeout": {
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"buy": 10,
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"sell": 30
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},
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"bid_strategy": {
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"price_side": "ask",
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"use_order_book": false,
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"ask_last_balance": 0.0,
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"order_book_top": 1,
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@ -22,12 +23,13 @@
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}
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},
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"ask_strategy":{
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"use_order_book": true,
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"price_side": "bid",
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"use_order_book": false,
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"order_book_min": 1,
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"order_book_max": 1,
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"use_sell_signal": true,
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"sell_profit_only": true,
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"ignore_roi_if_buy_signal": true
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"sell_profit_only": false,
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"ignore_roi_if_buy_signal": false
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},
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"exchange": {
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"name": "binance",
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@ -40,6 +42,7 @@
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},
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"pair_whitelist": [
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"LTC/USDT"
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],
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"pair_blacklist": [
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"BNB/BTC"
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@ -49,22 +52,22 @@
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{"method": "StaticPairList"}
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],
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"edge": {
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"enabled": false,
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"enabled": true,
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"process_throttle_secs": 3600,
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"calculate_since_number_of_days": 7,
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"allowed_risk": 0.01,
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"calculate_since_number_of_days": 14,
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"allowed_risk": 0.02,
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"stoploss_range_min": -0.01,
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"stoploss_range_max": -0.1,
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"stoploss_range_step": -0.01,
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"minimum_winrate": 0.60,
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"minimum_expectancy": 0.20,
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"min_trade_number": 10,
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"max_trade_duration_minute": 1440,
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"max_trade_duration_minute": 14400,
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"remove_pumps": false
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},
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"telegram": {
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"enabled": false,
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"token": "1661670711:AAEjHFL_wnvc-hN0V8NKe0G53mzrz9rWnzk",
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"token": "1725064196:AAGvqiJEC_uv8THpsZG1F_Or7T2dGYRFuqU",
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"chat_id": "1288347675",
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"keyboard": [
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["/daily", "/stats", "/balance", "/profit"],
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@ -53,13 +53,29 @@ class hyper_ltcusdt_1h(IHyperOpt):
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# GUARDS AND TRENDS
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if params.get('ao-enabled'):
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conditions.append(dataframe['ao'] > params['ao1-value'])
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conditions.append(dataframe['ao'] < params['ao2-value'])
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conditions.append(dataframe['ao'] > params['ao-value'])
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if params.get('uo-enabled'):
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conditions.append(dataframe['uo'] > params['uo-value'])
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if params.get('sar_close-enabled'):
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conditions.append(dataframe['sar_close'] < params['sar_close-value'])
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if params.get('natr-enabled'):
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conditions.append(dataframe['natr'] > params['natr-value'])
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if params.get('angle_tsf_mid-enabled'):
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conditions.append(dataframe['angle_tsf_mid'] < params['angle_tsf_mid-value'])
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if params.get('rsi-enabled'):
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conditions.append(params['rsi1-value'] < dataframe['rsi'])
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conditions.append(params['rsi2-value'] > dataframe['rsi'])
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if params.get('angle_trend_mid-enabled'):
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conditions.append(dataframe['angle_trend_mid'] < params['angle_trend_mid-value'])
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if params.get('mfi-enabled'):
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conditions.append(params['mfi-value'] < dataframe['mfi'])
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if params.get('angle-enabled'):
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conditions.append(params['angle-value'] < dataframe['angle'])
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if params.get('angle_macdsignal-enabled'):
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conditions.append(params['angle_macdsignal-value'] < dataframe['angle_macdsignal'])
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if params.get('macdhist-enabled'):
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conditions.append(params['macdhist-value'] < dataframe['macdhist'])
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if params.get('macdsignal-enabled'):
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conditions.append(params['macdsignal-value'] < dataframe['macdsignal'])
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if params.get('macd-enabled'):
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conditions.append(params['macd-value'] < (dataframe['macd'] - dataframe['macdsignal']))
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# TRIGGERS
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if 'trigger' in params:
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@ -71,6 +87,14 @@ class hyper_ltcusdt_1h(IHyperOpt):
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conditions.append(qtpylib.crossed_above(
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dataframe['leadsine'], dataframe['sine']
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))
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if params['trigger'] == 'phase':
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conditions.append(qtpylib.crossed_above(
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dataframe['quadrature'], dataframe['inphase']
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))
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if params['trigger'] == 'angle_tsf_mid':
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conditions.append(qtpylib.crossed_above(
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dataframe['angle_tsf_mid'], 0
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))
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if conditions:
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dataframe.loc[
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@ -87,15 +111,31 @@ class hyper_ltcusdt_1h(IHyperOpt):
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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return [
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Integer(-50, 50, name='ao1-value'),
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Integer(-50, 50, name='ao2-value'),
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Integer(-87, 85, name='angle_tsf_mid-value'),
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Integer(8, 92, name='rsi1-value'),
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Integer(8, 92, name='rsi2-value'),
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Integer(-50, 35, name='ao-value'),
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Integer(-86, 70, name='sar_close-value'),
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Integer(-75, 72, name='angle_tsf_mid-value'),
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Integer(-73, 68, name='angle_trend_mid-value'),
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Integer(-2, 80, name='uo-value'),
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Real(0.36, 11, name='natr-value'),
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Integer(0, 100, name='mfi-value'),
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Integer(-81, 78, name='angle-value'),
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Integer(-38, 50, name='angle_macdsignal-value'),
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Real(-4, 5.3, name='macdhist-value'),
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Integer(-15, 11, name='macdsignal-value'),
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Real(-4, 5, name='macd-value'),
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Categorical([True, False], name='ao-enabled'),
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Categorical([True, False], name='angle_tsf_mid-enabled'),
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Categorical([True, False], name='rsi-enabled'),
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Categorical(['sar', 'sine'], name='trigger')
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Categorical([True, False], name='sar_close-enabled'),
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Categorical([True, False], name='angle_trend_mid-enabled'),
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Categorical([True, False], name='uo-enabled'),
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Categorical([True, False], name='natr-enabled'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='angle-enabled'),
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Categorical([True, False], name='angle_macdsignal-enabled'),
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Categorical([True, False], name='macdhist-enabled'),
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Categorical([True, False], name='macdsignal-enabled'),
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Categorical([True, False], name='macd-enabled'),
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Categorical(['sar', 'sine', 'angle_tsf_mid', 'angle_trend_mid', 'phase'], name='trigger')
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]
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@staticmethod
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@ -112,13 +152,29 @@ class hyper_ltcusdt_1h(IHyperOpt):
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# GUARDS AND TRENDS
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if params.get('ao-enabled'):
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conditions.append(dataframe['ao'] > params['ao1-value_sell'])
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conditions.append(dataframe['ao'] < params['ao2-value_sell'])
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conditions.append(dataframe['ao'] > params['ao-value_sell'])
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if params.get('uo-enabled'):
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conditions.append(dataframe['uo'] > params['uo-value_sell'])
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if params.get('sar_close-enabled'):
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conditions.append(dataframe['sar_close'] < params['sar_close-value_sell'])
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if params.get('natr-enabled'):
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conditions.append(dataframe['natr'] > params['natr-value_sell'])
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if params.get('angle_tsf_mid-enabled'):
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conditions.append(dataframe['angle_tsf_mid'] > params['angle_tsf_mid-value_sell'])
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if params.get('rsi-enabled'):
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conditions.append(params['rsi1-value_sell'] < dataframe['rsi'])
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conditions.append(params['rsi2-value_sell'] > dataframe['rsi'])
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conditions.append(dataframe['angle_tsf_mid'] < params['angle_tsf_mid-value_sell'])
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if params.get('angle_trend_mid-enabled'):
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conditions.append(dataframe['angle_trend_mid'] < params['angle_trend_mid-value_sell'])
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if params.get('mfi-enabled'):
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conditions.append(params['mfi-value_sell'] < dataframe['mfi'])
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if params.get('angle-enabled'):
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conditions.append(params['angle-value_sell'] < dataframe['angle'])
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if params.get('angle_macdsignal-enabled'):
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conditions.append(params['angle_macdsignal-value_sell'] < dataframe['angle_macdsignal'])
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if params.get('macdhist-enabled'):
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conditions.append(params['macdhist-value_sell'] < dataframe['macdhist'])
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if params.get('macdsignal-enabled'):
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conditions.append(params['macdsignal-value_sell'] < dataframe['macdsignal'])
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if params.get('macd-enabled'):
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conditions.append(params['macd-value_sell'] < (dataframe['macd'] - dataframe['macdsignal']))
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# TRIGGERS
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if 'trigger' in params:
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@ -130,6 +186,18 @@ class hyper_ltcusdt_1h(IHyperOpt):
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conditions.append(qtpylib.crossed_below(
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dataframe['leadsine'], dataframe['sine']
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))
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if params['trigger'] == 'phase':
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conditions.append(qtpylib.crossed_below(
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dataframe['quadrature'], dataframe['inphase']
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))
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if params['trigger'] == 'angle_trend_mid':
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conditions.append(qtpylib.crossed_below(
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dataframe['angle_trend_mid'], 0
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))
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if params['trigger'] == 'angle_tsf_mid':
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conditions.append(qtpylib.crossed_below(
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dataframe['angle_tsf_mid'], 0
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))
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if conditions:
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@ -147,15 +215,31 @@ class hyper_ltcusdt_1h(IHyperOpt):
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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Integer(-50, 50, name='ao1-value_sell'),
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Integer(-50, 50, name='ao2-value_sell'),
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Integer(-87, 85, name='angle_tsf_mid-value_sell'),
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Integer(8, 92, name='rsi1-value_sell'),
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Integer(8, 92, name='rsi2-value_sell'),
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Integer(-50, 35, name='ao-value_sell'),
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Integer(-86, 70, name='sar_close-value_sell'),
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Integer(-75, 72, name='angle_tsf_mid-value_sell'),
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Integer(-73, 68, name='angle_trend_mid-value_sell'),
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Integer(-2, 80, name='uo-value_sell'),
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Real(0.36, 11, name='natr-value_sell'),
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Integer(0, 100, name='mfi-value_sell'),
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Integer(-81, 78, name='angle-value_sell'),
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Integer(-38, 50, name='angle_macdsignal-value_sell'),
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Real(-4, 5.3, name='macdhist-value_sell'),
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Integer(-15, 11, name='macdsignal-value_sell'),
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Real(-4, 5, name='macd-value_sell'),
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Categorical([True, False], name='ao-enabled'),
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Categorical([True, False], name='angle_tsf_mid-enabled'),
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Categorical([True, False], name='rsi-enabled'),
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Categorical(['sar', 'sine'], name='trigger')
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Categorical([True, False], name='sar_close-enabled'),
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Categorical([True, False], name='angle_trend_mid-enabled'),
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Categorical([True, False], name='uo-enabled'),
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Categorical([True, False], name='natr-enabled'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='angle-enabled'),
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Categorical([True, False], name='angle_macdsignal-enabled'),
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Categorical([True, False], name='macdhist-enabled'),
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Categorical([True, False], name='macdsignal-enabled'),
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Categorical([True, False], name='macd-enabled'),
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Categorical(['sar', 'sine', 'angle_tsf_mid', 'angle_trend_mid', 'phase'], name='trigger')
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]
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@staticmethod
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@ -208,7 +292,7 @@ class hyper_ltcusdt_1h(IHyperOpt):
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'stoploss' optimization hyperspace.
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"""
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return [
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Real(-0.25, -0.02, name='stoploss'),
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Real(-0.3, -0.02, name='stoploss'),
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]
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@staticmethod
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@ -1,240 +0,0 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer, Real # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta # noqa
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class hyper_ethusdt2(IHyperOpt):
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"""
|
||||
This is a Hyperopt template to get you started.
|
||||
|
||||
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
|
||||
|
||||
You should:
|
||||
- Add any lib you need to build your hyperopt.
|
||||
|
||||
You must keep:
|
||||
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
|
||||
|
||||
The methods roi_space, generate_roi_table and stoploss_space are not required
|
||||
and are provided by default.
|
||||
However, you may override them if you need 'roi' and 'stoploss' spaces that
|
||||
differ from the defaults offered by Freqtrade.
|
||||
Sample implementation of these methods will be copied to `user_data/hyperopts` when
|
||||
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
|
||||
or is available online under the following URL:
|
||||
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by Hyperopt.
|
||||
"""
|
||||
|
||||
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Buy strategy Hyperopt will build and use.
|
||||
"""
|
||||
conditions = []
|
||||
|
||||
# GUARDS AND TRENDS
|
||||
if params.get('ao-enabled'):
|
||||
conditions.append(dataframe['ao'] > params['ao1-value'])
|
||||
conditions.append(dataframe['ao'] < params['ao2-value'])
|
||||
if params.get('angle_tsf_mid-enabled'):
|
||||
conditions.append(dataframe['angle_tsf_mid'] < params['angle_tsf_mid-value'])
|
||||
if params.get('rsi-enabled'):
|
||||
conditions.append(params['rsi1-value'] < dataframe['rsi'])
|
||||
conditions.append(params['rsi2-value'] > dataframe['rsi'])
|
||||
|
||||
# TRIGGERS
|
||||
if 'trigger' in params:
|
||||
if params['trigger'] == 'ao_cross':
|
||||
conditions.append(qtpylib.crossed_above(
|
||||
dataframe['ao'], 0
|
||||
))
|
||||
if params['trigger'] == 'angle_tsf_mid_cross_up':
|
||||
conditions.append(qtpylib.crossed_above(
|
||||
dataframe['angle_tsf_mid'], -50
|
||||
))
|
||||
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_buy_trend
|
||||
|
||||
@staticmethod
|
||||
def indicator_space() -> List[Dimension]:
|
||||
"""
|
||||
Define your Hyperopt space for searching buy strategy parameters.
|
||||
"""
|
||||
return [
|
||||
Integer(-50, 50, name='ao1-value'),
|
||||
Integer(-50, 50, name='ao2-value'),
|
||||
Integer(-87, 85, name='angle_tsf_mid-value'),
|
||||
Integer(8, 92, name='rsi1-value'),
|
||||
Integer(8, 92, name='rsi2-value'),
|
||||
Categorical([True, False], name='ao-enabled'),
|
||||
Categorical([True, False], name='angle_tsf_mid-enabled'),
|
||||
Categorical([True, False], name='rsi-enabled'),
|
||||
Categorical(['ao_cross', 'angle_tsf_mid_cross_up'], name='trigger')
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the sell strategy parameters to be used by Hyperopt.
|
||||
"""
|
||||
|
||||
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Sell strategy Hyperopt will build and use.
|
||||
"""
|
||||
conditions = []
|
||||
|
||||
# GUARDS AND TRENDS
|
||||
if params.get('ao-enabled'):
|
||||
conditions.append(dataframe['ao'] > params['ao1-value_sell'])
|
||||
conditions.append(dataframe['ao'] < params['ao2-value_sell'])
|
||||
if params.get('angle_tsf_mid-enabled'):
|
||||
conditions.append(dataframe['angle_tsf_mid'] > params['angle_tsf_mid-value_sell'])
|
||||
if params.get('rsi-enabled'):
|
||||
conditions.append(params['rsi1-value_sell'] < dataframe['rsi'])
|
||||
conditions.append(params['rsi2-value_sell'] > dataframe['rsi'])
|
||||
|
||||
# TRIGGERS
|
||||
if 'trigger' in params:
|
||||
if params['trigger'] == 'ao_cross_dw':
|
||||
conditions.append(qtpylib.crossed_below(
|
||||
dataframe['ao'], 0
|
||||
))
|
||||
if params['trigger'] == 'angle_tsf_mid_cross_dw':
|
||||
conditions.append(qtpylib.crossed_below(
|
||||
dataframe['angle_tsf_mid'], 50
|
||||
))
|
||||
|
||||
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'sell'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_sell_trend
|
||||
|
||||
@staticmethod
|
||||
def sell_indicator_space() -> List[Dimension]:
|
||||
"""
|
||||
Define your Hyperopt space for searching sell strategy parameters.
|
||||
"""
|
||||
return [
|
||||
Integer(-50, 50, name='ao1-value_sell'),
|
||||
Integer(-50, 50, name='ao2-value_sell'),
|
||||
Integer(-87, 85, name='angle_tsf_mid-value_sell'),
|
||||
Integer(8, 92, name='rsi1-value_sell'),
|
||||
Integer(8, 92, name='rsi2-value_sell'),
|
||||
Categorical([True, False], name='ao-enabled'),
|
||||
Categorical([True, False], name='angle_tsf_mid-enabled'),
|
||||
Categorical([True, False], name='rsi-enabled'),
|
||||
Categorical(['angle_tsf_mid_cross_dw', 'ao_cross_dw'], name='trigger')
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def generate_roi_table(params: Dict) -> Dict[int, float]:
|
||||
"""
|
||||
Generate the ROI table that will be used by Hyperopt
|
||||
|
||||
This implementation generates the default legacy Freqtrade ROI tables.
|
||||
|
||||
Change it if you need different number of steps in the generated
|
||||
ROI tables or other structure of the ROI tables.
|
||||
|
||||
Please keep it aligned with parameters in the 'roi' optimization
|
||||
hyperspace defined by the roi_space method.
|
||||
"""
|
||||
roi_table = {}
|
||||
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
||||
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
|
||||
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
|
||||
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
|
||||
|
||||
return roi_table
|
||||
|
||||
@staticmethod
|
||||
def roi_space() -> List[Dimension]:
|
||||
"""
|
||||
Values to search for each ROI steps
|
||||
|
||||
Override it if you need some different ranges for the parameters in the
|
||||
'roi' optimization hyperspace.
|
||||
|
||||
Please keep it aligned with the implementation of the
|
||||
generate_roi_table method.
|
||||
"""
|
||||
return [
|
||||
Integer(60, 600, name='roi_t1'),
|
||||
Integer(300, 1000, name='roi_t2'),
|
||||
Integer(500, 1500, name='roi_t3'),
|
||||
Real(0.01, 0.04, name='roi_p1'),
|
||||
Real(0.01, 0.07, name='roi_p2'),
|
||||
Real(0.01, 0.20, name='roi_p3'),
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def stoploss_space() -> List[Dimension]:
|
||||
"""
|
||||
Stoploss Value to search
|
||||
|
||||
Override it if you need some different range for the parameter in the
|
||||
'stoploss' optimization hyperspace.
|
||||
"""
|
||||
return [
|
||||
Real(-0.25, -0.02, name='stoploss'),
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def trailing_space() -> List[Dimension]:
|
||||
"""
|
||||
Create a trailing stoploss space.
|
||||
|
||||
You may override it in your custom Hyperopt class.
|
||||
"""
|
||||
return [
|
||||
# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
|
||||
# is used. Otherwise hyperopt will vary other parameters that won't have effect if
|
||||
# trailing_stop is set False.
|
||||
# This parameter is included into the hyperspace dimensions rather than assigning
|
||||
# it explicitly in the code in order to have it printed in the results along with
|
||||
# other 'trailing' hyperspace parameters.
|
||||
Categorical([True], name='trailing_stop'),
|
||||
|
||||
Real(0.01, 0.35, name='trailing_stop_positive'),
|
||||
|
||||
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
|
||||
# so this intermediate parameter is used as the value of the difference between
|
||||
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
|
||||
# generate_trailing_params() method.
|
||||
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
|
||||
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
|
||||
|
||||
Categorical([True, False], name='trailing_only_offset_is_reached'),
|
||||
]
|
||||
@ -1,208 +0,0 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
# isort: skip_file
|
||||
# --- Do not remove these libs ---
|
||||
import numpy as np # noqa
|
||||
import pandas as pd # noqa
|
||||
from pandas import DataFrame
|
||||
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, MAX, MIN, SAR, CCI, \
|
||||
HT_TRENDLINE, HT_DCPERIOD, HT_TRENDMODE, HT_SINE, RSI
|
||||
|
||||
from freqtrade.strategy import merge_informative_pair
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
|
||||
# --------------------------------
|
||||
# Add your lib to import here
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
|
||||
"""
|
||||
|
||||
"""
|
||||
|
||||
|
||||
# This class is a sample. Feel free to customize it.
|
||||
class strg2_ETHUSDT_1h_prod1(IStrategy):
|
||||
"""
|
||||
This is a sample strategy to inspire you.
|
||||
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
|
||||
|
||||
You can:
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
- Rename the class name (Do not forget to update class_name)
|
||||
- Add any methods you want to build your strategy
|
||||
- Add any lib you need to build your strategy
|
||||
|
||||
You must keep:
|
||||
- the lib in the section "Do not remove these libs"
|
||||
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
|
||||
populate_sell_trend, hyperopt_space, buy_strategy_generator
|
||||
"""
|
||||
# Strategy interface version - allow new iterations of the strategy interface.
|
||||
# Check the documentation or the Sample strategy to get the latest version.
|
||||
INTERFACE_VERSION = 2
|
||||
|
||||
# Minimal ROI designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "minimal_roi".
|
||||
minimal_roi = {
|
||||
"0": 0.07371,
|
||||
"9817": 0.0461,
|
||||
"14487": 0.0254,
|
||||
"15960": 0
|
||||
}
|
||||
order_types = {
|
||||
'buy': 'market',
|
||||
'sell': 'market',
|
||||
'stoploss': 'market',
|
||||
'stoploss_on_exchange': False
|
||||
}
|
||||
# Optimal stoploss designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "stoploss".
|
||||
stoploss = -0.23371
|
||||
|
||||
# Trailing stop:
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.11193
|
||||
trailing_stop_positive_offset = 0.20381
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
plot_config = {
|
||||
'main_plot': {
|
||||
'upperband': {'upperband': 'green'},
|
||||
'middleband': {'color': 'green'},
|
||||
'lowerband': {'color': 'green'},
|
||||
'tsf_mid': {'color': 'white'},
|
||||
'ema': {'color': 'white'},
|
||||
},
|
||||
'subplots': {
|
||||
"corr": {
|
||||
'correl_h_l': {'color': 'black'},
|
||||
},
|
||||
"correl_tsf_mid_close": {
|
||||
'correl_tsf_mid_close': {'color': 'grey'},
|
||||
},
|
||||
"correl_angle_short_close": {
|
||||
'correl_angle_short_close': {'color': 'blue'},
|
||||
},
|
||||
"correl_angle_long_close": {
|
||||
'correl_angle_long_close': {'color': 'red'},
|
||||
},
|
||||
"correl_mfi_close": {
|
||||
'correl_mfi_close': {'color': 'black'},
|
||||
},
|
||||
"correl_hist_close": {
|
||||
'correl_tsf_mid_close': {'color': 'red'},
|
||||
},
|
||||
"mfi": {
|
||||
'mfi': {'color': 'yellow'},
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
def informative_pairs(self):
|
||||
"""
|
||||
Define additional, informative pair/interval combinations to be cached from the exchange.
|
||||
These pair/interval combinations are non-tradeable, unless they are part
|
||||
of the whitelist as well.
|
||||
For more information, please consult the documentation
|
||||
:return: List of tuples in the format (pair, interval)
|
||||
Sample: return [("ETH/USDT", "5m"),
|
||||
("BTC/USDT", "15m"),
|
||||
]
|
||||
"""
|
||||
return [("ETH/USDT", "1d")]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Adds several different TA indicators to the given DataFrame
|
||||
|
||||
Performance Note: For the best performance be frugal on the number of indicators
|
||||
you are using. Let uncomment only the indicator you are using in your strategies
|
||||
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||||
:param dataframe: Dataframe with data from the exchange
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
"""
|
||||
|
||||
# dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
|
||||
# slowperiod=26, signalperiod=7)
|
||||
|
||||
# dataframe['cci'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
|
||||
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=34)
|
||||
# dataframe['vwap'] = qtpylib.vwap(dataframe)
|
||||
# dataframe['vwap'] = qtpylib.rolling_vwap(dataframe)
|
||||
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
|
||||
# dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=200)
|
||||
# dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=7)
|
||||
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=50)
|
||||
|
||||
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=10)
|
||||
# dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=50)
|
||||
|
||||
# dataframe['tsf_max'] = MAX(dataframe['tsf_mid'], timeperiod=30)
|
||||
# dataframe['tsf_min'] = MIN(dataframe['tsf_mid'], timeperiod=30)
|
||||
# dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
|
||||
# dataframe['sine'], dataframe['leadsine'] = HT_SINE(dataframe['close'])
|
||||
# dataframe['trend'] = HT_TRENDLINE(dataframe['close'])
|
||||
# dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
|
||||
# timeperiod=5)
|
||||
# dataframe['angle_trend_mid'] = LINEARREG_ANGLE(dataframe['trend'], timeperiod=10)
|
||||
|
||||
# dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
|
||||
# timeperiod=30, nbdevup=2,
|
||||
# nbdevdn=2, matype=0)
|
||||
# if not self.dp:
|
||||
# # Don't do anything if DataProvider is not available.
|
||||
# return dataframe
|
||||
# # Get the informative pair
|
||||
# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
|
||||
# informative['trend'] = HT_TRENDLINE(informative['close'])
|
||||
# informative['period'] = HT_DCPERIOD(informative['close'])
|
||||
# informative['mode'] = HT_TRENDMODE(informative['close'])
|
||||
# # informative['sine'], informative['leadsine'] = HT_SINE(informative['close'])
|
||||
# informative['angle_trend'] = LINEARREG_ANGLE(informative['trend'], timeperiod=5)
|
||||
# # informative['sar'] = SAR(informative['high'], informative['low'])
|
||||
# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '1d', ffill=True)
|
||||
# return dataframe.set_index('date')['2021-01-01':].reset_index()
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the buy signal for the given dataframe
|
||||
:param dataframe: DataFrame populated with indicators
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(qtpylib.crossed_above(dataframe['angle_tsf_mid'], -50)) &
|
||||
|
||||
# (dataframe['sine_1h'] < dataframe['leadsine_1h']) &
|
||||
# (dataframe['tsf_mid'] > dataframe['close']) &
|
||||
# (dataframe['ao'] > -5) &
|
||||
# (dataframe['rsi'] < 36) &
|
||||
(dataframe['rsi'] > 14) &
|
||||
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the sell signal for the given dataframe
|
||||
:param dataframe: DataFrame populated with indicators
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(qtpylib.crossed_below(dataframe['angle_tsf_mid'], 50)) &
|
||||
# (dataframe['sine_1h'] > dataframe['leadsine_1h']) &
|
||||
# (dataframe['sar_1d'] < dataframe['close']) &
|
||||
# (dataframe['tsf_mid'] < dataframe['close']) &
|
||||
(dataframe['rsi'] > 50) &
|
||||
(dataframe['ao'] < -10) &
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
@ -43,10 +43,11 @@ class ltcusdt_1h(IStrategy):
|
||||
# Minimal ROI designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "minimal_roi".
|
||||
minimal_roi = {
|
||||
"0": 0.04371,
|
||||
"300": 0.0461,
|
||||
"14487": 0.0254,
|
||||
"15960": 0
|
||||
"0": 0.11465,
|
||||
"607": 0.08395,
|
||||
"1517": 0.01583,
|
||||
"1780": 0
|
||||
|
||||
}
|
||||
order_types = {
|
||||
'buy': 'market',
|
||||
@ -56,14 +57,15 @@ class ltcusdt_1h(IStrategy):
|
||||
}
|
||||
# Optimal stoploss designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "stoploss".
|
||||
stoploss = -0.23
|
||||
stoploss = -0.23987
|
||||
|
||||
# Trailing stop:
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.11193
|
||||
trailing_stop_positive_offset = 0.20381
|
||||
trailing_stop_positive = 0.29213
|
||||
trailing_stop_positive_offset = 0.3379
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
|
||||
plot_config = {
|
||||
'main_plot': {
|
||||
'upperband': {'upperband': 'green'},
|
||||
@ -123,26 +125,30 @@ class ltcusdt_1h(IStrategy):
|
||||
"""
|
||||
|
||||
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
|
||||
slowperiod=26, signalperiod=7)
|
||||
slowperiod=24, signalperiod=7)
|
||||
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
|
||||
timeperiod=14)
|
||||
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=34)
|
||||
timeperiod=12)
|
||||
dataframe['uo'] = ULTOSC(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod1=7, timeperiod2=12,
|
||||
timeperiod3=24)
|
||||
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=36)
|
||||
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=48)
|
||||
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
|
||||
dataframe['sar_close'] = dataframe['sar'] - dataframe['close']
|
||||
dataframe['natr'] = NATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
|
||||
|
||||
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=10)
|
||||
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=14)
|
||||
dataframe['sine'], dataframe['leadsine'] = HT_SINE(dataframe['close'])
|
||||
dataframe['sine'] = dataframe['sine'].multiply(10)
|
||||
dataframe['leadsine'] = dataframe['leadsine'].multiply(10)
|
||||
dataframe['trend'] = HT_TRENDLINE(dataframe['close'])
|
||||
dataframe['mode'] = HT_TRENDMODE(dataframe['close'])
|
||||
dataframe['inphase'], dataframe['quadrature'] = HT_PHASOR(dataframe['close'])
|
||||
|
||||
dataframe['angle_trend_mid'] = LINEARREG_ANGLE(dataframe['trend'], timeperiod=10)
|
||||
dataframe['angle_trend_mid'] = LINEARREG_ANGLE(dataframe['trend'], timeperiod=12)
|
||||
|
||||
dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=12)
|
||||
dataframe['angle_macdsignal'] = LINEARREG_ANGLE(dataframe['macdsignal'], timeperiod=15)
|
||||
dataframe['angle_min'] = MIN(dataframe['angle_trend_mid'], timeperiod=7)
|
||||
dataframe['angle_min_lead'] = MIN(dataframe['angle_trend_mid'], timeperiod=3)
|
||||
dataframe['angle_max_lead'] = MAX(dataframe['angle_trend_mid'], timeperiod=3)
|
||||
dataframe['angle_max'] = MAX(dataframe['angle_trend_mid'], timeperiod=7)
|
||||
dataframe['angle_macdsignal'] = LINEARREG_ANGLE(dataframe['macdsignal'], timeperiod=12)
|
||||
|
||||
return dataframe
|
||||
|
||||
@ -155,12 +161,12 @@ class ltcusdt_1h(IStrategy):
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(qtpylib.crossed_above(dataframe['leadsine'], dataframe['sine'])) &
|
||||
|
||||
# (dataframe['sine_1h'] < dataframe['leadsine_1h']) &
|
||||
# (dataframe['tsf_mid'] > dataframe['close']) &
|
||||
(dataframe['ao'] > -5) &
|
||||
(dataframe['angle_tsf_mid'] > -3) &
|
||||
(qtpylib.crossed_above(dataframe['angle_trend_mid'], 0)) &
|
||||
(dataframe['angle'] > -48) &
|
||||
(-21 < dataframe['angle_macdsignal']) &
|
||||
(dataframe['uo'] > 9) &
|
||||
(-0.90098 < (dataframe['macd'] - dataframe['macdsignal'])) &
|
||||
(-50 < dataframe['ao']) &
|
||||
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
@ -177,13 +183,114 @@ class ltcusdt_1h(IStrategy):
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(qtpylib.crossed_below(dataframe['leadsine'], dataframe['sine'])) &
|
||||
# (dataframe['sine_1h'] > dataframe['leadsine_1h']) &
|
||||
# (dataframe['sar_1d'] < dataframe['close']) &
|
||||
# (dataframe['tsf_mid'] < dataframe['close']) &
|
||||
# (dataframe['natr'] > 2.5) &
|
||||
(dataframe['angle_tsf_mid'] < 5) &
|
||||
(qtpylib.crossed_below(dataframe['angle_trend_mid'], 0)) &
|
||||
(dataframe['uo'] > 10) &
|
||||
(dataframe['angle'] > 58) &
|
||||
(10 < dataframe['ao']) &
|
||||
|
||||
(-3.60339 < (dataframe['macd'] - dataframe['macdsignal'])) &
|
||||
(10 > dataframe['angle_trend_mid']) &
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
|
||||
|
||||
"""
|
||||
|
||||
freqtrade hyperopt --config user_data/config_ltcusdt_1h.json --hyperopt hyper_ltcusdt_1h --hyperopt-loss OnlyProfitHyperOptLoss --strategy ltcusdt_1h -e 500 --spaces all --timerange 20200601-
|
||||
|
||||
|
||||
+--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
|
||||
| Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
|
||||
|--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
|
||||
| * Best | 8/500 | 1 | 1 0 0 | 17.74% | 17.75689174 USDT (17.74%) | 660.0 m | 0.94087 |
|
||||
| * Best | 24/500 | 71 | 43 25 3 | 1.30% | 92.24310471 USDT (92.15%) | 1,950.4 m | 0.69283 |
|
||||
| Best | 98/500 | 52 | 34 15 3 | 2.56% | 133.14323340 USDT (133.01%) | 2,576.5 m | 0.55663 |
|
||||
| Best | 134/500 | 119 | 61 52 6 | 1.19% | 141.48585752 USDT (141.34%) | 2,884.5 m | 0.52885 |
|
||||
| Best | 149/500 | 120 | 63 48 9 | 1.22% | 146.96457805 USDT (146.82%) | 3,091.5 m | 0.51061 |
|
||||
| Best | 163/500 | 56 | 38 18 0 | 3.21% | 180.15476192 USDT (179.97%) | 4,530.0 m | 0.40008 |
|
||||
[Epoch 500 of 500 (100%)] || | [Time: 0:24:40, Elapsed Time: 0:24:40]
|
||||
2021-03-20 23:04:51,078 - freqtrade.optimize.hyperopt - INFO - 500 epochs saved to '/home/crypto_rahino/freqtrade/user_data/hyperopt_results/strategy_ltcusdt_1h_hyperopt_results_2021-03-20_22-40-07.pickle'.
|
||||
|
||||
Best result:
|
||||
|
||||
163/500: 56 trades. 38/18/0 Wins/Draws/Losses. Avg profit 3.21%. Median profit 1.58%. Total profit 180.15476192 USDT ( 179.97Σ%). Avg duration 4530.0 min. Objective: 0.40008
|
||||
|
||||
|
||||
# Buy hyperspace params:
|
||||
buy_params = {
|
||||
'angle-enabled': True,
|
||||
'angle-value': -48,
|
||||
'angle_macdsignal-enabled': True,
|
||||
'angle_macdsignal-value': -21,
|
||||
'angle_trend_mid-enabled': False,
|
||||
'angle_trend_mid-value': -9,
|
||||
'angle_tsf_mid-enabled': False,
|
||||
'angle_tsf_mid-value': -18,
|
||||
'ao-enabled': True,
|
||||
'ao-value': -50,
|
||||
'macd-enabled': True,
|
||||
'macd-value': -0.90098,
|
||||
'macdhist-enabled': False,
|
||||
'macdhist-value': 1.44126,
|
||||
'macdsignal-enabled': False,
|
||||
'macdsignal-value': 10,
|
||||
'mfi-enabled': False,
|
||||
'mfi-value': 63,
|
||||
'natr-enabled': False,
|
||||
'natr-value': 1.38067,
|
||||
'sar_close-enabled': False,
|
||||
'sar_close-value': -3,
|
||||
'trigger': 'angle_trend_mid',
|
||||
'uo-enabled': True,
|
||||
'uo-value': 9
|
||||
}
|
||||
|
||||
# Sell hyperspace params:
|
||||
sell_params = {
|
||||
'angle-enabled': True,
|
||||
'angle-value_sell': 58,
|
||||
'angle_macdsignal-enabled': True,
|
||||
'angle_macdsignal-value_sell': -14,
|
||||
'angle_trend_mid-enabled': False,
|
||||
'angle_trend_mid-value_sell': -73,
|
||||
'angle_tsf_mid-enabled': False,
|
||||
'angle_tsf_mid-value_sell': -11,
|
||||
'ao-enabled': True,
|
||||
'ao-value_sell': 10,
|
||||
'macd-enabled': True,
|
||||
'macd-value_sell': -3.60339,
|
||||
'macdhist-enabled': False,
|
||||
'macdhist-value_sell': 4.06627,
|
||||
'macdsignal-enabled': False,
|
||||
'macdsignal-value_sell': -1,
|
||||
'mfi-enabled': False,
|
||||
'mfi-value_sell': 13,
|
||||
'natr-enabled': False,
|
||||
'natr-value_sell': 7.852,
|
||||
'sar_close-enabled': False,
|
||||
'sar_close-value_sell': 17,
|
||||
'trigger': 'angle_trend_mid',
|
||||
'uo-enabled': True,
|
||||
'uo-value_sell': 10
|
||||
}
|
||||
|
||||
# ROI table:
|
||||
minimal_roi = {
|
||||
"0": 0.11465,
|
||||
"607": 0.08395,
|
||||
"1517": 0.01583,
|
||||
"1780": 0
|
||||
}
|
||||
|
||||
# Stoploss:
|
||||
stoploss = -0.20987
|
||||
|
||||
# Trailing stop:
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.29213
|
||||
trailing_stop_positive_offset = 0.3379
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
"""
|
||||
|
||||
@ -4,8 +4,10 @@
|
||||
import numpy as np # noqa
|
||||
import pandas as pd # noqa
|
||||
from pandas import DataFrame
|
||||
from talib._ta_lib import ULTOSC, MACD, SAR, LINEARREG_ANGLE, TEMA, STOCHRSI, STOCH, STOCHF, RSI
|
||||
from talib._ta_lib import ULTOSC, MACD, SAR, LINEARREG_ANGLE, TEMA, TSF, CCI, ATR, CORREL, \
|
||||
BOP, WMA, KAMA, HT_DCPERIOD, HT_TRENDMODE, HT_SINE
|
||||
|
||||
from freqtrade.strategy import merge_informative_pair
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
|
||||
# --------------------------------
|
||||
@ -33,38 +35,36 @@ class quick_btcusdt_1m(IStrategy):
|
||||
"""
|
||||
# Strategy interface version - allow new iterations of the strategy interface.
|
||||
# Check the documentation or the Sample strategy to get the latest version.
|
||||
INTERFACE_VERSION = 1
|
||||
INTERFACE_VERSION = 2
|
||||
|
||||
# Minimal ROI designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "minimal_roi".
|
||||
minimal_roi = {
|
||||
"0": 0.07186329732926479,
|
||||
"6": 0.03610260437996321,
|
||||
"14": 0.014117594921808408,
|
||||
"23": 0
|
||||
"0": 0.06443,
|
||||
"360": 0.06597,
|
||||
"1790": 0.0108,
|
||||
"2116": 0
|
||||
|
||||
}
|
||||
|
||||
# Optimal stoploss designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "stoploss".
|
||||
stoploss = -0.073946396013718
|
||||
# Stoploss:
|
||||
stoploss = -0.15825
|
||||
|
||||
# Trailing stoploss
|
||||
# Trailing stop:
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.11645094244761
|
||||
trailing_stop_positive_offset = 0.20201226976340847
|
||||
trailing_stop_positive = 0.3274
|
||||
trailing_stop_positive_offset = 0.38967
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
|
||||
# Optimal ticker interval for the strategy.
|
||||
timeframe = '1m'
|
||||
|
||||
# Run "populate_indicators()" only for new candle.
|
||||
process_only_new_candles = False
|
||||
|
||||
# These values can be overridden in the "ask_strategy" section in the config.
|
||||
use_sell_signal = True
|
||||
sell_profit_only = True
|
||||
ignore_roi_if_buy_signal = False
|
||||
|
||||
# Number of candles the strategy requires before producing valid signals
|
||||
startup_candle_count: int = 30
|
||||
|
||||
@ -78,8 +78,8 @@ class quick_btcusdt_1m(IStrategy):
|
||||
|
||||
# Optional order time in force.
|
||||
order_time_in_force = {
|
||||
'buy': 'gtc',
|
||||
'sell': 'gtc'
|
||||
'buy': 'fok',
|
||||
'sell': 'fok'
|
||||
}
|
||||
|
||||
plot_config = {
|
||||
@ -109,7 +109,7 @@ class quick_btcusdt_1m(IStrategy):
|
||||
("BTC/USDT", "15m"),
|
||||
]
|
||||
"""
|
||||
return [("BTC/USDT", "1m")]
|
||||
return [("BTC/USDT", "1h")]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
@ -122,38 +122,39 @@ class quick_btcusdt_1m(IStrategy):
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
"""
|
||||
|
||||
# MACD
|
||||
dataframe['period'] = HT_DCPERIOD(dataframe['close'])
|
||||
|
||||
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
|
||||
slowperiod=26, signalperiod=9)
|
||||
dataframe['macd_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
|
||||
dataframe['macdhist_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
|
||||
dataframe['cci'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
|
||||
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
|
||||
dataframe['wma'] = WMA(dataframe['close'], timeperiod=30)
|
||||
dataframe['wma_ratio'] = (dataframe['close'] - dataframe['wma'])
|
||||
dataframe['kama'] = KAMA(dataframe['close'], timeperiod=30)
|
||||
dataframe['angle_kama'] = LINEARREG_ANGLE(dataframe['kama'], timeperiod=10)
|
||||
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=30)
|
||||
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=10)
|
||||
dataframe['atr'] = ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
|
||||
dataframe['uo'] = ULTOSC(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod1=7, timeperiod2=14,
|
||||
timeperiod3=28)
|
||||
dataframe['tema'] = TEMA(dataframe['close'], timeperiod=50)
|
||||
dataframe['macd_ratio'] = (dataframe['macd'] - dataframe['macdsignal'])
|
||||
dataframe['tsf_ratio'] = (dataframe['tsf_mid'] - dataframe['close'])
|
||||
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
|
||||
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=12)
|
||||
dataframe['bop'] = BOP(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
|
||||
|
||||
# Linear angle
|
||||
dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=14)
|
||||
if not self.dp:
|
||||
# Don't do anything if DataProvider is not available.
|
||||
return dataframe
|
||||
# Get the informative pair
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
|
||||
informative['bop'] = BOP(informative['open'], informative['high'], informative['low'], informative['close'])
|
||||
informative['period'] = HT_DCPERIOD(informative['close'])
|
||||
informative['mode'] = HT_TRENDMODE(informative['close'])
|
||||
informative['sine'], informative['leadsine'] = HT_SINE(informative['close'])
|
||||
|
||||
dataframe['tema'] = TEMA(dataframe['close'], timeperiod=30)
|
||||
dataframe['sr_fastk'], dataframe['sr_fastd'] = STOCHRSI(dataframe['close'], timeperiod=14, fastk_period=5,
|
||||
fastd_period=3, fastd_matype=0)
|
||||
dataframe['sr_fastd_angle'] = LINEARREG_ANGLE(dataframe['sr_fastd'], timeperiod=4)
|
||||
|
||||
dataframe['slowk'], dataframe['slowd'] = STOCH(dataframe['high'], dataframe['low'], dataframe['close'],
|
||||
fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3,
|
||||
slowd_matype=0)
|
||||
dataframe['slowd_angle'] = LINEARREG_ANGLE(dataframe['slowd'], timeperiod=3)
|
||||
dataframe['sf_fastk'], dataframe['sf_fastd'] = STOCHF(dataframe['high'], dataframe['low'], dataframe['close'], fastk_period=5, fastd_period=3, fastd_matype=0)
|
||||
dataframe['sf_fastd_angle'] = LINEARREG_ANGLE(dataframe['sf_fastd'], timeperiod=3)
|
||||
|
||||
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['rsi_angle'] = LINEARREG_ANGLE(dataframe['rsi'], timeperiod=5)
|
||||
|
||||
|
||||
# # first check if dataprovider is available
|
||||
# if self.dp:
|
||||
# if self.dp.runmode in ('live', 'dry_run'):
|
||||
# ob = self.dp.orderbook(metadata['pair'], 1)
|
||||
# dataframe['best_bid'] = ob['bids'][0][0]
|
||||
# dataframe['best_ask'] = ob['asks'][0][0]
|
||||
#
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '1h', ffill=True)
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
@ -165,12 +166,8 @@ class quick_btcusdt_1m(IStrategy):
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) &
|
||||
(dataframe['sr_fastd_angle'] > -39) &
|
||||
(dataframe['sf_fastk'] > 33) &
|
||||
(dataframe['sf_fastd_angle'] > -80) &
|
||||
(dataframe['rsi_angle'] > 24) &
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
(qtpylib.crossed_above(dataframe['low'], dataframe['tsf_mid']))
|
||||
|
||||
),
|
||||
'buy'] = 1
|
||||
|
||||
@ -185,12 +182,102 @@ class quick_btcusdt_1m(IStrategy):
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
((dataframe['macdhist']) < 0.000241872676925719) &
|
||||
(dataframe['sr_fastd_angle'] > 71) &
|
||||
(dataframe['sf_fastd_angle'] < 21) &
|
||||
(dataframe['rsi_angle'] < 1) &
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
(qtpylib.crossed_below(dataframe['high'], dataframe['tsf_mid']))
|
||||
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
|
||||
|
||||
"""
|
||||
freqtrade hyperopt --config user_data/config_btcusdt_1m.json --hyperopt hyper_btcusdt_1m --hyperopt-loss OnlyProfitHyperOptLoss --strategy quick_btcusdt_1m -e 500 --spaces all
|
||||
|
||||
+--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
|
||||
| Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
|
||||
|--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
|
||||
| * Best | 1/500 | 2 | 1 0 1 | -3.39% | -67.10930706 USDT (-6.77%) | 4,154.0 m | 1.02257 |
|
||||
| * Best | 2/500 | 2 | 1 0 1 | 0.08% | 1.60699817 USDT (0.16%) | 1,156.5 m | 0.99946 |
|
||||
| * Best | 8/500 | 4 | 4 0 0 | 3.59% | 142.17731149 USDT (14.35%) | 981.5 m | 0.95218 |
|
||||
| Best | 39/500 | 13 | 11 0 2 | 3.80% | 489.61841267 USDT (49.41%) | 736.2 m | 0.83531 |
|
||||
| Best | 70/500 | 25 | 19 4 2 | 2.99% | 740.67168932 USDT (74.74%) | 1,597.7 m | 0.75086 |
|
||||
| Best | 106/500 | 52 | 35 12 5 | 1.82% | 937.66182441 USDT (94.62%) | 1,666.9 m | 0.6846 |
|
||||
| Best | 427/500 | 52 | 32 17 3 | 2.83% | 1,460.67464976 USDT (147.40%) | 2,592.9 m | 0.50868 |
|
||||
| Best | 439/500 | 179 | 101 57 21 | 0.83% | 1,480.44117660 USDT (149.39%) | 3,457.9 m | 0.50203 |
|
||||
[Epoch 500 of 500 (100%)] || | [Time: 1:22:09, Elapsed Time: 1:22:09]
|
||||
2021-03-26 22:31:08,615 - freqtrade.optimize.hyperopt - INFO - 500 epochs saved to '/home/crypto_rahino/freqtrade/user_data/hyperopt_results/strategy_quick_btcusdt_1m_hyperopt_results_2021-03-26_21-08-16.pickle'.
|
||||
|
||||
Best result:
|
||||
|
||||
439/500: 179 trades. 101/57/21 Wins/Draws/Losses. Avg profit 0.83%. Median profit 0.82%. Total profit 1480.44117660 USDT ( 149.39Σ%). Avg duration 3457.9 min. Objective: 0.50203
|
||||
|
||||
|
||||
# Buy hyperspace params:
|
||||
buy_params = {
|
||||
'angle_tsf_mid-enabled': False,
|
||||
'angle_tsf_mid-value': 9,
|
||||
'atr-enabled': False,
|
||||
'atr-value': 180,
|
||||
'bop-value': 0.7274,
|
||||
'cci-enabled': False,
|
||||
'cci-value': 57,
|
||||
'correl_h_l-enabled': True,
|
||||
'correl_h_l-value': -0.5389,
|
||||
'correl_tsf_mid_close-enabled': False,
|
||||
'correl_tsf_mid_close-value': -0.8364,
|
||||
'macd_ratio-enabled': False,
|
||||
'macd_ratio-value': 101,
|
||||
'macdhist-enabled': True,
|
||||
'macdhist-value': 22,
|
||||
'macdsignal-enabled': False,
|
||||
'macdsignal-value': -478,
|
||||
'trigger': 'macd',
|
||||
'tsf_ratio-enabled': False,
|
||||
'tsf_ratio-value': -1323,
|
||||
'uo-enabled': False,
|
||||
'uo-value': 33.5021
|
||||
}
|
||||
|
||||
# Sell hyperspace params:
|
||||
sell_params = {
|
||||
'angle_tsf_mid-enabled': False,
|
||||
'angle_tsf_mid-value': 9,
|
||||
'atr-enabled': False,
|
||||
'atr-value': 180,
|
||||
'cci-enabled': False,
|
||||
'cci-value': 57,
|
||||
'correl_h_l-enabled': True,
|
||||
'correl_h_l-value': -0.5389,
|
||||
'correl_tsf_mid_close-enabled': False,
|
||||
'correl_tsf_mid_close-value': -0.8364,
|
||||
'macd_ratio-enabled': False,
|
||||
'macd_ratio-value': 101,
|
||||
'macdhist-enabled': True,
|
||||
'macdhist-value': 22,
|
||||
'macdsignal-enabled': False,
|
||||
'macdsignal-value': -478,
|
||||
'trigger': 'macd',
|
||||
'tsf_ratio-enabled': False,
|
||||
'tsf_ratio-value': -1323,
|
||||
'uo-enabled': False,
|
||||
'uo-value': 33.5021
|
||||
}
|
||||
|
||||
# ROI table:
|
||||
minimal_roi = {
|
||||
"0": 0.16344,
|
||||
"793": 0.05931,
|
||||
"1121": 0.03143,
|
||||
"1474": 0
|
||||
}
|
||||
|
||||
# Stoploss:
|
||||
stoploss = -0.2884
|
||||
|
||||
# Trailing stop:
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.21554
|
||||
trailing_stop_positive_offset = 0.23749
|
||||
trailing_only_offset_is_reached = False
|
||||
|
||||
|
||||
"""
|
||||
Loading…
Reference in New Issue
Block a user