cleanup: remove 23 duplicate meta.jsons, restructure strategies/ by version
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{"StructureFlowScalp":{"run_id":"5d9dbcad66211a164ff6ea751da905abf504163e","backtest_start_time":1781075231,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"0668e50d1dd7394da760d48c358fb89d9940b1a1","backtest_start_time":1781075580,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"dfb6675634b8f42b210664b4c725945953d69c38","backtest_start_time":1781075697,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"7e1a878d1791bcd1831a1606377d8f7f1f9c0b11","backtest_start_time":1781076774,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"7c8a6f0cafe3a24cd12498907194a9f13dfc9f0a","backtest_start_time":1781077325,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"611c4e0a697035bd2fe70b7fb5de30c17f4348ce","backtest_start_time":1781078709,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"ec43a348e14d2e8a0fdd7185d44a06c81c268a20","backtest_start_time":1781078822,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"40f3de7a7034b27ed88379d85c9004a6bdeda26a","backtest_start_time":1781078915,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"5006df21d4680b5858c830464277152b62693f2d","backtest_start_time":1781079411,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1778803200,"backtest_end_ts":1781049600}}
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{"StructureFlowScalp":{"run_id":"8c9173b91d5557afcc9b0a2f22579422a79d5433","backtest_start_time":1781079721,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1704067200}}
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{"StructureFlowMomentumScalp":{"run_id":"55e90e69bb662d449103b9620f51e93c6346fe80","backtest_start_time":1781080191,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1704067200}}
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{"StructureFlowMomentumScalp":{"run_id":"961e38d12b56872445e5f4f32b6a9db3cf560e6f","backtest_start_time":1781080317,"timeframe":"5m","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1704067200}}
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{"StructureFlowSwingV30":{"run_id":"3f57b6fb50a476c00713b62bd67ab20bd90c4910","backtest_start_time":1781095529,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1703980800}}
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{"StructureFlowSwingV31":{"run_id":"014572729a6218a457183a82370acffdd8e774c5","backtest_start_time":1781096292,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1703980800}}
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{"StructureFlowSwingV31":{"run_id":"7dc34d1e93588cd38be7826605224908c275ae4f","backtest_start_time":1781097736,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1735689600,"backtest_end_ts":1767139200}}
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{"StructureFlowSwingV31":{"run_id":"294bccacc1613b0ef19c3bb6e9542436c02d857d","backtest_start_time":1781097759,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1751328000,"backtest_end_ts":1756598400}}
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{"StructureFlowSwingV32":{"run_id":"f84aa424a0c90ffb223bb6fc5e38f9a10f67db9a","backtest_start_time":1781099073,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1703980800}}
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{"StructureFlowSwingV32":{"run_id":"4ec9b78473c02a5ceb38b37d87c539bf2312b18c","backtest_start_time":1781099114,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1703980800}}
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{"StructureFlowSwingV32":{"run_id":"c666105d00ba25635608f1fd7d581009b616535a","backtest_start_time":1781099393,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1735689600,"backtest_end_ts":1767139200}}
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{"StructureFlowSwingV32":{"run_id":"d2626584bb8172a85399c4dcb0972772a9f0621f","backtest_start_time":1781099393,"timeframe":"4h","timeframe_detail":null,"backtest_start_ts":1672531200,"backtest_end_ts":1703980800}}
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{"StructureFlowStrategyV22d":{"run_id":"dadac4a0466e755cc789643df6326a91a1d44622","backtest_start_time":1781159624,"timeframe":"1h","timeframe_detail":null,"backtest_start_ts":1780963200,"backtest_end_ts":1781136000}}
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{"StructureFlowStrategyV22d":{"run_id":"a486f604bb6528d007ee6459e8b6738e66aa582e","backtest_start_time":1781159650,"timeframe":"1h","timeframe_detail":null,"backtest_start_ts":1780963200,"backtest_end_ts":1781136000}}
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{"StructureFlowStrategyV22d":{"run_id":"70aaa0ea25ba897e4b392d156ccfff724d968d5e","backtest_start_time":1781159710,"timeframe":"1h","timeframe_detail":null,"backtest_start_ts":1780963200,"backtest_end_ts":1781136000}}
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2288
backtests/full/v2_2c_full_2021_2026.txt
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backtests/full/v2_2c_full_2021_2026.txt
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backtests/full/v2_2d_full_2021_2026.txt
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backtests/full/v2_2d_full_2021_2026.txt
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# flake8: noqa: F401
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# isort: skip_file
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# --- Do not remove these imports ---
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta, timezone
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from pandas import DataFrame
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from typing import Optional, Union
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from freqtrade.strategy import (
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IStrategy,
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Trade,
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Order,
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PairLocks,
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informative, # @informative decorator
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# Hyperopt Parameters
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BooleanParameter,
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CategoricalParameter,
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DecimalParameter,
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IntParameter,
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RealParameter,
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# timeframe helpers
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timeframe_to_minutes,
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timeframe_to_next_date,
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timeframe_to_prev_date,
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# Strategy helper functions
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merge_informative_pair,
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stoploss_from_absolute,
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stoploss_from_open,
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)
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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from technical import qtpylib
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# This class is a sample. Feel free to customize it.
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class SampleStrategy(IStrategy):
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"""
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This is a sample strategy to inspire you.
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More information in https://www.freqtrade.io/en/stable/strategy-customization/
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You can:
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:return: a Dataframe with all mandatory indicators for the strategies
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- Rename the class name (Do not forget to update class_name)
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- Add any methods you want to build your strategy
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- Add any lib you need to build your strategy
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You must keep:
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- the lib in the section "Do not remove these libs"
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- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
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You should keep:
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- timeframe, minimal_roi, stoploss, trailing_*
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"""
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 3
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# Can this strategy go short?
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can_short: bool = False
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi".
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minimal_roi = {
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# "120": 0.0, # exit after 120 minutes at break even
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"60": 0.01,
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"30": 0.02,
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"0": 0.04,
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}
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# Optimal stoploss designed for the strategy.
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# This attribute will be overridden if the config file contains "stoploss".
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stoploss = -0.10
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# Trailing stoploss
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trailing_stop = False
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# trailing_only_offset_is_reached = False
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# trailing_stop_positive = 0.01
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# trailing_stop_positive_offset = 0.0 # Disabled / not configured
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# Optimal timeframe for the strategy.
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timeframe = "5m"
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = True
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# These values can be overridden in the config.
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use_exit_signal = True
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exit_profit_only = False
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ignore_roi_if_entry_signal = False
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# Hyperoptable parameters
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buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
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short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
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exit_short_rsi = IntParameter(
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low=1, high=50, default=30, space="exit", optimize=True, load=True
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)
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 200
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# Optional order type mapping.
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order_types = {
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"entry": "limit",
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"exit": "limit",
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"stoploss": "market",
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"stoploss_on_exchange": False,
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}
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# Optional order time in force.
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order_time_in_force = {"entry": "GTC", "exit": "GTC"}
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plot_config = {
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"main_plot": {
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"tema": {},
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"sar": {"color": "white"},
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},
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"subplots": {
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"MACD": {
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"macd": {"color": "blue"},
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"macdsignal": {"color": "orange"},
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},
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"RSI": {
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"rsi": {"color": "red"},
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},
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},
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}
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Dataframe with data from the exchange
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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# Momentum Indicators
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# ------------------------------------
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# ADX
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dataframe["adx"] = ta.ADX(dataframe)
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# # Plus Directional Indicator / Movement
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# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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# # Minus Directional Indicator / Movement
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# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# # Aroon, Aroon Oscillator
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# aroon = ta.AROON(dataframe)
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# dataframe['aroonup'] = aroon['aroonup']
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# dataframe['aroondown'] = aroon['aroondown']
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# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
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# # Awesome Oscillator
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# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# # Keltner Channel
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# keltner = qtpylib.keltner_channel(dataframe)
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# dataframe["kc_upperband"] = keltner["upper"]
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# dataframe["kc_lowerband"] = keltner["lower"]
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# dataframe["kc_middleband"] = keltner["mid"]
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# dataframe["kc_percent"] = (
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# (dataframe["close"] - dataframe["kc_lowerband"]) /
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
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# )
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# dataframe["kc_width"] = (
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
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# )
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# # Ultimate Oscillator
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# dataframe['uo'] = ta.ULTOSC(dataframe)
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# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
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# dataframe['cci'] = ta.CCI(dataframe)
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# RSI
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dataframe["rsi"] = ta.RSI(dataframe)
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# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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# rsi = 0.1 * (dataframe['rsi'] - 50)
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# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
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# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
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# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# # Stochastic Slow
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# stoch = ta.STOCH(dataframe)
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# dataframe['slowd'] = stoch['slowd']
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# dataframe['slowk'] = stoch['slowk']
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# Stochastic Fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe["fastd"] = stoch_fast["fastd"]
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dataframe["fastk"] = stoch_fast["fastk"]
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# # Stochastic RSI
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# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
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# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
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# stoch_rsi = ta.STOCHRSI(dataframe)
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# dataframe['fastd_rsi'] = stoch_rsi['fastd']
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# dataframe['fastk_rsi'] = stoch_rsi['fastk']
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# MACD
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macd = ta.MACD(dataframe)
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dataframe["macd"] = macd["macd"]
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dataframe["macdsignal"] = macd["macdsignal"]
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dataframe["macdhist"] = macd["macdhist"]
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# MFI
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dataframe["mfi"] = ta.MFI(dataframe)
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# # ROC
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# dataframe['roc'] = ta.ROC(dataframe)
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# Overlap Studies
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# ------------------------------------
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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|
||||||
dataframe["bb_upperband"] = bollinger["upper"]
|
|
||||||
dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
|
|
||||||
dataframe["bb_upperband"] - dataframe["bb_lowerband"]
|
|
||||||
)
|
|
||||||
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
|
|
||||||
"bb_middleband"
|
|
||||||
]
|
|
||||||
|
|
||||||
# Bollinger Bands - Weighted (EMA based instead of SMA)
|
|
||||||
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
|
|
||||||
# qtpylib.typical_price(dataframe), window=20, stds=2
|
|
||||||
# )
|
|
||||||
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
|
|
||||||
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
|
|
||||||
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
|
|
||||||
# dataframe["wbb_percent"] = (
|
|
||||||
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
|
|
||||||
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
|
|
||||||
# )
|
|
||||||
# dataframe["wbb_width"] = (
|
|
||||||
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
|
|
||||||
# dataframe["wbb_middleband"]
|
|
||||||
# )
|
|
||||||
|
|
||||||
# # EMA - Exponential Moving Average
|
|
||||||
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
|
|
||||||
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
|
|
||||||
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
|
||||||
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
|
|
||||||
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
|
||||||
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
|
|
||||||
|
|
||||||
# # SMA - Simple Moving Average
|
|
||||||
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
|
|
||||||
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
|
|
||||||
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
|
|
||||||
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
|
|
||||||
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
|
|
||||||
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
|
|
||||||
|
|
||||||
# Parabolic SAR
|
|
||||||
dataframe["sar"] = ta.SAR(dataframe)
|
|
||||||
|
|
||||||
# TEMA - Triple Exponential Moving Average
|
|
||||||
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
|
|
||||||
|
|
||||||
# Cycle Indicator
|
|
||||||
# ------------------------------------
|
|
||||||
# Hilbert Transform Indicator - SineWave
|
|
||||||
hilbert = ta.HT_SINE(dataframe)
|
|
||||||
dataframe["htsine"] = hilbert["sine"]
|
|
||||||
dataframe["htleadsine"] = hilbert["leadsine"]
|
|
||||||
|
|
||||||
# Pattern Recognition - Bullish candlestick patterns
|
|
||||||
# ------------------------------------
|
|
||||||
# # Hammer: values [0, 100]
|
|
||||||
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
|
|
||||||
# # Inverted Hammer: values [0, 100]
|
|
||||||
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
|
|
||||||
# # Dragonfly Doji: values [0, 100]
|
|
||||||
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
|
|
||||||
# # Piercing Line: values [0, 100]
|
|
||||||
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
|
|
||||||
# # Morningstar: values [0, 100]
|
|
||||||
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
|
|
||||||
# # Three White Soldiers: values [0, 100]
|
|
||||||
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
|
|
||||||
|
|
||||||
# Pattern Recognition - Bearish candlestick patterns
|
|
||||||
# ------------------------------------
|
|
||||||
# # Hanging Man: values [0, 100]
|
|
||||||
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
|
|
||||||
# # Shooting Star: values [0, 100]
|
|
||||||
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
|
|
||||||
# # Gravestone Doji: values [0, 100]
|
|
||||||
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
|
|
||||||
# # Dark Cloud Cover: values [0, 100]
|
|
||||||
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
|
|
||||||
# # Evening Doji Star: values [0, 100]
|
|
||||||
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
|
|
||||||
# # Evening Star: values [0, 100]
|
|
||||||
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
|
|
||||||
|
|
||||||
# Pattern Recognition - Bullish/Bearish candlestick patterns
|
|
||||||
# ------------------------------------
|
|
||||||
# # Three Line Strike: values [0, -100, 100]
|
|
||||||
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
|
|
||||||
# # Spinning Top: values [0, -100, 100]
|
|
||||||
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
|
|
||||||
# # Engulfing: values [0, -100, 100]
|
|
||||||
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
|
|
||||||
# # Harami: values [0, -100, 100]
|
|
||||||
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
|
|
||||||
# # Three Outside Up/Down: values [0, -100, 100]
|
|
||||||
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
|
|
||||||
# # Three Inside Up/Down: values [0, -100, 100]
|
|
||||||
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
|
|
||||||
|
|
||||||
# # Chart type
|
|
||||||
# # ------------------------------------
|
|
||||||
# # Heikin Ashi Strategy
|
|
||||||
# heikinashi = qtpylib.heikinashi(dataframe)
|
|
||||||
# dataframe['ha_open'] = heikinashi['open']
|
|
||||||
# dataframe['ha_close'] = heikinashi['close']
|
|
||||||
# dataframe['ha_high'] = heikinashi['high']
|
|
||||||
# dataframe['ha_low'] = heikinashi['low']
|
|
||||||
|
|
||||||
# Retrieve best bid and best ask from the orderbook
|
|
||||||
# ------------------------------------
|
|
||||||
"""
|
|
||||||
# first check if dataprovider is available
|
|
||||||
if self.dp:
|
|
||||||
if self.dp.runmode.value 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]
|
|
||||||
"""
|
|
||||||
|
|
||||||
return dataframe
|
|
||||||
|
|
||||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
||||||
"""
|
|
||||||
Based on TA indicators, populates the entry signal for the given dataframe
|
|
||||||
:param dataframe: DataFrame
|
|
||||||
:param metadata: Additional information, like the currently traded pair
|
|
||||||
:return: DataFrame with entry columns populated
|
|
||||||
"""
|
|
||||||
dataframe.loc[
|
|
||||||
(
|
|
||||||
# Signal: RSI crosses above 30
|
|
||||||
(qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
|
|
||||||
& (dataframe["tema"] <= dataframe["bb_middleband"]) # Guard: tema below BB middle
|
|
||||||
& (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
|
|
||||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
|
||||||
),
|
|
||||||
"enter_long",
|
|
||||||
] = 1
|
|
||||||
|
|
||||||
dataframe.loc[
|
|
||||||
(
|
|
||||||
# Signal: RSI crosses above 70
|
|
||||||
(qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value))
|
|
||||||
& (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
|
|
||||||
& (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
|
|
||||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
|
||||||
),
|
|
||||||
"enter_short",
|
|
||||||
] = 1
|
|
||||||
|
|
||||||
return dataframe
|
|
||||||
|
|
||||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
||||||
"""
|
|
||||||
Based on TA indicators, populates the exit signal for the given dataframe
|
|
||||||
:param dataframe: DataFrame
|
|
||||||
:param metadata: Additional information, like the currently traded pair
|
|
||||||
:return: DataFrame with exit columns populated
|
|
||||||
"""
|
|
||||||
dataframe.loc[
|
|
||||||
(
|
|
||||||
# Signal: RSI crosses above 70
|
|
||||||
(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
|
|
||||||
& (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
|
|
||||||
& (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
|
|
||||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
|
||||||
),
|
|
||||||
"exit_long",
|
|
||||||
] = 1
|
|
||||||
|
|
||||||
dataframe.loc[
|
|
||||||
(
|
|
||||||
# Signal: RSI crosses above 30
|
|
||||||
(qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value))
|
|
||||||
&
|
|
||||||
# Guard: tema below BB middle
|
|
||||||
(dataframe["tema"] <= dataframe["bb_middleband"])
|
|
||||||
& (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
|
|
||||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
|
||||||
),
|
|
||||||
"exit_short",
|
|
||||||
] = 1
|
|
||||||
|
|
||||||
return dataframe
|
|
||||||
0
strategies/current/.gitkeep
Normal file
0
strategies/current/.gitkeep
Normal file
1
strategies/current/structure_flow_strategy_v2_2d.py
Symbolic link
1
strategies/current/structure_flow_strategy_v2_2d.py
Symbolic link
@ -0,0 +1 @@
|
|||||||
|
../v2.2d/structure_flow_strategy_v2_2d.py
|
||||||
Reference in New Issue
Block a user