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✨ New indicators + minor roi,lookback candles adjustment. #140

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Sep 23, 2021
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✨ New indicators + minor roi,lookback candles adjustment. #140

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3b2a357
✨ Indicators update
Sep 12, 2021
2890549
✨ Indicators update
Sep 12, 2021
38b6f0c
✨ Roi Adjustments
Sep 12, 2021
72981d0
✨ Roi + max_trend_total_signal_needed_candles_lookback_window_value a…
Sep 12, 2021
2ec808f
♻️ Refactored `MoniGoManiHyperStrategy`
Rikj000 Sep 12, 2021
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6 changes: 3 additions & 3 deletions user_data/mgm-config.example.json
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
"max_weighted_signal_value": 100,
"min_trend_total_signal_needed_value": 60,
"min_trend_total_signal_needed_candles_lookback_window_value": 1,
"max_trend_total_signal_needed_candles_lookback_window_value": 6,
"max_trend_total_signal_needed_candles_lookback_window_value": 8,
"min_trend_signal_triggers_needed": 2,
"search_threshold_weighted_signal_values": 30,
"search_threshold_trend_total_signal_needed_candles_lookback_window_value": 1,
Expand All @@ -34,8 +34,8 @@
},
"roi_spaces": {
"roi_table_step_size": 5,
"roi_time_interval_scaling": 1.0,
"roi_value_step_scaling": 1.0
"roi_time_interval_scaling": 1.6,
"roi_value_step_scaling": 0.9
},
"unclogger_spaces": {
"unclogger_buy_cooldown_minutes_window": {
Expand Down
74 changes: 34 additions & 40 deletions user_data/strategies/MoniGoManiHyperStrategy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,59 +14,53 @@
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.constants import ListPairsWithTimeframes


# Master Framework file must reside in same folder as Strategy file
sys.path.append(str(Path(__file__).parent))
from MasterMoniGoManiHyperStrategy import MasterMoniGoManiHyperStrategy
# ---- ↑ Do not remove these libs ↑ ------------------------------------------------------------------------------------

# Define the Weighted Buy Signals to be used by MGM
buy_signals = {
# Weighted Buy Signal: ADX above 25 & +DI above -DI (The trend has strength while moving up)
'adx_strong_up': lambda df: (df['adx'] > 25),
# Weighted Buy Signal: Re-Entering Lower Bollinger Band after downward breakout
'bollinger_bands': lambda df: (qtpylib.crossed_above(df['close'], df['bb_lowerband'])),
# Weighted Buy Signal: EMA long term Golden Cross (Medium term EMA crosses above Long term EMA)
'ema_long_golden_cross': lambda df: (qtpylib.crossed_above(df['ema50'], df['ema200'])),
# Weighted Buy Signal: EMA short term Golden Cross (Short term EMA crosses above Medium term EMA)
'ema_short_golden_cross': lambda df: (qtpylib.crossed_above(df['ema9'], df['ema50'])),
# Weighted Buy Signal: MACD above Signal
'macd': lambda df: (df['macd'] > df['macdsignal']),
# Weighted Buy Signal: MFI crosses above 20 (Under-bought / low-price and rising indication)
'mfi': lambda df: (qtpylib.crossed_above(df['mfi'], 20)),
# Weighted Buy Signal: RSI crosses above 30 (Under-bought / low-price and rising indication)
'rsi': lambda df: (qtpylib.crossed_above(df['rsi'], 30)),
# Weighted Buy Signal: VWAP crosses above current price
'vwap_cross': lambda df: (qtpylib.crossed_above(df['vwap'], df['close'])),
# Weighted Buy Signal: Price crosses above Parabolic SAR
'sar_cross': lambda df: (qtpylib.crossed_above(df['sar'], df['close'])),
# Weighted Buy Signal: Stochastic Slow below 20 (Under-bought, indication of starting to move up)
'stoch': lambda df: (df['slowk'] < 20),
# Weighted Buy Signal: SMA long term Golden Cross (Medium term SMA crosses above Long term SMA)
'sma_long_golden_cross': lambda df: (qtpylib.crossed_above(df['sma50'], df['sma200'])),
# Weighted Buy Signal: SMA short term Golden Cross (Short term SMA crosses above Medium term SMA)
'sma_short_golden_cross': lambda df: (qtpylib.crossed_above(df['sma9'], df['sma50'])),
# Weighted Sell Signal: VWAP crosses above current price
'vwap_cross': lambda df: (qtpylib.crossed_above(df['vwap'], df['close']))
# Weighted Buy Signal: TEMA
'tema': lambda df: (df['tema'] <= df['bb_middleband']) & (df['tema'] > df['tema'].shift(1))
}

# Define the Weighted Sell Signals to be used by MGM
sell_signals = {
# Weighted Sell Signal: ADX above 25 & +DI below -DI (The trend has strength while moving down)
'adx_strong_down': lambda df: (df['adx'] > 25),
# Weighted Sell Signal: Re-Entering Upper Bollinger Band after upward breakout
'bollinger_bands': lambda df: (qtpylib.crossed_below(df['close'], df['bb_upperband'])),
# Weighted Sell Signal: EMA long term Death Cross (Medium term EMA crosses below Long term EMA)
'ema_long_death_cross': lambda df: (qtpylib.crossed_below(df['ema50'], df['ema200'])),
# Weighted Sell Signal: EMA short term Death Cross (Short term EMA crosses below Medium term EMA)
'ema_short_death_cross': lambda df: (qtpylib.crossed_below(df['ema9'], df['ema50'])),
# Weighted Sell Signal: MACD below Signal
'macd': lambda df: (df['macd'] < df['macdsignal']),
# Weighted Sell Signal: MFI crosses below 80 (Over-bought / high-price and dropping indication)
'mfi': lambda df: (qtpylib.crossed_below(df['mfi'], 80)),
# Weighted Sell Signal: RSI crosses below 70 (Over-bought / high-price and dropping indication)
'rsi': lambda df: (qtpylib.crossed_below(df['rsi'], 70)),
# Weighted Sell Signal: VWAP crosses below current price
'vwap_cross': lambda df: (qtpylib.crossed_below(df['vwap'], df['close'])),
# Weighted Sell Signal: Price crosses below Parabolic SAR
'sar_cross': lambda df: (qtpylib.crossed_below(df['sar'], df['close'])),
# Weighted Sell Signal: Stochastic Slow above 80 (Over-bought, indication of starting to move down)
'stoch': lambda df: (df['slowk'] > 80),
# Weighted Sell Signal: SMA long term Death Cross (Medium term SMA crosses below Long term SMA)
'sma_long_death_cross': lambda df: (qtpylib.crossed_below(df['sma50'], df['sma200'])),
# Weighted Sell Signal: SMA short term Death Cross (Short term SMA crosses below Medium term SMA)
'sma_short_death_cross': lambda df: (qtpylib.crossed_below(df['sma9'], df['sma50'])),
# Weighted Sell Signal: VWAP crosses below current price
'vwap_cross': lambda df: (qtpylib.crossed_below(df['vwap'], df['close']))
# Weighted Buy Signal: TEMA
'tema': lambda df: (df['tema'] > df['bb_middleband']) & (df['tema'] < df['tema'].shift(1))
}


# Returns the method responsible for decorating the current class with all the parameters of the MGM
generate_mgm_attributes = MasterMoniGoManiHyperStrategy.generate_mgm_attributes(buy_signals, sell_signals)

Expand Down Expand Up @@ -106,17 +100,17 @@ class MoniGoManiHyperStrategy(MasterMoniGoManiHyperStrategy):
# Plot configuration to show all Weighted Signals/Indicators used by MoniGoMani in FreqUI.
# Also loads in MGM Framework Plots for Buy/Sell Signals/Indicators and Trend Detection.
plot_config = MasterMoniGoManiHyperStrategy.populate_frequi_plots({
# Main Plots Signals/Indicators (SMAs, EMAs, Bollinger Bands, VWAP)
# Main Plots Signals/Indicators (SMAs, EMAs, Bollinger Bands, VWAP, TEMA)
'main_plot': {
'sma9': {'color': '#2c05f6'},
'sma50': {'color': '#19038a'},
'sma200': {'color': '#0d043b'},
'ema9': {'color': '#12e5a6'},
'ema50': {'color': '#0a8963'},
'ema200': {'color': '#074b36'},
'bb_upperband': {'color': '#6f1a7b'},
'bb_lowerband': {'color': '#6f1a7b'},
'vwap': {'color': '#727272'}
'bb_middleband': {'color': '#6f1a7b'},
'vwap': {'color': '#727272'},
'tema': {'color': '#9345ee'}
},
# Sub Plots - Each dict defines one additional plot
'subplots': {
Expand All @@ -133,6 +127,9 @@ class MoniGoManiHyperStrategy(MasterMoniGoManiHyperStrategy):
},
'RSI (Relative Strength Index)': {
'rsi': {'color': '#7fb92a'}
},
'Stochastic Slow': {
'slowk': {'color': '#14efe7'}
}
}
})
Expand Down Expand Up @@ -177,8 +174,12 @@ def do_populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFr
# Momentum Indicators (timeperiod is expressed in candles)
# -------------------

# ADX - Average Directional Index (The Trend Strength Indicator)
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) # 14 timeperiods is usually used for ADX
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)

# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']

# MACD - Moving Average Convergence Divergence
macd = ta.MACD(dataframe)
Expand All @@ -188,28 +189,21 @@ def do_populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFr
# MFI - Money Flow Index (Under bought / Over sold & Over bought / Under sold / volume Indicator)
dataframe['mfi'] = ta.MFI(dataframe)

# RSI - Relative Strength Index (Under bought / Over sold & Over bought / Under sold indicator Indicator)
dataframe['rsi'] = ta.RSI(dataframe)

# Overlap Studies
# ---------------

# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']

# SMA's & EMA's are trend following tools (Should not be used when line goes sideways)
# SMA - Simple Moving Average (Moves slower compared to EMA, price trend over X periods)
dataframe['sma9'] = ta.SMA(dataframe, timeperiod=9)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)

# EMA - Exponential Moving Average (Moves quicker compared to SMA, more weight added)
# (For traders who trade intra-day and fast-moving markets, the EMA is more applicable)
dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9) # timeperiod is expressed in candles
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)

# Volume Indicators
# -----------------
Expand Down