Merge changes from rd-mg

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:latest
+FROM python:3.9
 
 # TA-lib is required by the python TA-lib wrapper. This provides analysis.
 COPY lib/ta-lib-0.4.0-src.tar.gz /tmp/ta-lib-0.4.0-src.tar.gz
@@ -14,6 +14,9 @@ COPY ./app /app
 
 WORKDIR /app
 
+RUN apt update && apt upgrade -y
+RUN apt install -y fail2ban
+
 RUN pip install --upgrade pip
 RUN pip install -r requirements-step-1.txt
 RUN pip install -r requirements-step-2.txt

README.md

@@ -5,7 +5,7 @@ Development branch to testing new features. This develop version has a lot of im
 ## Notable Changes
 - It creates candle bar charts with MAs, RSI and MACD. These images can be sent as part of a Telegram notification or a Webhook call.
 - It allows to include prices as part of the notification message.
-- New configuration to easily add many coins. Check bellow for "all_pairs".
+- New configuration  to easily add many coins. Check bellow for "all_pairs".
 - New config var to use a custom "indicator_label" for each configured indicator and crossovers. Mainly useful for std_crossover.
 - New indicator iiv (Increase In Volume) to try to identify a pump/dump.
 - New indicator MA Ribbon
@@ -16,7 +16,14 @@ Development branch to testing new features. This develop version has a lot of im
 - New indicator StochRSI Cross
 - New indicator Sqzmon - Squeeze Momentum Indicator
 - New option to customize ichimoku strategies and added chikou span
-
+- Uptrend evaluation for a signal, it compares two signal candles to determine uptrend
+- Changed Bollinger bands indicator type from informant to indicator to send message status to telegram
+- Adde Bollinger bands width to Bollinger bands indicator
+- New Indicator NATR
+- New Indicator ROC
+- New indicator IFish Stoch
+- Added pandas-ta and dependencies in requirements
+- New indicator IIP (price)
 
 ## Installing And Running
 The commands listed below are intended to be run in a terminal.
@@ -25,13 +32,17 @@ Be sure you have git installed in your system.
 
 1. Clone this repo `git clone https://github.com/w1ld3r/crypto-signal.git`
 
-1. Enter to cripto-signal folder `cd crypto-signal`
+2. Enter to cripto-signal folder `cd crypto-signal`
 
-1. Switch to develop branch `git checkout develop`
+3. Switch to develop branch `git checkout develop`
 
-1. Create a config.yml file and put it into "app" folder.
+4. Create a config.yml file and put it into "app" folder.
+
+5. Build and run the docker container: `docker-compose up --build`
+
+6. For testing and debugging run docker with "-t" option `docker run --rm -ti -v $PWD/app:/app crypto-signal_app:latest`
 
-1. Build and run the docker container: `docker-compose up --build`
+7. For production run in daemon mode using "-d" option `docker run --rm -di -v $PWD/app:/app crypto-signal_app:latest`
 
 
 ### Configuring config.yml

app/analysis.py

@@ -39,6 +39,8 @@ def indicator_dispatcher(self):
             'momentum': momentum.Momentum().analyze,
             'mfi': mfi.MFI().analyze,
             'stoch_rsi': stoch_rsi.StochasticRSI().analyze,
+            'stoch_hma_avg_rsi': stoch_hma_avg_rsi.StochHMAAVGRSI().analyze,
+            'stoch_hma': stoch_hma.StochHMA().analyze,
             'obv': obv.OBV().analyze,
             'iiv': iiv.IIV().analyze,
             'ma_ribbon': ma_ribbon.MARibbon().analyze,
@@ -47,7 +49,13 @@ def indicator_dispatcher(self):
             'bbp': bbp.BBP().analyze,
             'macd_cross': macd_cross.MACDCross().analyze,
             'stochrsi_cross': stochrsi_cross.StochRSICross().analyze,
-            'sqzmom': sqzmom.SQZMOM().analyze
+            'sqzmom': sqzmom.SQZMOM().analyze,
+            'natr': natr.NATR().analyze,
+            'bollinger_bands': bollinger_bands.Bollinger().analyze,
+            'roc': roc.ROC().analyze,
+            'ifish_stoch': ifish_stoch.IFISH_STOCH().analyze,
+            'iip': iip.IIP().analyze
+
         }
 
         return dispatcher
@@ -63,9 +71,9 @@ def informant_dispatcher(self):
             'sma': sma.SMA().analyze,
             'ema': ema.EMA().analyze,
             'vwap': vwap.VWAP().analyze,
-            'bollinger_bands': bollinger_bands.Bollinger().analyze,
             'ohlcv': ohlcv.OHLCV().analyze,
-            'lrsi': lrsi.LRSI().analyze
+            'lrsi': lrsi.LRSI().analyze,
+            'hma': hma.HMA().analyze,
         }
 
         return dispatcher
@@ -82,3 +90,16 @@ def crossover_dispatcher(self):
         }
 
         return dispatcher
+
+    def uptrend_dispatcher(self):
+        """Returns a pandas.DataFrame for dynamic uptrend selector
+
+        Returns:
+            dictionary: A dictionary of functions to serve as a dynamic uptrend selector.
+        """
+
+        dispatcher = {
+            'std_uptrend': uptrend.UpTrend().analyze
+        }
+
+        return dispatcher

app/analyzers/__init__.py

@@ -1 +1 @@
-__all__ = ['crossover']
+__all__ = ['crossover', 'uptrend']

app/analyzers/crossover.py

@@ -1,30 +1,23 @@
-""" Crossover analysis indicator
-"""
-
-import numpy
-import pandas
-from talib import abstract
-
+import pandas as pd
 from analyzers.utils import IndicatorUtils
 
-
 class CrossOver(IndicatorUtils):
     def analyze(self, key_indicator, key_signal, key_indicator_index,
                 crossed_indicator, crossed_signal, crossed_indicator_index):
         """ Tests for key_indicator crossing over the crossed_indicator.
 
         Args:
-            key_indicator (pandas.DataFrame): A dataframe containing the results of the analysis
+            key_indicator (pd.DataFrame): A dataframe containing the results of the analysis
                 for the selected key indicator.
             key_signal (str): The name of the key indicator.
             key_indicator_index (int): The configuration index of the key indicator to use.
-            crossed_indicator (pandas.DataFrame): A dataframe containing the results of the
+            crossed_indicator (pd.DataFrame): A dataframe containing the results of the
                 analysis for the selected indicator to test for a cross.
             crossed_signal (str): The name of the indicator expecting to be crossed.
             crossed_indicator_index (int): The configuration index of the crossed indicator to use.
 
         Returns:
-            pandas.DataFrame: A dataframe containing the indicators and hot/cold values.
+            pd.DataFrame: A dataframe containing the indicators and hot/cold values.
         """
 
         key_indicator_name = '{}_{}'.format(key_signal, key_indicator_index)
@@ -39,10 +32,19 @@ def analyze(self, key_indicator, key_signal, key_indicator_index,
             column_indexed_name = '{}_{}'.format(column, crossed_indicator_index)
             new_crossed_indicator.rename(columns={column: column_indexed_name}, inplace=True)
 
-        combined_data = pandas.concat([new_key_indicator, new_crossed_indicator], axis=1)
+        combined_data = pd.concat([new_key_indicator, new_crossed_indicator], axis=1)
         combined_data.dropna(how='any', inplace=True)
 
-        combined_data['is_hot'] = combined_data[key_indicator_name] > combined_data[crossed_indicator_name]
-        combined_data['is_cold'] = combined_data[key_indicator_name] < combined_data[crossed_indicator_name]
+        # Check if the key indicator crossed above the crossed indicator
+        combined_data['is_hot'] = (
+            (combined_data[key_indicator_name].shift() < combined_data[crossed_indicator_name].shift()) &
+            (combined_data[key_indicator_name] > combined_data[crossed_indicator_name])
+        )
+
+        # Check if the key indicator crossed below the crossed indicator
+        combined_data['is_cold'] = (
+            (combined_data[key_indicator_name].shift() > combined_data[crossed_indicator_name].shift()) &
+            (combined_data[key_indicator_name] < combined_data[crossed_indicator_name])
+        )
 
         return combined_data

app/analyzers/indicators/__init__.py

@@ -8,6 +8,8 @@
     'momentum',
     'rsi',
     'stoch_rsi',
+    'stoch_hma_avg_rsi',
+    'stoch_hma',
     'mfi',
     'obv',
     'iiv',
@@ -17,5 +19,10 @@
     'bbp',
     'macd_cross',
     'stochrsi_cross',
-    'sqzmom'
+    'sqzmom',
+    'natr',
+    'bollinger_bands',
+    'roc',
+    'ifish_stoch', 
+    'iip'
 ]

app/analyzers/indicators/bbp.py

@@ -7,13 +7,14 @@
 
 import pandas
 from talib import BBANDS, abstract
+import numpy as np
 
 from analyzers.utils import IndicatorUtils
 
 
 class BBP(IndicatorUtils):
 
-    def analyze(self, historical_data, signal=['bbp'], hot_thresh=0, cold_thresh=0.8, period_count=20, std_dev=2):
+    def analyze(self, historical_data, signal=['bbp'], hot_thresh=0.3, cold_thresh=0.8, period_count=20, std_dev=2):
         """Check when close price cross the Upper/Lower bands.
 
         Args:
@@ -47,8 +48,8 @@ def analyze(self, historical_data, signal=['bbp'], hot_thresh=0, cold_thresh=0.8
 
         bollinger['is_hot'] = False
         bollinger['is_cold'] = False
+
+        bollinger['is_hot'] = bollinger['bbp'] <= hot_thresh
+        bollinger['is_cold'] = bollinger['bbp'] >= cold_thresh
 
-        bollinger['is_hot'].iloc[-1] = bollinger['bbp'].iloc[-2] <= hot_thresh and bollinger['bbp'].iloc[-2] < bollinger['bbp'].iloc[-1]
-        bollinger['is_cold'].iloc[-1] = bollinger['bbp'].iloc[-1] >= cold_thresh
-
-        return bollinger
+        return bollinger

app/analyzers/informants/bollinger_bands.py → app/analyzers/indicators/bollinger_bands.py

@@ -11,7 +11,7 @@
 
 
 class Bollinger(IndicatorUtils):
-    def analyze(self, historical_data, period_count=21):
+    def analyze(self, historical_data, signal=['bbwidth'], hot_thresh=None, cold_thresh=None,  period_count=21):
         """Performs a bollinger band analysis on the historical data
 
         Args:
@@ -28,7 +28,8 @@ def analyze(self, historical_data, period_count=21):
         bb_columns = {
             'upperband': [numpy.nan] * dataframe.index.shape[0],
             'middleband': [numpy.nan] * dataframe.index.shape[0],
-            'lowerband': [numpy.nan] * dataframe.index.shape[0]
+            'lowerband': [numpy.nan] * dataframe.index.shape[0],
+            'bbwidth': [numpy.nan] * dataframe.index.shape[0]
         }
 
         bb_values = pandas.DataFrame(
@@ -44,10 +45,13 @@ def analyze(self, historical_data, period_count=21):
 
             for index in range(period_count, bb_df_size):
                 data_index = index - period_count
-                bb_values['lowerband'][index] = bb_data[0][data_index]
-                bb_values['middleband'][index] = bb_data[1][data_index]
-                bb_values['upperband'][index] = bb_data[2][data_index]
-
+                bb_values['lowerband'].iloc[index] = bb_data[0][data_index]
+                bb_values['middleband'].iloc[index] = bb_data[1][data_index]
+                bb_values['upperband'].iloc[index] = bb_data[2][data_index]
+                bb_values['bbwidth'].iloc[index] = (bb_data[2][data_index] - bb_data[0][data_index]) / bb_data[1][data_index]
+
+        bb_values['is_hot'] = True
+        bb_values['is_cold'] = False
         bb_values.dropna(how='all', inplace=True)
 
         return bb_values

app/analyzers/indicators/ifish_stoch.py

@@ -0,0 +1,40 @@
+
+""" 
+IFISH STOCHATIC RSI indicator
+"""
+
+import pandas as pd
+import talib
+import numpy as np
+import pandas_ta as ta
+
+from analyzers.utils import IndicatorUtils
+
+
+class IFISH_STOCH(IndicatorUtils):
+    def analyze(self, historical_data, signal="ifish_stoch", hot_thresh=-0.9, cold_thresh=0.9, period_count=5):
+        """
+        Calculates the Ifish Stoch indicator using the inverse of the Fisher transform
+        :param df: Dataframe with close prices
+        :param n: Period for Stochastic oscillator
+        :param m: Period for the momentum indicator
+        :param r: Harmonic ratio for the Fisher transform
+        :return: Dataframe with Ifish Stoch values
+        """
+        # Calculate Stochastic oscillator
+        df = pd.DataFrame()
+        df = self.convert_to_dataframe(historical_data)
+        # Calcular la transformada de Fisher estocástica
+        df.ta.fisher(length= period_count, signal = 9, append= True)
+        print(df.tail())
+
+        # Calcular la inversa de la transformada de Fisher estocástica
+        df["ifish_stoch"] = np.log((1-df["FISHERT_5_9"])/df["FISHERT_5_9"])
+        print(df.tail())
+
+
+        df["is_hot"] = (df["ifish_stoch"] < hot_thresh) & (
+            df["ifish_stoch"] > df["ifish_stoch"].shift(1))
+        df["is_cold"] = (df["ifish_stoch"] > cold_thresh) | (
+            df["ifish_stoch"] < df["ifish_stoch"].shift(1))
+        return df

app/analyzers/indicators/iip.py

@@ -0,0 +1,39 @@
+""" Custom Indicator Increase In  Volume
+"""
+
+import numpy as np
+from scipy import stats
+import pandas_ta as pta
+
+
+from analyzers.utils import IndicatorUtils
+
+
+class IIP(IndicatorUtils):
+    def analyze(self, historical_data, signal=['iip'], hot_thresh=2, cold_thresh=0, period_count=9):
+        """Performs an analysis about the increase in price to detect pump dump on the historical data
+
+        Args:
+            historical_data (list): A matrix of historical OHCLV data.
+            signal (list, optional): Defaults to iip. The indicator line to check hot against.
+            hot_thresh (float, optional): Defaults to 10. 
+            cold_thresh: below hot+thresh
+
+
+        Returns:
+            pandas.DataFrame: A dataframe containing the indicator and hot/cold values.
+        """
+
+        dataframe = self.convert_to_dataframe(historical_data)
+
+        dataframe['candle_length'] = np.abs(dataframe['close']*100000 - dataframe['open']*100000)
+        dataframe.ta.zscore(close= dataframe['candle_length'], length=period_count, std=hot_thresh, append=True)
+        dataframe['iip'] = np.abs(dataframe[f"ZS_{period_count}"])
+        dataframe.dropna(how='all', inplace=True)
+
+        dataframe['is_hot'] = False
+        dataframe['is_cold'] = False
+        dataframe['is_hot'] = (dataframe["iip"] >= hot_thresh) & (dataframe['close'] > dataframe['open'])
+        dataframe['is_cold'] = (dataframe["iip"] >= hot_thresh) & (dataframe['close'] < dataframe['open'])
+
+        return dataframe

app/analyzers/indicators/macd_cross.py

@@ -29,8 +29,8 @@ def analyze(self, historical_data, signal=['macd'], hot_thresh=None, cold_thresh
         macd, macdsignal, macdhist = talib.MACD(
             dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
 
-        macd_values = pandas.DataFrame([macd, macdsignal]).T.rename(
-            columns={0: "macd", 1: "signal"})
+        macd_values = pandas.DataFrame([macd, macdsignal, macdhist]).T.rename(
+            columns={0: "macd", 1: "signal", 2: "macdhist"})
 
         macd_cross = pandas.concat([dataframe, macd_values], axis=1)
         macd_cross.dropna(how='all', inplace=True)

app/analyzers/indicators/natr.py

@@ -0,0 +1,32 @@
+""" NATR Indicator
+"""
+
+import math
+
+import pandas
+from talib import abstract
+
+from analyzers.utils import IndicatorUtils
+
+
+class NATR(IndicatorUtils):
+    def analyze(self, historical_data, signal=['natr'], hot_thresh=None, cold_thresh=None, period_count=14):
+        """Performs an NATR analysis on the historical data
+
+                Args:
+                        historical_data (list): A matrix of historical OHCLV data.
+                        period_count (int, optional): Defaults to 14. The number of data points to consider for
+                                our exponential moving average.
+
+                Returns:
+                        pandas.DataFrame: A dataframe containing the indicators and hot/cold values.
+                """
+
+        dataframe = self.convert_to_dataframe(historical_data)
+        natr_values = abstract.NATR(dataframe, period_count).to_frame()
+        natr_values.dropna(how='all', inplace=True)
+        natr_values.rename(columns={0: 'natr'}, inplace=True)
+        natr_values['is_hot'] = True
+        natr_values['is_cold'] = False
+
+        return natr_values