Merging beta into main

.github/workflows/unit-tests.yml

@@ -24,8 +24,8 @@ jobs:
     - name: Find typos with codespell
       uses: codespell-project/actions-codespell@master
       with:
-        ignore_words_list: zar,hist,VAI
-        skip: "*.json,./tests/unit_tests/responses"
+        ignore_words_list: vai,zar,hist
+        skip: ".venv,.git,*.json,./tests/unit_tests/responses"
     - uses: actions/cache@v2
       with:
         path: ${{ env.pythonLocation }}

.vscode/settings.json

@@ -1,5 +1,5 @@
 {
-    "python.formatting.provider": "black",
+    "python.formatting.provider": "none",
     "python.formatting.blackArgs": [
         "--line-length",
         "160"
@@ -8,4 +8,7 @@
         "--max-line-length=160",
         "--ignore=E203,E266,E501,W503,F403,F401,C901",
     ],
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter"
+    },
 }

CHANGELOG.md

@@ -8,6 +8,10 @@ Upgrade version:
 Upgrade library dependencies (if required):
 - python3 -m pip install -r requirements.txt -U
 
+## [8.2.0] - 2023-04
+
+- removed statsmodels which has a dependency on scipy which is huge and causes a lot of problems
+
 ## [8.0.*] - 2023-04
 
 - bug fixes and improvements

Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.11.4-slim-bullseye AS compile-image
 
 RUN DEBIAN_FRONTEND=noninteractive apt-get update && \
     apt-get install --no-install-recommends -y \
-    build-essential gfortran && \
+    build-essential && \
     rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app

README.md

@@ -1,4 +1,4 @@
-# Python Crypto Bot v8.1.1 (pycryptobot)
+# Python Crypto Bot v8.2.0 (pycryptobot)
 
 [![Docker](https://github.com/whittlem/pycryptobot/actions/workflows/container.yml/badge.svg)](https://github.com/whittlem/pycryptobot/actions/workflows/container.yml/badge.svg) [![Tests](https://github.com/whittlem/pycryptobot/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/whittlem/pycryptobot/actions/workflows/unit-tests.yml/badge.svg)
 

config.json.sample

@@ -34,7 +34,6 @@
             "trailingbuypcnt": 0.5,
             "telegramtradesonly": 0,
             "granularity": "1hour",
-            "enableml": 0,
             "use_sell_fee": 1
         }
     },
@@ -72,8 +71,7 @@
             "sellsmartswitch": 0,
             "trailingbuypcnt": 0.5,
             "telegramtradesonly": 0,
-            "granularity": "3600",
-            "enableml": 0
+            "granularity": "3600"
         }
     },
     "coinbasepro": {
@@ -110,8 +108,7 @@
             "sellsmartswitch": 0,
             "trailingbuypcnt": 0.5,
             "telegramtradesonly": 0,
-            "granularity": "3600",
-            "enableml": 0
+            "granularity": "3600"
         }
     },
     "telegram": {

controllers/PyCryptoBot.py

@@ -910,17 +910,6 @@ def _notify(notification: str = "", level: str = "normal") -> None:
                         self.console_log.print(self.table_console)
                     self.table_console = Table(title=None, box=None, show_header=False, show_footer=False)  # clear table
 
-                    if self.enableml:
-                        # Seasonal Autoregressive Integrated Moving Average (ARIMA) model (ML prediction for 3 intervals from now)
-                        if not self.is_sim:
-                            try:
-                                prediction = _technical_analysis.arima_model_prediction(int(self.granularity.to_integer / 60) * 3)  # 3 intervals from now
-                                _notify(
-                                    f"Seasonal ARIMA model predicts the closing self.price will be {str(round(prediction[1], 2))} at {prediction[0]} (delta: {round(prediction[1] - self.price, 2)})"
-                                )
-                            except Exception:
-                                pass
-
                     if self.state.last_action == "BUY":
                         # display support, resistance and fibonacci levels
                         if not self.is_sim:
@@ -2304,15 +2293,6 @@ def config_option_row_enum(
             default_value=False,
             arg_name="manualtradesonly",
         )
-        config_option_row_bool(
-            "AI/ML Predictions",
-            "enableml",
-            "Enable AI / Machine Learning Predictions",
-            break_below=False,
-            store_invert=False,
-            default_value=False,
-            arg_name="predictions",
-        )
         config_option_row_str(
             "Start Method",
             "startmethod",

examples/create-graphs.py

@@ -17,5 +17,4 @@
 # tradinggraphs.render_fibonacci_retracement(True)
 # tradinggraphs.render_support_resistance(True)
 # tradinggraphs.render_candle_sticks(30, True)
-# tradinggraphs.render_arima_model_prediction(1, True)
 tradinggraphs.render_bollinger_bands()

models/BotConfig.py

@@ -121,7 +121,6 @@ def __init__(self, *args, **kwargs):
         self.disabletelegram = False
         self.disablelog = False
         self.disabletracker = True
-        self.enableml = False
         self.websocket = False
         self.exitaftersell = False
         self.ignorepreviousbuy = True
@@ -424,7 +423,6 @@ def _parse_arguments(self):
         parser.add_argument("--insufficientfundslogging", type=int, help="Enable insufficient funds logging")
         parser.add_argument("--logbuysellinjson", type=int, help="Log buy and sell orders in a JSON file")
         parser.add_argument("--manualtradesonly", type=int, help="Manual Trading Only (HODL)")
-        parser.add_argument("--predictions", type=int, help="Enable AI / Machine Learning Predictions")
         parser.add_argument("--startmethod", type=str, help="Bot start method ('scanner', 'standard', 'telegram')")
         parser.add_argument("--recvwindow", type=int, help="Binance exchange API recvwindow, integer between 5000 and 60000")
         parser.add_argument("--lastaction", type=str, help="Manually set the last action performed by the bot (BUY, SELL)")

models/Trading.py

@@ -19,12 +19,8 @@
 )
 from pandas import concat, DataFrame, Series
 from datetime import datetime, timedelta
-from statsmodels.tsa.statespace.sarimax import SARIMAX, SARIMAXResultsWrapper
-from statsmodels.tools.sm_exceptions import ConvergenceWarning
 from views.PyCryptoBot import RichText
 
-warnings.simplefilter("ignore", ConvergenceWarning)
-
 
 class TechnicalAnalysis:
     def __init__(self, data=DataFrame(), total_periods: int = 300, app: object = None) -> None:
@@ -772,62 +768,6 @@ def add_williamsr(self, period: int = 14) -> None:
         # self.df["williamsr" + str(period)] = self.df["williamsr" + str(period)].replace(nan, -50)
         self.df["williamsr" + str(period)] = ta.willr(high=self.df["high"], close=self.df["close"], low=self.df["low"], interval=period, fillna=self.df.close)
 
-    def seasonal_arima_model(self) -> SARIMAXResultsWrapper:
-        """Returns the Seasonal ARIMA Model for price predictions"""
-
-        # hyperparameters for SARIMAX
-        if not self.df.index.freq:
-            freq = str(self.df["granularity"].iloc[-1]).replace("m", "T").replace("h", "H").replace("d", "D")
-            if freq.isdigit():
-                freq += "S"
-            self.df.index = self.df.index.to_period(freq)
-        model = SARIMAX(self.df["close"], trend="n", order=(0, 1, 0), seasonal_order=(1, 1, 1, 12))
-        return model.fit(disp=-1)
-
-    def seasonal_arima_model_fitted_values(self):  # TODO: annotate return type
-        """Returns the Seasonal ARIMA Model for price predictions"""
-
-        return self.seasonal_arima_model().fittedvalues
-
-    def seasonal_arima_model_prediction(self, minutes: int = 180) -> tuple:
-        """Returns seasonal ARIMA model prediction
-
-        Parameters
-        ----------
-        minutes     : int
-            Number of minutes to predict
-        """
-
-        if not isinstance(minutes, int):
-            raise TypeError("Prediction minutes is not numeric.")
-
-        if minutes < 1 or minutes > 4320:
-            raise ValueError("Predication minutes is out of range")
-
-        results_ARIMA = self.seasonal_arima_model()
-
-        start_ts = self.df.last_valid_index()
-        end_ts = start_ts + timedelta(minutes=minutes)
-        pred = results_ARIMA.predict(start=str(start_ts), end=str(end_ts), dynamic=True)
-
-        try:
-            if len(pred) == 0:
-                df_last = self.df["close"].tail(1)
-                return (
-                    str(df_last.index.values[0]).replace("T", " ").replace(".000000000", ""),
-                    df_last.values[0],
-                )
-            else:
-                df_last = pred.tail(1)
-                return (
-                    str(df_last.index.values[0]).replace("T", " ").replace(".000000000", ""),
-                    df_last.values[0],
-                )
-        except Exception:
-            return None
-
-        return None
-
     def simple_moving_average(self, period: int) -> float:
         """Calculates the Simple Moving Average (SMA)"""
 

models/config/default_parser.py

@@ -244,7 +244,6 @@ def config_option_date(
     config_option_bool(option_name="insufficientfundslogging", option_default=False, store_name="enableinsufficientfundslogging", store_invert=False)
     config_option_bool(option_name="logbuysellinjson", option_default=False, store_name="logbuysellinjson", store_invert=False)
     config_option_bool(option_name="manualtradesonly", option_default=False, store_name="manual_trades_only", store_invert=False)
-    config_option_bool(option_name="predictions", option_default=False, store_name="enableml", store_invert=False)
     config_option_str(option_name="startmethod", option_default="standard", store_name="startmethod", valid_options=["scanner", "standard", "telegram"])
     config_option_int(option_name="recvwindow", option_default=5000, store_name="recv_window", value_min=5000, value_max=60000)
     config_option_str(option_name="lastaction", option_default=None, store_name="last_action", valid_options=["BUY", "SELL"])

pages/config.py

@@ -480,10 +480,6 @@
                                                             "label": "Disable Bot Logs",
                                                             "value": "disablelog",
                                                         },
-                                                        {
-                                                            "label": "Enable Machine Learning Messages",
-                                                            "value": "enableml",
-                                                        },
                                                     ],
                                                     value=[1],
                                                     id="switches-extras",

requirements.txt

@@ -2,8 +2,6 @@ urllib3>=1.26.5
 pandas==1.5.1
 pandas-ta
 numpy
-scipy>=1.8.1
-statsmodels
 requests==2.31.0
 matplotlib==3.3.3
 mock==4.0.3

tests/unit_tests/test_telegram_bot.py

@@ -1,12 +1,9 @@
 import os
 import sys
 import unittest
-# pylint: disable=import-error
-from models.telegram import (
-    Wrapper,
-)
 
 sys.path.append(".")
+from models.telegram import Wrapper
 
 wrapper = Wrapper("config.json.sample")
 wrapper.helper.datafolder = os.path.join(os.curdir, "tests", "unit_tests", "data")