removed seasonal arima model predictions as it's very processor inten…

…sive and not needed

config.json.sample

@@ -34,7 +34,6 @@
             "trailingbuypcnt": 0.5,
             "telegramtradesonly": 0,
             "granularity": "1hour",
-            "enableml": 0,
             "use_sell_fee": 1
         }
     },
@@ -73,7 +72,6 @@
             "trailingbuypcnt": 0.5,
             "telegramtradesonly": 0,
             "granularity": "3600",
-            "enableml": 0
         }
     },
     "coinbasepro": {
@@ -111,7 +109,6 @@
             "trailingbuypcnt": 0.5,
             "telegramtradesonly": 0,
             "granularity": "3600",
-            "enableml": 0
         }
     },
     "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/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",

views/TradingGraphs.py

@@ -302,36 +302,6 @@ def format_date(x, pos=None):  # pylint: disable=unused-argument
         if saveOnly is False:
             plt.show()
 
-    def render_seasonal_arima_model(self, saveFile="", saveOnly=False):
-        """Render the seasonal ARIMA model
-
-        Parameters
-        ----------
-        saveFile : str, optional
-            Save the figure
-        saveOnly : bool
-            Save the figure without displaying it
-        """
-
-        fittedValues = self.technical_analysis.seasonal_arima_model_fitted_values()
-
-        plt.plot(self.df["close"], label="original")
-        plt.plot(fittedValues, color="red", label="fitted")
-        plt.title("RSS: %.4f" % sum((fittedValues - self.df["close"]) ** 2))
-        plt.legend()
-        plt.ylabel("Price")
-        plt.xticks(rotation=90)
-        plt.tight_layout()
-
-        try:
-            if saveFile != "":
-                plt.savefig(saveFile)
-        except OSError:
-            raise SystemExit(f"Unable to save: {saveFile}")
-
-        if saveOnly is False:
-            plt.show()
-
     def render_sma_and_macd(self, saveFile="", saveOnly=False):
         """Render the self.price, SMA20, SMA50, and SMA200
 
@@ -369,72 +339,6 @@ def render_sma_and_macd(self, saveFile="", saveOnly=False):
         if saveOnly is False:
             plt.show()
 
-    def render_arima_model_prediction(self, days=30, saveOnly=False):
-        """Render the seasonal ARIMA model prediction
-
-        Parameters
-        ----------
-        days     : int
-            Number of days to predict
-        saveOnly : bool
-            Save the figure without displaying it
-        """
-
-        # get dataframe from technical analysis object
-        df = self.technical_analysis.get_df()
-
-        if not isinstance(days, int):
-            raise TypeError("Prediction days is not numeric.")
-
-        if days < 1 or days > len(df):
-            raise ValueError("Predication days is out of range")
-
-        # extract market and granularity from trading dataframe
-        market = df.iloc[0].market
-        granularity = df.iloc[0].granularity
-
-        results_ARIMA = self.technical_analysis.seasonal_arima_model()
-
-        df = pd.DataFrame(self.df["close"])
-        start_date = df.last_valid_index()
-        end_date = start_date + timedelta(days=days)
-        pred = results_ARIMA.predict(start=str(start_date), end=str(end_date), dynamic=True)
-
-        fig, axes = plt.subplots(ncols=1, figsize=(12, 6))  # pylint: disable=unused-variable
-        fig.autofmt_xdate()
-        ax1 = plt.subplot(111)
-        ax1.set_title("Seasonal ARIMA Model Prediction")
-
-        date = pd.to_datetime(pred.index).to_pydatetime()
-
-        pred_length = len(pred)
-        # the evenly spaced plot indices
-        # indices = np.arange(pred_length)  # TODO: why is this here?
-
-        def format_date(x, pos=None):  # pylint: disable=unused-argument
-            thisind = np.clip(int(x + 0.5), 0, pred_length - 1)
-            return date[thisind].strftime("%Y-%m-%d %H:%M:%S")
-
-        fig, ax = plt.subplots(ncols=1, figsize=(12, 6))  # pylint: disable=unused-variable
-        fig.autofmt_xdate()
-
-        ax = plt.subplot(111)
-        ax.set_title("Seasonal ARIMA Model Prediction")
-        ax.plot(pred, label="prediction", color="black")
-        ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
-
-        plt.xticks(rotation=90)
-        plt.tight_layout()
-
-        try:
-            RichText.notify(f"creating: graphs/SAM_{market}_{str(granularity)}.png", self.app, "info")
-            plt.savefig(f"graphs/SAM_{market}_{str(granularity)}.png", dpi=300)
-        except OSError:
-            raise SystemExit(f"Unable to save: graphs/SAM_{market}_{str(granularity)}.png")
-
-        if saveOnly is False:
-            plt.show()
-
     def render_candle_stick_astral_pattern(self, period=30, saveOnly=False):
         # get dataframe from technical analysis object
         df = self.technical_analysis.get_df()

websvc/app/pages/pages.py

@@ -450,32 +450,6 @@ def technical_analysis(exchange: str, market: str, g1, g2, g3) -> str:
             adx14_6h_class = "table-danger"
             adx14_6h_desc = "Weak Trend Up"
 
-        def arima_predictions(even_rows: bool = True):
-            results_ARIMA = ta.seasonal_arima_model()
-            start_date = df_1h.last_valid_index()
-            end_date = start_date + datetime.timedelta(days=3)
-            arima_pred = results_ARIMA.predict(
-                start=str(start_date), end=str(end_date), dynamic=True
-            )
-
-            if even_rows:
-                arima_pred_rows = arima_pred.iloc[::2]
-            else:
-                arima_pred_rows = arima_pred.iloc[1::2]
-
-            html = ""
-            for index, pred in arima_pred_rows.items():
-                html += f"""
-                <tbody>
-                    <tr class={'table-success' if pred >= ticker[1] else 'table-danger'}>
-                        <td>{index}</td>
-                        <td>{pred}</td>
-                    </tr>
-                </tbody>
-                """
-
-            return html
-
         return f"""
         {header()}
 
@@ -955,7 +929,6 @@ def arima_predictions(even_rows: bool = True):
                             <th scope="col" style="width: 50%">Time</th>
                             <th scope="col" style="width: 50%">Prediction</th>
                         </thead>
-                        {arima_predictions(True)}
                     </table>
                 </div>
                 <div class="col-sm">
@@ -964,7 +937,6 @@ def arima_predictions(even_rows: bool = True):
                             <th scope="col" style="width: 50%">Time</th>
                             <th scope="col" style="width: 50%">Prediction</th>
                         </thead>
-                        {arima_predictions(False)}
                     </table>
                 </div>
             <div>