start with neural prophet

algo_bot/commands/strategies.py

@@ -1,6 +1,5 @@
 import argparse
 import re
-import time
 
 import numpy as np
 import pandas as pd
@@ -10,7 +9,7 @@
 from algo_bot import cache, charting, utils
 from algo_bot.clients import alpha_vantage_client as avc
 from algo_bot.db.models import Screener
-from prophet import Prophet
+from neuralprophet import NeuralProphet
 
 RUN_STRATEGY_PARSER = argparse.ArgumentParser()
 RUN_STRATEGY_PARSER.add_argument("--screener-id", type=int, required=False)
@@ -29,11 +28,10 @@ def run_strategy_predict_price(message, ticker):
     df = pd.DataFrame(data=results.loc[:, ["date", "4. close"]]).rename(columns={"date": "ds", "4. close": "y"})
     # df['cap'] = df["y"].max()
     price = pd.DataFrame(data=results.loc[:, ["date", "4. close"]]).rename(columns={"4. close": "close"}).set_index(["date"])
-    m = Prophet(mcmc_samples=300)
-    m.fit(df, control={'max_treedepth': 10})
-    future = m.make_future_dataframe(periods=365, freq='d', include_history = True)
-    # future['cap'] = df["y"].max()
-    forecast = m.predict(future).set_index(["ds"]).sort_values(by="ds", ascending=False)
+    m = NeuralProphet(daily_seasonality=True)
+    m.fit(df, freq="D")
+    future = m.make_future_dataframe(df, periods=365, n_historic_predictions=True)
+    forecast = m.predict(future).set_index(["ds"]).sort_values(by="ds", ascending=True).rename(columns={"yhat1": "yhat"})
     fig = charting.predicted_price(
         start=utils.today(),
         stop=utils.years_ago(),
@@ -84,7 +82,7 @@ def run_strategy_risk_vs_reward(message, params):
                 message, "Error: Screener not found or does not belong to you!"
             )
     elif params.ticker:
-        data = { }
+        data = {}
         ticker = params.ticker
         data[ticker] = avc.time_series_daily(ticker)["4. close"]
         df = pd.DataFrame(data=data).sort_values(by="date", ascending=False).dropna()
@@ -99,8 +97,8 @@ def run_strategy_risk_vs_reward(message, params):
         )
     else:
         utils.reply_webapi(
-                message, "Error: Requires either --screener-id or --ticker"
-            )
+            message, "Error: Requires either --screener-id or --ticker"
+        )
 
 
 @respond_to("^strategy-turtle (.*)")
@@ -197,14 +195,3 @@ def _turtle(screener):
     df = pd.DataFrame(records)
 
     return df
-
-
-def _retry(callable):
-    while True:
-        try:
-            callable()
-        except ValueError as exc:
-            print(exc)
-            time.sleep(60)
-        else:
-            break

requirements.lock

@@ -7,6 +7,7 @@ argcomplete==1.12.3
 argon2-cffi==20.1.0
 astor==0.8.1
 async-timeout==3.0.1
+attrdict==2.0.1
 attrs==21.2.0
 Babel==2.9.1
 backcall==0.2.0
@@ -30,14 +31,16 @@ entrypoints==0.3
 ephem==4.0.0.2
 finviz==1.4.2
 funcparserlib==0.3.6
+future==0.18.2
 greenlet==1.1.1
 holidays==0.10.4
 hy==0.20.0
 idna==2.10
 importlib-metadata==4.7.1
 ipykernel==6.2.0
-ipython==7.26.0
+ipython==7.27.0
 ipython-genutils==0.2.0
+ipywidgets==7.6.3
 jedi==0.18.0
 Jinja2==3.0.1
 jmespath==0.10.0
@@ -49,6 +52,7 @@ jupyter-server==1.10.2
 jupyterlab==3.0.5
 jupyterlab-pygments==0.1.2
 jupyterlab-server==2.7.2
+jupyterlab-widgets==1.0.0
 kiwisolver==1.3.2
 korean-lunar-calendar==0.2.1
 LunarCalendar==0.0.9
@@ -65,6 +69,7 @@ nbclient==0.5.4
 nbconvert==6.1.0
 nbformat==5.1.3
 nest-asyncio==1.4.3
+neuralprophet==0.2.7
 notebook==6.4.3
 numpy==1.21.2
 packaging==21.0
@@ -115,6 +120,8 @@ tabulate==0.8.7
 tenacity==8.0.1
 terminado==0.11.1
 testpath==0.5.0
+torch==1.6.0
+torch-lr-finder==0.2.1
 tornado==6.1
 tqdm==4.62.2
 traitlets==5.0.5
@@ -126,6 +133,7 @@ wcwidth==0.2.5
 webencodings==0.5.1
 websocket-client==0.44.0
 wheel==0.37.0
+widgetsnbextension==3.5.1
 yarl==1.6.3
 yfinance==0.1.63
 zipp==3.5.0

requirements.txt

@@ -2,6 +2,7 @@ requests==2.25.1
 finviz==1.4.2
 pystan==2.19.1.1
 numpy==1.21.2
+neuralprophet
 prophet==1.0.1
 plotly==4.14.3
 pandas==1.2.0