add app.py

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0", "--port", "7860",  "--allow-websocket-origin", "*"]
+
+RUN mkdir /.cache
+RUN chmod 777 /.cache
+RUN mkdir .chroma
+RUN chmod 777 .chroma

app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+import hvplot as hv
+import panel as pn
+import hvplot.pandas
+
+hv.extension('bokeh')
+
+def simulate(initialcapital , bet_chance , betsize , rewardrisk, riskpercent, max_rounds, max_profit , num_realization):
+
+  bet = lambda cash: cash * betsize
+
+  all_profits = []
+  for i in range(0, num_realization):
+      profits = []
+      cash = initialcapital
+      for i in range(0, max_rounds):
+          if cash <= 1: # went blowout
+              break
+
+          if cash >= max_profit:
+              break
+
+          bet_value = bet(cash)
+          if np.random.rand() < bet_chance:
+              cash += bet_value *rewardrisk * riskpercent #*.1
+          else:
+              cash -= bet_value *riskpercent #* .05
+
+          profits.append(cash)
+
+      all_profits.append(profits)
+  df = pd.DataFrame(all_profits).T
+  plot1 =  df.hvplot.line( logy=True, height=600, width=1200).opts(show_grid=True, ylabel='Profit', xlabel='Bet')
+
+  bust = [ x for x in all_profits if x[-1] <= 1 ]
+  rich = [ x for x in all_profits if x[-1] >= max_profit ]
+  text = f"""Blowout: {round(len(bust) / len(all_profits) * 100)} %
+             Avg time to go Blowout: {np.mean([ len(x) for x in bust ]):.1f}
+             Reach Max profit: {round(len(rich) / len(all_profits) * 100):.1f} %
+             Avg time to reach max profit:, {np.mean([ len(x) for x in rich ]):.1f}
+             Challenge from {initialcapital}$ to {max_profit}$
+          """
+  if round(len(bust) / len(all_profits)) > .5:
+    alert_type="danger"
+  else:
+    alert_type="success"
+  return pn.Column(plot1, pn.pane.Alert(text, alert_type=alert_type, height=150, width=1200, sizing_mode="fixed"))
+
+
+initialcapital = pn.widgets.IntSlider(name='Initial Capital', start=0, end=100000, step=1000, value=2000)
+bet_chance = pn.widgets.FloatSlider(name='Win Rate', start=0, end=1.0, step=0.1, value=0.60)
+betsize = pn.widgets.FloatSlider(name='Bet Size', start=0, end=1.0, step=0.1, value=0.50)
+rewardrisk = pn.widgets.FloatSlider(name='Reward Risk', start=0, end=1.0, step=0.1, value=1.0)
+riskpercent = pn.widgets.FloatSlider(name='risk percent', start=0, end=1.0, step=0.1, value=1.0)
+max_rounds = pn.widgets.IntSlider(name='Max Rounds', start=1000, end=10000, step=1000, value=1000)
+max_profit = pn.widgets.IntSlider(name='Max Profit', start=10000, end=10000000, step=1000, value=1000000)
+num_realization = pn.widgets.IntSlider(name='Number of Realization', start=100, end=10000, step=100, value=100)
+# selectedmethod = pn.widgets.Select(name='Select Method', value='Mean', options=['Mean' , 'Min' , 'Max'])
+
+
+bound_plot = pn.bind(simulate, initialcapital= initialcapital, bet_chance =bet_chance , betsize=betsize , rewardrisk=rewardrisk, riskpercent=riskpercent, max_rounds=max_rounds, max_profit=max_profit , num_realization=num_realization)
+
+pn.Row(pn.Column(initialcapital, bet_chance, betsize, rewardrisk, riskpercent, max_rounds, max_profit, num_realization),bound_plot).servable(title="Bet Size Optimizer - Simulation Account Growth")
+

requirements.txt

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+panel
+hvplot
+bokeh