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#ABMPC-TEST
import streamlit as st
import pandas as pd
import plotly.graph_objects as go
# Must be called first.
st.set_page_config(
layout="wide", # 'centered' or 'wide'
page_title='ABMPC-TEST',
menu_items={
'About': "A shareable data application. https://www.giltedged.systems"
}
)
header = st.container()
dataset = st.container()
body = st.container()
footer = st.container()
with header:
# st.title("Agent-Based Model Portfolio Choice: Test Ouput")
col1, col2 = st.columns(2)
with col1:
st.title("Agent-Based Model Portfolio Choice")
st.subheader("Test Ouput")
with col2:
st.info("[GiltEdged.systems/early-models/abmpc](https://www.giltedged.systems/early-models/abmpc)")
with dataset:
data_url = ('https://www.data-reports.net/model-output/abmpc-test/abmpc-test-01.csv')
# @st.cache_data
def load_data(steps):
data = pd.read_csv(data_url, nrows=steps)
return data
with body:
with st.expander("See The Puzzling Impact of Interest Rates"):
st.write("'.. It follows, in contrast to what most students of principles of economics are taught, that higher interest rates generate more economic activity, not less. Unless high interest rates have a detrimental impact on some components of aggregate demand.' G&L, Chapter 4, p.114")
st.write("**G&L continue:** '.. The fall in bond prices induces an immediate reduction in wealth. Interest rates have a big effect on asset prices and this is one of the main conduits for monetary policy - working in the conventional way. A rise in interest rates reduces demand for a time in model LP this is achieved by a reduction in consumption expenditures, with a lag, through the reduction in the wealth term that appears in the consumption function. And this puts the earlier unconventional result of Chapter 4 into a proper perspective. Model LP shows that interest rate increases do have a negative effect on real demand, but this negative effect is only a temporary one (which may still last for quite a long time). In a longer period, the effect of higher interest rates is positive as we have said before.' G&L, Chapter 5, p.151")
st.caption("G&L: Godley, W & Lavoie, M, Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth.")
# st.divider()
col1, col2, col3, col4 = st.columns(4)
col1.metric("Model Steps", "170")
col2.metric("Expenditure", "20 Units", "Every Step", delta_color="off")
col3.metric("Interest Rate Environment", "0% to 1.25%")
col4.metric("Taxation", "20%", "Flat Rate", delta_color="off")
colA, colB = st.columns([2,3], gap="large")
with colA:
stepsDefault = 170 # Default number of steps to show.
steps = st.slider("Model Steps", min_value=1, max_value=170, value=stepsDefault, step=2)
with colB:
option = st.selectbox(
'Model Interest Rate:',
('0% Interest-Bearing Bills', '0.75% Interest-Bearing Bills', '0.75% to 1.25% Interest-Bearing Bills (@Step 90)', '1.25% to 0.75% Interest-Bearing Bills (@Step 90)', '0.75% Interest-Bearing Bills (Multiple Agents: 1 Producer, 3 Households)'))
if option == "0% Interest-Bearing Bills":
data_url = "https://www.data-reports.net/model-output/abmpc-test/abmpc-test-run-01.csv"
elif option == "0.75% Interest-Bearing Bills":
data_url = "https://www.data-reports.net/model-output/abmpc-test/abmpc-test-run-02.csv"
elif option == "0.75% to 1.25% Interest-Bearing Bills (@Step 90)":
data_url = "https://www.data-reports.net/model-output/abmpc-test/abmpc-test-run-03.csv"
elif option == "1.25% to 0.75% Interest-Bearing Bills (@Step 90)":
data_url = "https://www.data-reports.net/model-output/abmpc-test/abmpc-test-run-04.csv"
elif option == "0.75% Interest-Bearing Bills (Multiple Agents: 1 Producer, 3 Households)":
data_url = "https://www.data-reports.net/model-output/abmpc-test/abmpc-test-run-05.csv"
# Load data.
df = load_data(steps)
def display_chart():
dff = df
goFig = go.Figure()
goFig.add_trace(go.Scatter(x=dff.Step, y=dff.national_income,
mode='lines',
name='National Income'
))
goFig.add_trace(go.Scatter(x=dff.Step, y=dff.disposable_income,
mode='lines',
name='Disposable Income'
))
goFig.add_trace(go.Scatter(x=dff.Step, y=dff.consumption,
mode='lines',
name='Consumption'
))
goFig.add_trace(go.Scatter(x=dff.Step, y=dff.bills_issued,
mode='lines',
name='Bills Issued'
))
goFig.add_trace(go.Scatter(x=dff.Step, y=dff.fiscal_balance,
mode='lines',
name='Fiscal Balance'
))
goFig.update_layout(
margin=dict(l=50,r=40,b=40,t=40),
height=700,
template="plotly_dark",
xaxis_title="Model Steps",
xaxis_tickfont_size=14,
yaxis=dict(
title='Monetary Units',
),
showlegend=True,
legend_title="Macro Indicators",
title=go.layout.Title(
text="Model Run: " + option,
xref="paper",
x=0
)
)
st.plotly_chart(goFig, use_container_width=True)
display_chart()
with footer:
st.markdown("---")
st.caption("Visit the [GiltEdged.systems](https://www.www.giltedged.systems) website.")
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