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#!/usr/bin/env -S pkgx +python@3.7 uv run -- streamlit run
import pandas as pd
import streamlit as st
from streamlit.components.v1 import iframe, html
import altair as alt
from pygwalker.api.streamlit import StreamlitRenderer, init_streamlit_comm
from types import SimpleNamespace
from df import fetch
alt.renderers.set_embed_options(theme="dark")
@st.cache_data(ttl="30m")
def fetch_asset(asset):
return fetch(asset)
def gen_charts(asset, chart_size={"width": 560, "height": 150}):
# Gen data
data = fetch_asset(asset)
etf_volumes = data.etf_volumes
price = data.price
etf_flow_individual = data.etf_flow_individual
etf_flow_total = data.etf_flow_total
cum_flow_individual = data.cum_flow_individual
cum_flow_total = data.cum_flow_total
# Create bindings for interval selection
scale_selection = alt.selection_interval(encodings=["x"], bind="scales")
# Line chart of price
price = (
(
alt.Chart(price)
.mark_line()
.encode(
x=alt.X(
"Date:T",
axis=alt.Axis(tickCount={"interval": "month", "step": 1}),
title="",
),
y=alt.Y("close:Q").scale(zero=False),
color=alt.value("crimson"),
)
)
.add_params(scale_selection)
.properties(
width=chart_size["width"],
height=chart_size["height"],
)
)
trading_vol_individual = (
(
alt.Chart(etf_volumes)
.transform_fold(
etf_volumes.drop(columns="Date").columns.to_list(),
as_=["Funds", "Volume"],
)
.mark_line()
.encode(
x=alt.X(
"Date:T",
axis=alt.Axis(tickCount={"interval": "month", "step": 1}),
title="",
),
y=alt.Y("Volume:Q", title="Trading Volume Individual"),
color="Funds:N",
)
)
.add_params(scale_selection)
.properties(
width=chart_size["width"],
height=chart_size["height"],
)
)
trading_vol_total = (
(
alt.Chart(etf_volumes)
.transform_fold(
etf_volumes.drop(columns="Date").columns.to_list(),
as_=["Funds", "Volume"],
)
.mark_rule()
.encode(
x=alt.X(
"Date:T",
axis=alt.Axis(tickCount={"interval": "month", "step": 1}),
title="",
),
y=alt.Y("sum(Volume):Q", title="Trading Volume Total"),
color=alt.value("teal"),
)
)
.add_params(scale_selection)
.properties(
width=chart_size["width"],
height=chart_size["height"],
)
)
# Net flow individual
net_flow_individual = (
(
alt.Chart(etf_flow_individual)
.transform_fold(
etf_flow_individual.drop(columns="Date").columns.to_list(),
as_=["Funds", "Net Flow"],
)
.mark_line()
.encode(
x=alt.X(
"Date:T",
axis=alt.Axis(tickCount={"interval": "month", "step": 1}),
title="",
),
y=alt.Y("Net Flow:Q", title="Net Flow Individual"),
color="Funds:N",
)
)
.add_params(scale_selection)
.properties(
width=chart_size["width"],
height=chart_size["height"],
)
)
net_flow_total = (
(
alt.Chart(etf_flow_total)
.mark_rule()
.encode(
x=alt.X(
"Date:T",
axis=alt.Axis(tickCount={"interval": "month", "step": 1}),
title="",
),
y=alt.Y("Total:Q", title="Net Flow Total"),
color=alt.condition(
alt.datum.Total > 0,
alt.value("seagreen"), # The positive color
alt.value("orangered"), # The negative color
),
)
)
.add_params(scale_selection)
.properties(
width=chart_size["width"],
height=chart_size["height"],
)
)
# Stacking area chart of flow from individual funds
cum_flow_individual = (
(
alt.Chart(cum_flow_individual)
.transform_fold(
cum_flow_individual.drop(columns="Date").columns.to_list(),
as_=["Funds", "Net Flow"],
)
.mark_area()
.encode(
x=alt.X(
"Date:T",
axis=alt.Axis(tickCount={"interval": "month", "step": 1}),
title="",
),
y=alt.Y("Net Flow:Q", title="Cumulative Flow Individual"),
color=alt.Color("Funds:N", scale=alt.Scale(scheme="tableau20")),
)
)
.add_params(scale_selection)
.properties(
width=chart_size["width"],
height=chart_size["height"],
)
)
# Area chart for cumulative flow
cum_flow_total = (
(
alt.Chart(cum_flow_total)
.transform_calculate(
negative="datum.Total < 0",
)
.mark_area()
.encode(
x=alt.X(
"Date:T",
axis=alt.Axis(tickCount={"interval": "month", "step": 1}),
title="",
),
y=alt.Y("Total:Q", title="Cumulative Flow Total", impute={"value": 0}),
color=alt.Color(
"negative:N", title="Negative Flow", scale=alt.Scale(scheme="set2")
),
)
)
.add_params(scale_selection)
.properties(
width=chart_size["width"],
height=chart_size["height"],
)
)
return SimpleNamespace(
price=price,
trading_vol_individual=trading_vol_individual,
trading_vol_total=trading_vol_total,
net_flow_individual=net_flow_individual,
net_flow_total=net_flow_total,
cum_flow_individual=cum_flow_individual,
cum_flow_total=cum_flow_total,
)
def asset_charts(asset: str, chart_size={"width": "container", "height": 150}):
charts = gen_charts(asset, chart_size)
# Vertical concat the charts in each asset into single column of that asset
all_charts = (
(
charts.price
& charts.trading_vol_individual
& charts.trading_vol_total
& charts.net_flow_individual
& charts.net_flow_total
& charts.cum_flow_individual
& charts.cum_flow_total
)
.resolve_scale(
color="independent",
)
.properties(
title=f"{asset} ETF",
)
)
return all_charts
def app():
# Set page config
st.set_page_config(layout="wide", page_icon="📈")
# Initialize pygwalker communication
init_streamlit_comm()
dashboard_tab, single_view, flow_tab, volume_tab, price_tab = st.tabs(
[
"Dashboard",
"View Single ETF",
"Explore ETF Flow",
"Explore ETF Volume",
"Explore ETF Asset Price",
]
)
btc = fetch_asset("BTC")
eth = fetch_asset("ETH")
with dashboard_tab:
btc_charts = asset_charts(
"BTC", chart_size={"width": "container", "height": 150}
)
eth_charts = asset_charts(
"ETH", chart_size={"width": "container", "height": 150}
)
# Display charts
btc_chart_col, eth_chart_col = st.columns(2)
with btc_chart_col:
st.altair_chart(btc_charts, use_container_width=True)
with eth_chart_col:
st.altair_chart(eth_charts, use_container_width=True)
# Display iframes
btc_col, eth_col = st.columns(2)
with btc_col:
html(btc.tdv, height=640)
iframe(btc.url, height=1200, scrolling=True)
with eth_col:
html(eth.tdv, height=640)
iframe(eth.url, height=1200, scrolling=True)
with single_view:
asset = st.selectbox(
"Asset to view",
("BTC", "ETH"),
)
charts = asset_charts(asset, chart_size={"width": "container", "height": 300})
st.altair_chart(charts, use_container_width=True)
match asset:
case "BTC":
html(btc.tdv, height=640)
case "ETH":
html(eth.tdv, height=640)
iframe(fetch_asset(asset).url, height=1200, scrolling=True)
with flow_tab:
btc_flow, eth_flow = btc.etf_flow, eth.etf_flow
btc_flow["Asset"] = "BTC"
eth_flow["Asset"] = "ETH"
df = pd.concat([btc_flow, eth_flow])
StreamlitRenderer(df).explorer()
with volume_tab:
btc_volume, eth_volume = btc.etf_volumes, eth.etf_volumes
btc_volume["Asset"] = "BTC"
eth_volume["Asset"] = "ETH"
df = pd.concat([btc_volume, eth_volume])
StreamlitRenderer(df).explorer()
with price_tab:
btc_price, eth_price = btc.price, eth.price
btc_price["Asset"] = "BTC"
eth_price["Asset"] = "ETH"
df = pd.concat([btc_price, eth_price])
StreamlitRenderer(df).explorer()
if __name__ == "__main__":
app()
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