Update asset liability page

src/page/asset_liability.py

@@ -1,22 +1,9 @@
-import asyncio
-from asyncio import AbstractEventLoop
-import os
-import time
-
-from anchorpy import Wallet
-from driftpy.account_subscription_config import AccountSubscriptionConfig
 from driftpy.constants.perp_markets import mainnet_perp_market_configs
 from driftpy.constants.spot_markets import mainnet_spot_market_configs
-from driftpy.drift_client import DriftClient
-from lib.page import RPC_STATE_KEY
-from lib.page import VAT_STATE_KEY
-from sections.asset_liab_matrix import get_matrix
-from solana.rpc.async_api import AsyncClient
+from lib.api import api
+import pandas as pd
 import streamlit as st
 
-from utils import load_newest_files
-from utils import load_vat
-
 
 options = [0, 1, 2, 3]
 labels = [
@@ -27,44 +14,32 @@
 ]
 
 
-def asset_liab_matrix_page():  # (loop: AbstractEventLoop, vat: Vat, drift_client: DriftClient, env='mainnet'):
-    st.write("Loading vat...")
-    rpc = st.session_state[RPC_STATE_KEY]
-    loop: AbstractEventLoop = asyncio.new_event_loop()
-    drift_client = DriftClient(
-        AsyncClient(rpc),
-        Wallet.dummy(),
-        account_subscription=AccountSubscriptionConfig("cached"),
-    )
-    loop: AbstractEventLoop = asyncio.new_event_loop()
-    newest_snapshot = load_newest_files(os.getcwd() + "/pickles")
-    start_load_vat = time.time()
-    vat = loop.run_until_complete(load_vat(drift_client, newest_snapshot))
-    st.session_state["vat"] = vat
-    st.write(f"loaded vat in {time.time() - start_load_vat}")
-    st.session_state[VAT_STATE_KEY] = vat
-
+def asset_liab_matrix_page():
     mode = st.selectbox("Options", options, format_func=lambda x: labels[x])
-
     if mode is None:
         mode = 0
 
-    perp_market_inspect = st.selectbox(
+    perp_market_index = st.selectbox(
         "Market index", [x.market_index for x in mainnet_perp_market_configs]
     )
-
-    if perp_market_inspect is None:
-        perp_market_inspect = 0
-
-    res, df = get_matrix(loop, vat, drift_client, "mainnet", mode, perp_market_inspect)
+    if perp_market_index is None:
+        perp_market_index = 0
+
+    result = api(
+        "asset-liability",
+        "matrix",
+        "0" if mode is None else str(mode),
+        "0" if perp_market_index is None else str(perp_market_index),
+        as_json=True,
+    )
+    res = pd.DataFrame(result["res"])
+    df = pd.DataFrame(result["df"])
 
     st.write(f"{df.shape[0]} users for scenario")
-
     st.write(res)
 
     tabs = st.tabs(["FULL"] + [x.symbol for x in mainnet_spot_market_configs])
-
-    tabs[0].dataframe(df)
+    tabs[0].dataframe(df, hide_index=True)
 
     for idx, tab in enumerate(tabs[1:]):
         important_cols = [x for x in df.columns if "spot_" + str(idx) in x]
@@ -73,4 +48,4 @@ def asset_liab_matrix_page():  # (loop: AbstractEventLoop, vat: Vat, drift_clien
             by="spot_" + str(idx) + "_all", ascending=False
         )
         tab.write(f"{ len(toshow)} users with this asset to cover liabilities")
-        tab.dataframe(toshow)
+        tab.dataframe(toshow, hide_index=True)

src/page/backend.py

@@ -0,0 +1,22 @@
+import pandas as pd
+import requests
+import streamlit as st
+
+
+def backend_page():
+
+    if st.button("Load Pickle"):
+        result = requests.get("http://localhost:8000/pickle")
+        st.write(result.json())
+
+    st.write(
+        """
+    ## Backend API
+
+    - [swagger](http://localhost:8000/docs)
+
+    - [redoc](http://localhost:8000/redoc)
+    """
+    )
+
+    st.title("Health")

src/page/health.py

@@ -1,36 +1,41 @@
+from lib.api import api
+import plotly.express as px
 import streamlit as st
 
-from lib.health_utils import (
-    get_account_health_distribution,
-    get_largest_perp_positions,
-    get_largest_spot_borrows,
-    get_most_levered_perp_positions_above_1m,
-    get_most_levered_spot_borrows_above_1m,
-)
-from lib.page import VAT_STATE_KEY
-
 
 def health_page():
-    vat = st.session_state[VAT_STATE_KEY]
-    health_distribution = get_account_health_distribution(vat)
+    health_distribution = api("health", "health_distribution")
+    largest_perp_positions = api("health", "largest_perp_positions")
+    most_levered_positions = api("health", "most_levered_perp_positions_above_1m")
+    largest_spot_borrows = api("health", "largest_spot_borrows")
+    most_levered_borrows = api("health", "most_levered_spot_borrows_above_1m")
+
+    fig = px.bar(
+        health_distribution,
+        x="Health Range",
+        y="Counts",
+        title="Health Distribution",
+        hover_data={"Notional Values": ":,"},  # Custom format for notional values
+        labels={"Counts": "Num Users", "Notional Values": "Notional Value ($)"},
+    )
+
+    fig.update_traces(
+        hovertemplate="<b>Health Range: %{x}</b><br>Count: %{y}<br>Notional Value: $%{customdata[0]:,.0f}<extra></extra>"
+    )
 
     with st.container():
-        st.plotly_chart(health_distribution, use_container_width=True)
+        st.plotly_chart(fig, use_container_width=True)
 
     perp_col, spot_col = st.columns([1, 1])
 
     with perp_col:
-        largest_perp_positions = get_largest_perp_positions(vat)
         st.markdown("### **Largest perp positions:**")
-        st.table(largest_perp_positions)
-        most_levered_positions = get_most_levered_perp_positions_above_1m(vat)
+        st.write(largest_perp_positions)
         st.markdown("### **Most levered perp positions > $1m:**")
-        st.table(most_levered_positions)
+        st.write(most_levered_positions)
 
     with spot_col:
-        largest_spot_borrows = get_largest_spot_borrows(vat)
         st.markdown("### **Largest spot borrows:**")
-        st.table(largest_spot_borrows)
-        most_levered_borrows = get_most_levered_spot_borrows_above_1m(vat)
+        st.write(largest_spot_borrows)
         st.markdown("### **Most levered spot borrows > $750k:**")
-        st.table(most_levered_borrows)
+        st.write(most_levered_borrows)

src/page/price_shock.py

@@ -8,16 +8,14 @@
 from driftpy.account_subscription_config import AccountSubscriptionConfig
 from driftpy.drift_client import DriftClient
 from driftpy.pickle.vat import Vat
+from lib.api import api
 from lib.page import RPC_STATE_KEY
 from lib.page import VAT_STATE_KEY
 from lib.user_metrics import get_usermap_df
 import pandas as pd
 from solana.rpc.async_api import AsyncClient
 import streamlit as st
 
-from utils import load_newest_files
-from utils import load_vat
-
 
 def price_shock_plot(price_scenario_users: list[Any], oracle_distort: float):
     levs = price_scenario_users
@@ -62,22 +60,6 @@ def price_shock_plot(price_scenario_users: list[Any], oracle_distort: float):
 
 
 def price_shock_page():
-
-    rpc = st.session_state[RPC_STATE_KEY]
-    loop: AbstractEventLoop = asyncio.new_event_loop()
-    drift_client = DriftClient(
-        AsyncClient(rpc),
-        Wallet.dummy(),
-        account_subscription=AccountSubscriptionConfig("cached"),
-    )
-    loop: AbstractEventLoop = asyncio.new_event_loop()
-    newest_snapshot = load_newest_files(os.getcwd() + "/pickles")
-    start_load_vat = time.time()
-    vat = loop.run_until_complete(load_vat(drift_client, newest_snapshot))
-    st.session_state["vat"] = vat
-    print(f"loaded vat in {time.time() - start_load_vat}")
-    st.session_state[VAT_STATE_KEY] = vat
-
     cov_col, distort_col = st.columns(2)
     cov = cov_col.selectbox(
         "covariance:",
@@ -96,24 +78,19 @@ def price_shock_page():
         help="step size of oracle distortions",
     )
 
-    user_keys = list(vat.users.user_map.keys())
-    st.write(len(user_keys), "drift users")
-    start_time = time.time()
-
-    price_scenario_users, user_keys, distorted_oracles = loop.run_until_complete(
-        get_usermap_df(drift_client, vat.users, "oracles", oracle_distort, None, cov)
-    )
-
-    end_time = time.time()
-    time_to_run = end_time - start_time
-    st.write(
-        time_to_run,
-        "seconds to run",
-        1 + len(price_scenario_users[1]) + len(price_scenario_users[2]),
-        "price-shock scenarios",
+    result = api(
+        "price-shock",
+        "usermap",
+        params={
+            "asset_group": cov,
+            "oracle_distortion": oracle_distort,
+            "n_scenarios": 5,
+        },
+        as_json=True,
     )
+    st.write(result)
 
-    price_shock_plot(price_scenario_users, oracle_distort)
+    # price_shock_plot(price_scenario_users, oracle_distort)
 
     # oracle_down_max = pd.DataFrame(price_scenario_users[-1][-1], index=user_keys)
     # with st.expander(