Merge pull request #109 from Synthetixio/feat/add-arbitrum-perps

Add Arbitrum Perps

dashboard/modules/all_chains/perp_stats.py

@@ -50,10 +50,24 @@ def fetch_data(filters):
             FROM {st.secrets.database.DB_ENV}_optimism_mainnet.fct_v2_stats_{resolution}_optimism_mainnet
             where ts >= '{filters["start_date"]}'
                 and ts <= '{filters["end_date"]}'
+        ),
+        arbitrum as (
+            SELECT
+                ts,
+                'Arbitrum (V3)' as label,
+                volume,
+                trades,
+                exchange_fees as fees,
+                liquidated_accounts as liquidations
+            FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_stats_{resolution}_arbitrum_mainnet
+            WHERE ts >= '{start_date}' and ts <= '{end_date}'
+
         )
         select * from base
         union all
         select * from optimism
+        union all
+        select * from arbitrum
         order by ts
         """,
         db,

dashboard/modules/arbitrum_mainnet/perp_account.py

@@ -0,0 +1,309 @@
+import streamlit as st
+import pandas as pd
+import sqlite3
+import plotly.express as px
+from datetime import datetime, timedelta
+from utils import get_connection
+from utils import chart_bars, chart_lines, export_data
+
+## set default filters
+filters = {
+    "account_id": "NULL",
+    "start_date": datetime.today().date() - timedelta(days=14),
+    "end_date": datetime.today().date() + timedelta(days=1),
+}
+
+
+## data
+@st.cache_data(ttl=60)
+def fetch_data(filters):
+    # get filters
+    account_id = filters["account_id"]
+    start_date = filters["start_date"]
+    end_date = filters["end_date"]
+
+    # initialize connection
+    db = get_connection()
+
+    # read data
+    df_order = pd.read_sql_query(
+        f"""
+        SELECT distinct account_id, sender FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_orders_arbitrum_mainnet
+    """,
+        db,
+    )
+    df_order_expired = pd.read_sql_query(
+        f"""
+        SELECT
+            block_timestamp,
+            cast(account_id as text) as account_id,
+            market_id,
+            acceptable_price,
+            commitment_time
+        FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_previous_order_expired_arbitrum_mainnet
+        WHERE account_id = {account_id if account_id else 'NULL'}
+        and date(block_timestamp) >= '{start_date}' and date(block_timestamp) <= '{end_date}'
+    """,
+        db,
+    )
+    df_trade = pd.read_sql_query(
+        f"""
+        SELECT
+            ts,
+            cast(account_id as text) as account_id,
+            market_id,
+            market_symbol,
+            position_size,
+            notional_position_size,
+            trade_size,
+            notional_trade_size,
+            fill_price,
+            total_fees,
+            accrued_funding,
+            tracking_code
+        FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_trades_arbitrum_mainnet
+        WHERE account_id = '{account_id}'
+        and ts >= '{start_date}' and ts <= '{end_date}'
+    """,
+        db,
+    )
+    df_transfer = pd.read_sql_query(
+        f"""
+        SELECT
+            block_timestamp,
+            cast(account_id as text) as account_id,
+            synth_market_id,
+            amount_delta
+        FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_collateral_modified_arbitrum_mainnet
+        WHERE account_id = {account_id if account_id else 'NULL'}
+        and date(block_timestamp) >= '{start_date}' and date(block_timestamp) <= '{end_date}'
+    """,
+        db,
+    )
+    df_interest = pd.read_sql_query(
+        f"""
+        SELECT
+            block_timestamp,
+            transaction_hash,
+            cast(account_id as text) as account_id,
+            interest
+        FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_interest_charged_arbitrum_mainnet
+        WHERE account_id = {account_id if account_id else 'NULL'}
+        and date(block_timestamp) >= '{start_date}' and date(block_timestamp) <= '{end_date}'
+    """,
+        db,
+    )
+
+    df_account_liq = pd.read_sql_query(
+        f"""
+        SELECT
+            ts,
+            account_id,
+            total_reward
+        FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_liq_account_arbitrum_mainnet
+        WHERE account_id = '{account_id}'
+        and ts >= '{start_date}' and ts <= '{end_date}'
+    """,
+        db,
+    )
+    df_hourly = pd.read_sql_query(
+        f"""
+        SELECT
+            ts,
+            cumulative_volume,
+            cumulative_fees
+        FROM {st.secrets.database.DB_ENV}_arbitrum_mainnet.fct_perp_account_stats_hourly_arbitrum_mainnet
+        WHERE account_id = '{account_id}'
+        and ts >= '{start_date}' and ts <= '{end_date}'
+        order by ts
+    """,
+        db,
+    )
+
+    df_accounts = df_order[["account_id", "sender"]].drop_duplicates()
+    df_accounts.columns = ["id", "owner"]
+
+    # one basic transform
+    df_transfer["amount_delta"] = df_transfer["amount_delta"] / 1e18
+
+    db.close()
+
+    return {
+        "accounts": df_accounts,
+        "order_expired": df_order_expired,
+        "interest": df_interest,
+        "trade": df_trade,
+        "transfer": df_transfer,
+        "account_liq": df_account_liq,
+        "hourly": df_hourly,
+    }
+
+
+def make_charts(data):
+    return {
+        "cumulative_volume": chart_lines(
+            data["hourly"], "ts", ["cumulative_volume"], "Cumulative Volume"
+        ),
+        "cumulative_fees": chart_lines(
+            data["hourly"], "ts", ["cumulative_fees"], "Cumulative Fees"
+        ),
+    }
+
+
+def main():
+    st.markdown("## V3 Perps Accounts")
+    data = fetch_data(filters)
+
+    ## date filter
+    with st.expander("Date filter"):
+        filt_col1, filt_col2 = st.columns(2)
+        with filt_col1:
+            filters["start_date"] = st.date_input("Start", filters["start_date"])
+
+        with filt_col2:
+            filters["end_date"] = st.date_input("End", filters["end_date"])
+
+    ## account lookup
+    with st.expander("Look up accounts by address"):
+        address = st.text_input("Enter an address to look up associated accounts")
+
+        df_accounts = data["accounts"]
+        account_numbers = df_accounts[(df_accounts["owner"] == address)]["id"].unique()
+
+        # account_numbers
+        if len(account_numbers) > 0:
+            st.dataframe(account_numbers, hide_index=True)
+
+    ## account select
+    accounts = data["accounts"]["id"].unique()
+    accounts = sorted(list([int(_) for _ in accounts]))
+    filters["account_id"] = st.selectbox("Select account", accounts, index=0)
+    data = fetch_data(filters)
+
+    ## do some lighter transforms
+    df_open_positions = (
+        data["trade"]
+        .sort_values("ts")
+        .groupby(["account_id", "market_id"])
+        .last()
+        .reset_index()
+    )
+    df_open_positions = df_open_positions[df_open_positions["position_size"].abs() > 0]
+
+    ## make the charts
+    charts = make_charts(data)
+
+    ## display
+    # Open positions
+    df_open_account = df_open_positions[
+        df_open_positions["account_id"] == filters["account_id"]
+    ]
+
+    last_liq = (
+        data["account_liq"]
+        .loc[data["account_liq"]["account_id"] == filters["account_id"], "ts"]
+        .max()
+    )
+
+    # this is a hack to handle the case where there are no liquidations
+    last_liq = last_liq if pd.isna(last_liq) == False else "2023-01-01 00:00:00+00:00"
+
+    df_open_account = df_open_account.loc[
+        df_open_account["ts"] > last_liq,
+        ["account_id", "market_symbol", "position_size", "notional_position_size"],
+    ]
+
+    st.markdown(
+        """
+    ### Open Positions
+    """
+    )
+    if len(df_open_account) > 0:
+        df_open_account
+    else:
+        st.markdown(
+            """
+        No open positions
+        """
+        )
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.plotly_chart(charts["cumulative_volume"], use_container_width=True)
+        pass
+
+    with col2:
+        st.plotly_chart(charts["cumulative_fees"], use_container_width=True)
+        pass
+
+    # Recent trades
+    st.markdown(
+        """
+    ### Recent Trades
+    """
+    )
+
+    st.dataframe(
+        data["trade"].sort_values("ts", ascending=False).head(50),
+        use_container_width=True,
+        hide_index=True,
+    )
+
+    # Recent transfers
+    st.markdown(
+        """
+    ### Recent Transfers
+    """
+    )
+
+    st.dataframe(
+        data["transfer"].sort_values("block_timestamp", ascending=False).head(50),
+        use_container_width=True,
+        hide_index=True,
+    )
+
+    # Account liquidations table
+    st.markdown(
+        """
+    ### Liquidations
+    """
+    )
+
+    st.dataframe(
+        data["account_liq"].sort_values("ts", ascending=False).head(25),
+        use_container_width=True,
+        hide_index=True,
+    )
+
+    # Expired orders table
+    st.markdown(
+        """
+    ### Expired Orders
+    """
+    )
+
+    st.dataframe(
+        data["order_expired"],
+        use_container_width=True,
+        hide_index=True,
+    )
+
+    # Interest charged table
+    st.markdown(
+        """
+    ### Interest Charged
+    """
+    )
+
+    st.dataframe(
+        data["interest"],
+        use_container_width=True,
+        hide_index=True,
+    )
+
+    ## export
+    exports = [{"title": export, "df": data[export]} for export in data.keys()]
+    with st.expander("Exports"):
+        for export in exports:
+            export_data(export["title"], export["df"])