Merge pull request #27 from Synthetixio/feat/depth-report

Depth Report

dashboards/system_monitor/app.py

@@ -35,10 +35,11 @@ def load_api():
 core = st.Page("views/core.py", title="Core System")
 perps = st.Page("views/perps.py", title="Perps Markets")
 performance = st.Page("views/performance.py", title="Query Performance")
+depth = st.Page("views/depth.py", title="Market Depth")
 
 # navigation
 pages = {
-    "": [core, perps, performance],
+    "": [core, perps, performance, depth],
 }
 nav = st.navigation(pages)
 nav.run()

dashboards/system_monitor/views/depth.py

@@ -0,0 +1,298 @@
+import streamlit as st
+import pandas as pd
+
+from synthetix import Synthetix
+from synthetix.utils.multicall import multicall_erc7412, call_erc7412
+from synthetix.utils import ether_to_wei, wei_to_ether
+from api.internal_api import SynthetixAPI, get_db_config
+from dashboards.system_monitor.modules.settings import settings
+from dashboards.utils.charts import chart_lines, chart_bars
+
+
+PERPS_NETWORKS = [
+    8453,
+    84532,
+    42161,
+    421614,
+]
+
+ALL_USD_POSITION_SIZES = [-10000, -1000, -100, 100, 1000, 10000] + list(
+    range(-10000000, 10000000, 50000)
+)
+
+DISPLAY_USD_POSITION_SIZES = [
+    10000000,
+    5000000,
+    1000000,
+    500000,
+    100000,
+    10000,
+    1000,
+    100,
+    -100,
+    -1000,
+    -10000,
+    -100000,
+    -500000,
+    -1000000,
+    -5000000,
+    -10000000,
+]
+
+
+def plot_impact(df):
+    """Create a price impact analysis chart.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing the data for the chart.
+
+    Returns:
+        plotly.graph_objs._figure.Figure: The generated chart.
+    """
+    fig = chart_lines(
+        df,
+        x_col="order_size_usd",
+        y_cols="price_impact_pct",
+        title="Price Impact",
+        x_format="$",
+        y_format="%",
+        smooth=True,
+    )
+    # add axis labels
+    fig.update_xaxes(title_text="Order Size")
+    fig.update_yaxes(title_text="Price Impact %")
+    return fig
+
+
+def plot_depth(df):
+    """Create a depth analysis chart.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing the data for the chart.
+
+    Returns:
+        plotly.graph_objs._figure.Figure: The generated chart.
+    """
+    fig = chart_lines(
+        df,
+        x_col="order_size_usd",
+        y_cols="fill_price",
+        title="Depth",
+        x_format="$",
+        y_format="$",
+        smooth=True,
+    )
+    fig.update_xaxes(title_text="Order Size")
+    fig.update_yaxes(title_text="Fill Price")
+    return fig
+
+
+def get_markets(snx):
+    """Retrieve the markets from the Synthetix instance.
+
+    Args:
+        snx (Synthetix): The Synthetix instance.
+
+    Returns:
+        dict: A dictionary of markets by name.
+    """
+    return snx.perps.markets_by_name
+
+
+def get_market_info(snx, market_name):
+    """Retrieve market information for a specific market.
+
+    Args:
+        snx (Synthetix): The Synthetix instance.
+        market_name (str): The name of the market.
+
+    Returns:
+        pd.DataFrame: DataFrame containing the market information.
+    """
+    market_info = snx.perps.markets_by_name[market_name]
+
+    df_market_info = pd.DataFrame().from_dict(
+        {market_name: market_info}, orient="index"
+    )
+    df_market_info["skew_usd"] = df_market_info["skew"] * df_market_info["index_price"]
+    df_market_info = df_market_info[
+        [
+            "market_name",
+            "size",
+            "skew",
+            "skew_usd",
+            "skew_scale",
+            "maker_fee",
+            "taker_fee",
+            "max_open_interest",
+            "current_funding_rate",
+        ]
+    ]
+
+    # max leverage
+    liquidation_parameters = call_erc7412(
+        snx,
+        snx.perps.market_proxy,
+        "getLiquidationParameters",
+        (market_info["market_id"]),
+    )
+    min_initial_margin_ratio = wei_to_ether(liquidation_parameters[1])
+    max_leverage = 1 / min_initial_margin_ratio
+
+    pct_cols = ["current_funding_rate", "maker_fee", "taker_fee"]
+    df_market_info[pct_cols] = df_market_info[pct_cols].applymap(lambda x: f"{x:.4%}")
+
+    val_cols = ["size", "skew", "skew_scale", "max_open_interest"]
+    df_market_info[val_cols] = df_market_info[val_cols].applymap(lambda x: f"{x:.2f}")
+
+    df_market_info["skew_usd"] = df_market_info["skew_usd"].apply(
+        lambda x: f"${x:,.2f}"
+    )
+    df_market_info["max_leverage"] = f"{max_leverage:.2g}x"
+    return df_market_info.transpose()
+
+
+@st.cache_data(ttl=600, hash_funcs={Synthetix: lambda x: x.network_id})
+def get_depth(snx, market_name):
+    """Retrieve the depth information for a specific market.
+
+    Args:
+        snx (Synthetix): The Synthetix instance.
+        market_name (str): The name of the market.
+
+    Returns:
+        pd.DataFrame: DataFrame containing the depth information.
+    """
+    market_info = snx.perps.markets_by_name[market_name]
+    price = market_info["index_price"]
+    skew = market_info["skew"]
+    skew_usd = skew * price
+    funding_rate_24h = market_info["current_funding_rate"]
+    funding_rate_1h = funding_rate_24h / 24
+
+    # check the market depth at various sizes
+    position_sizes_usd = ALL_USD_POSITION_SIZES + [-skew_usd]
+    position_sizes_usd.sort(reverse=True)
+    position_sizes = [size / price for size in position_sizes_usd]
+
+    order_fees_result = multicall_erc7412(
+        snx,
+        snx.perps.market_proxy,
+        "computeOrderFeesWithPrice",
+        [
+            (market_info["market_id"], ether_to_wei(size), ether_to_wei(price))
+            for size in position_sizes
+        ],
+    )
+
+    settlement_reward_cost = call_erc7412(
+        snx,
+        snx.perps.market_proxy,
+        "getSettlementRewardCost",
+        (market_info["market_id"], 0),
+    )
+    settlement_reward_cost = wei_to_ether(settlement_reward_cost)
+
+    depths = {}
+    for size_usd, size, fee_result in zip(
+        position_sizes_usd, position_sizes, order_fees_result
+    ):
+        order_fees = wei_to_ether(fee_result[0])
+        fill_price = wei_to_ether(fee_result[1])
+
+        depths[size_usd] = {
+            "order_size_usd": size_usd,
+            "order_size": size,
+            "order_fees": order_fees,
+            "fill_price": fill_price,
+            "index_price": price,
+            "settlement_reward_cost": settlement_reward_cost,
+        }
+
+    df = pd.DataFrame().from_dict(depths, orient="index")
+    df = df.reset_index()
+
+    # add columns
+    df["total_fee_usd"] = df["order_fees"] + df["settlement_reward_cost"]
+    df["premium_discount_pct"] = (df["fill_price"] - df["index_price"]) / df[
+        "index_price"
+    ]
+    df["price_impact_pct"] = (
+        abs(df["fill_price"] - df["index_price"]) / df["index_price"]
+    )
+    df["order_fee_pct"] = abs(df["order_fees"] / df["order_size_usd"])
+    df["settlement_fee_pct"] = df["settlement_reward_cost"] / df["order_size_usd"]
+    df["total_fee_pct"] = df["total_fee_usd"] / df["order_size_usd"]
+    df["funding_per_hour_usd"] = funding_rate_1h * df["order_size_usd"]
+
+    cols = [
+        "order_size_usd",
+        "index_price",
+        "fill_price",
+        "premium_discount_pct",
+        "price_impact_pct",
+        "total_fee_usd",
+        "total_fee_pct",
+        "funding_per_hour_usd",
+        "order_fees",
+        "order_fee_pct",
+        "settlement_reward_cost",
+        "settlement_fee_pct",
+    ]
+    return df[cols]
+
+
+def format_depth(df):
+    """Format the depth DataFrame for display.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing the depth information.
+
+    Returns:
+        pd.DataFrame: Formatted DataFrame.
+    """
+    df = df.copy()
+    pct_cols = [c for c in df.columns if c.endswith("_pct")]
+    df[pct_cols] = df[pct_cols].applymap(lambda x: f"{x:.3%}")
+
+    usd_cols = [
+        c
+        for c in df.columns
+        if c.endswith("_usd") or c.endswith("_fees") or c.endswith("_price")
+    ]
+    df[usd_cols] = df[usd_cols].applymap(lambda x: f"${x:,.2f}")
+    return df
+
+
+# add the settings dropdown
+settings(enabled_markets=PERPS_NETWORKS)
+
+markets = get_markets(st.session_state.snx)
+
+st.selectbox("Market", list(markets.keys()), key="market_name")
+
+st.markdown("## Market Depth")
+df_depth = get_depth(st.session_state.snx, st.session_state.market_name)
+
+st.dataframe(
+    format_depth(df_depth[df_depth["order_size_usd"].isin(DISPLAY_USD_POSITION_SIZES)]),
+    use_container_width=True,
+)
+
+st.markdown("#### Max Maker Order")
+st.dataframe(
+    format_depth(df_depth[~df_depth["order_size_usd"].isin(ALL_USD_POSITION_SIZES)]),
+    use_container_width=True,
+)
+
+df_market_info = get_market_info(st.session_state.snx, st.session_state.market_name)
+st.dataframe(df_market_info)
+
+# charts
+col1, col2 = st.columns(2)
+
+with col1:
+    st.plotly_chart(plot_impact(df_depth), use_container_width=True)
+
+with col2:
+    st.plotly_chart(plot_depth(df_depth), use_container_width=True)

dashboards/system_monitor/views/perps.py

@@ -119,6 +119,7 @@ def clean_markets(configs):
         axis=1,
     )
     configs["oi_used_pct"] = configs["oi_used"].map("{:.2%}".format)
+    configs.sort_values("open_interest", ascending=False, inplace=True)
     return configs