Update liquidation curves to show user keys

Author: SinaKhalili

backend/api/liquidation.py

@@ -11,11 +11,11 @@
 @router.get("/liquidation-curve")
 def get_liquidation_curve(request: BackendRequest, market_index: int):
     vat: Vat = request.state.backend_state.vat
-    liquidations_long: list[tuple[float, float]] = []
-    liquidations_short: list[tuple[float, float]] = []
+    liquidations_long: list[tuple[float, float, str]] = []
+    liquidations_short: list[tuple[float, float, str]] = []
     market_price = vat.perp_oracles.get(market_index)
     market_price_ui = market_price.price / PRICE_PRECISION
-    for user in vat.users.user_map.values():
+    for pubkey, user in vat.users.user_map.items():
         perp_position = user.get_perp_position(market_index)
         if perp_position is not None:
             liquidation_price = user.get_perp_liq_price(market_index)
@@ -29,9 +29,13 @@ def get_liquidation_curve(request: BackendRequest, market_index: int):
                 if is_zero:
                     continue
                 if is_short and liquidation_price_ui > market_price_ui:
-                    liquidations_short.append((liquidation_price_ui, position_notional))
+                    liquidations_short.append(
+                        (liquidation_price_ui, position_notional, str(pubkey))
+                    )
                 elif is_long and liquidation_price_ui < market_price_ui:
-                    liquidations_long.append((liquidation_price_ui, position_notional))
+                    liquidations_long.append(
+                        (liquidation_price_ui, position_notional, str(pubkey))
+                    )
                 else:
                     pass
 

src/page/liquidation_curves.py

@@ -19,27 +19,36 @@ def filter_outliers(
     ):
         """Filter out liquidations based on a range multiplier of the market price."""
         return [
-            (price, notional)
-            for price, notional in liquidations
+            (price, notional, pubkey)
+            for price, notional, pubkey in liquidations
             if lower_bound_multiplier * market_price_ui
             <= price
             <= upper_bound_multiplier * market_price_ui
         ]
 
     def aggregate_liquidations(liquidations):
-        """Aggregate liquidations to calculate cumulative notional amounts."""
-        price_to_notional = defaultdict(float)
-        for price, notional in liquidations:
-            price_to_notional[price] += notional
-        return price_to_notional
+        """Aggregate liquidations to calculate cumulative notional amounts and track pubkeys with their sizes."""
+        price_to_data = defaultdict(lambda: {"notional": 0.0, "positions": []})
+        for price, notional, pubkey in liquidations:
+            price_to_data[price]["notional"] += notional
+            price_to_data[price]["positions"].append(
+                (pubkey, notional)
+            )  # Store tuple of (pubkey, size)
+        return price_to_data
 
     def prepare_data_for_plot(aggregated_data, reverse=False):
         """Prepare and sort data for plotting, optionally reversing the cumulative sum for descending plots."""
         sorted_prices = sorted(aggregated_data.keys(), reverse=reverse)
         cumulative_notional = np.cumsum(
-            [aggregated_data[price] for price in sorted_prices]
+            [aggregated_data[price]["notional"] for price in sorted_prices]
         )
-        return sorted_prices, cumulative_notional
+        # Accumulate positions (pubkey and size) for each price point
+        cumulative_positions = []
+        current_positions = []
+        for price in sorted_prices:
+            current_positions.extend(aggregated_data[price]["positions"])
+            cumulative_positions.append(list(current_positions))
+        return sorted_prices, cumulative_notional, cumulative_positions
 
     # Filter outliers based on defined criteria
     liquidations_long = filter_outliers(
@@ -53,10 +62,12 @@ def prepare_data_for_plot(aggregated_data, reverse=False):
     aggregated_long = aggregate_liquidations(liquidations_long)
     aggregated_short = aggregate_liquidations(liquidations_short)
 
-    long_prices, long_cum_notional = prepare_data_for_plot(
+    long_prices, long_cum_notional, long_pubkeys = prepare_data_for_plot(
         aggregated_long, reverse=True
     )
-    short_prices, short_cum_notional = prepare_data_for_plot(aggregated_short)
+    short_prices, short_cum_notional, short_pubkeys = prepare_data_for_plot(
+        aggregated_short
+    )
 
     if not long_prices or not short_prices:
         return None
@@ -73,6 +84,8 @@ def prepare_data_for_plot(aggregated_data, reverse=False):
             mode="lines",
             name="Long Positions",
             line=dict(color="purple", width=2),
+            hovertemplate="Price: %{x}<br>Cumulative Notional: %{y}<br>Accounts: %{text}<extra></extra>",
+            text=[f"{len(pubkeys)} accounts" for pubkeys in long_pubkeys],
         )
     )
     short_fig.add_trace(
@@ -82,6 +95,8 @@ def prepare_data_for_plot(aggregated_data, reverse=False):
             mode="lines",
             name="Short Positions",
             line=dict(color="turquoise", width=2),
+            hovertemplate="Price: %{x}<br>Cumulative Notional: %{y}<br>Accounts: %{text}<extra></extra>",
+            text=[f"{len(pubkeys)} accounts" for pubkeys in short_pubkeys],
         )
     )
 
@@ -102,7 +117,7 @@ def prepare_data_for_plot(aggregated_data, reverse=False):
         yaxis=dict(showgrid=True),
     )
 
-    return long_fig, short_fig
+    return long_fig, short_fig, long_pubkeys, short_pubkeys, long_prices, short_prices
 
 
 def liquidation_curves_page():
@@ -140,12 +155,86 @@ def liquidation_curves_page():
         st.write(e)
         st.stop()
 
-    (long_fig, short_fig) = plot_liquidation_curves(liquidation_data)
+    # Unpack all returned values
+    (long_fig, short_fig, long_pubkeys, short_pubkeys, long_prices, short_prices) = (
+        plot_liquidation_curves(liquidation_data)
+    )
 
     long_col, short_col = st.columns([1, 1])
 
     with long_col:
         st.plotly_chart(long_fig, use_container_width=True)
 
+        # Add accordion for long positions
+        with st.expander("Long Position Accounts"):
+            if long_pubkeys and len(long_pubkeys[-1]) > 0:
+                st.write(f"Total Accounts: {len(long_pubkeys[-1])}")
+                long_data = []
+                for i, positions in enumerate(long_pubkeys):
+                    if i > 0:
+                        new_positions = set(positions) - set(long_pubkeys[i - 1])
+                        if new_positions:
+                            for pubkey, size in new_positions:
+                                long_data.append(
+                                    {
+                                        "Price": f"{long_prices[i]:.2f}",
+                                        "Size": f"{size:,.2f}",
+                                        "Account": pubkey,
+                                        "Link": f"https://app.drift.trade/overview?userAccount={pubkey}",
+                                    }
+                                )
+                if long_data:
+                    st.dataframe(
+                        long_data,
+                        column_config={
+                            "Price": st.column_config.TextColumn("Price"),
+                            "Size": st.column_config.TextColumn("Size"),
+                            "Account": st.column_config.TextColumn(
+                                "Account", width="large"
+                            ),
+                            "Link": st.column_config.LinkColumn(
+                                "Link", display_text="View"
+                            ),
+                        },
+                        hide_index=True,
+                    )
+            else:
+                st.write("No long positions found")
+
     with short_col:
         st.plotly_chart(short_fig, use_container_width=True)
+
+        with st.expander("Short Position Accounts"):
+            if short_pubkeys and len(short_pubkeys[-1]) > 0:
+                st.write(f"Total Accounts: {len(short_pubkeys[-1])}")
+                short_data = []
+                for i, positions in enumerate(short_pubkeys):
+                    if i > 0:
+                        new_positions = set(positions) - set(short_pubkeys[i - 1])
+                        if new_positions:
+                            for pubkey, size in new_positions:
+                                short_data.append(
+                                    {
+                                        "Price": f"{short_prices[i]:.2f}",
+                                        "Size": f"{size:,.2f}",
+                                        "Account": pubkey,
+                                        "Link": f"https://app.drift.trade/overview?userAccount={pubkey}",
+                                    }
+                                )
+                if short_data:
+                    st.dataframe(
+                        short_data,
+                        column_config={
+                            "Price": st.column_config.TextColumn("Price"),
+                            "Size": st.column_config.TextColumn("Size"),
+                            "Account": st.column_config.TextColumn(
+                                "Account", width="large"
+                            ),
+                            "Link": st.column_config.LinkColumn(
+                                "Link", display_text="View"
+                            ),
+                        },
+                        hide_index=True,
+                    )
+            else:
+                st.write("No short positions found")