Visualize liquidation map depth mode="gross_value"

README.md

@@ -2,4 +2,58 @@
 [![Format code](https://github.com/aoki-h-jp/py-liquidation-map/actions/workflows/Formatter.yml/badge.svg)](https://github.com/aoki-h-jp/py-liquidation-map/actions/workflows/Formatter.yml)
 
 # py-liquidation-map
-Visualize Liquidation Map from actual execution data.
+Visualize Liquidation Map from actual execution data. Supports for all historical data from binance and bybit. Receiving orders in real-time via websocket and drawing liquidation maps is being implemented.
+
+## Installation
+
+```bash
+pip install git+https://github.com/aoki-h-jp/py-liquidation-map
+```
+
+## Usage
+### Visualize liquidation map from historical data
+Download binance BTCUSDT data from start_datetime to end_datetime and draw a liquidation map calculated from orders above threshold=100000 [USDT].
+```python
+from liqmap.mapping import HistoricalMapping
+
+mapping = HistoricalMapping(
+    start_datetime='2023-08-01 00:00:00',
+    end_datetime='2023-08-01 06:00:00',
+    symbol='BTCUSDT',
+    exchange='binance',
+)
+
+mapping.liquidation_map_from_historical(
+    mode="gross_value",
+    threshold_gross_value=100000
+)
+```
+### Output
+![image](img/BTCUSDT_2023-08-01_00-00-00-2023-08-01_06-00-00_gross_value_100000.png)
+### Visualize liquidation map depth
+```python
+from liqmap.mapping import HistoricalMapping
+
+mapping = HistoricalMapping(
+    start_datetime='2023-08-01 00:00:00',
+    end_datetime='2023-08-01 06:00:00',
+    symbol='BTCUSDT',
+    exchange='binance',
+)
+
+mapping.liquidation_map_depth_from_historical(
+    mode="gross_value",
+    threshold_gross_value=100000
+)
+```
+
+### Output
+![image](img/BTCUSDT_2023-08-01_00-00-00-2023-08-01_06-00-00_gross_value_100000_depth.png)
+
+## If you want to report a bug or request a feature
+Please create an issue on this repository!
+
+## Disclaimer
+This project is for educational purposes only. You should not construe any such information or other material as legal, tax, investment, financial, or other advice. Nothing contained here constitutes a solicitation, recommendation, endorsement, or offer by me or any third party service provider to buy or sell any securities or other financial instruments in this or in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction.
+
+Under no circumstances will I be held responsible or liable in any way for any claims, damages, losses, expenses, costs, or liabilities whatsoever, including, without limitation, any direct or indirect damages for loss of profits.

liqmap/mapping.py

@@ -1,14 +1,18 @@
 # import standard libraries
 import datetime
 import math
+import warnings
 
 # import third-party libraries
 import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
 import pandas as pd
 from download import BinanceAggTradesDownload, BybitAggTradesDownloader
 from exceptions import ExchangeNotSupportedError
 from rich.progress import track
 
+warnings.filterwarnings("ignore")
+
 
 class HistoricalMapping:
     """
@@ -150,9 +154,18 @@ def _format_aggtrade_dataframe(self, filepath: str) -> pd.DataFrame:
 
         return df
 
+    @staticmethod
+    def human_format(x, pos):
+        if x < 1e6:
+            return str(x)
+        elif x < 1e9:
+            return '{:.1f}M'.format(x * 1e-6)
+        else:
+            return '{:.1f}B'.format(x * 1e-9)
+
     def liquidation_map_from_historical(
         self, mode="gross_value", threshold_gross_value=100000
-    ):
+    ) -> None:
         """
         Draw liquidation map from historical data
         :param mode: draw mode
@@ -391,8 +404,9 @@ def liquidation_map_from_historical(
             ),
             label="Current Price",
         )
+        ax2.xaxis.set_major_formatter(ticker.FuncFormatter(self.human_format))
         ax2.set_title("Estimated Liquidation Amount")
-        ax2.set_xlabel("Amount")
+        ax2.set_xlabel("Amount [USDT]")
         ax2.tick_params(axis="x", labelrotation=45)
         plt.legend(loc="upper right")
         plt.tight_layout()
@@ -404,3 +418,244 @@ def liquidation_map_from_historical(
             df_l.to_csv(
                 f"{save_title.replace('.png', '')}_{label.replace(' ','_')}_sell.csv"
             )
+
+    def liquidation_map_depth_from_historical(
+            self, mode="gross_value", threshold_gross_value=100000
+    ) -> None:
+        """
+        Draw liquidation map depth from historical data
+        :param mode:
+        :param threshold_gross_value:
+        :return:
+        """
+        # Downloading historical data
+        self._download()
+
+        # Formatting historical data
+        df_merged = pd.DataFrame()
+        for prefix in track(self._make_prefix_list(), description="Formatting data"):
+            df_prefix = self._format_aggtrade_dataframe(prefix)
+            df_merged = pd.concat([df_merged, df_prefix])
+
+        df_merged = df_merged.sort_values(by="timestamp")
+        df_merged = df_merged.reset_index(drop=True)
+        df_merged = df_merged[df_merged["timestamp"] <= self._end_datetime]
+        df_merged = df_merged[df_merged["timestamp"] >= self._start_datetime]
+
+        # Visualize liquidation map
+        # mode: gross_value
+        df_buy = df_merged[df_merged["side"] == "Buy"]
+        df_sell = df_merged[df_merged["side"] == "Sell"]
+
+        df_buy = df_buy[df_buy["amount"] >= threshold_gross_value]
+        df_sell = df_sell[df_sell["amount"] >= threshold_gross_value]
+
+        df_buy["LossCut100x"] = df_buy["price"] * 0.99
+        df_buy["LossCut50x"] = df_buy["price"] * 0.98
+        df_buy["LossCut25x"] = df_buy["price"] * 0.96
+        df_buy["LossCut10x"] = df_buy["price"] * 0.90
+
+        df_sell["LossCut100x"] = df_sell["price"] * 1.01
+        df_sell["LossCut50x"] = df_sell["price"] * 1.02
+        df_sell["LossCut25x"] = df_sell["price"] * 1.04
+        df_sell["LossCut10x"] = df_sell["price"] * 1.10
+
+        fig = plt.figure(figsize=(9, 9))
+        ax1 = fig.add_subplot(111)
+        ax2 = ax1.twinx()
+        plt.xlabel("datetime")
+        plt.ylabel("volume [USDT]")
+        plt.xticks(rotation=45)
+        title = f"{self._symbol}\n{self._start_datetime} -> {self._end_datetime}"
+        if mode == "gross_value":
+            title += f"\nthreshold: >= {threshold_gross_value} [USDT]"
+        plt.title(title)
+
+        df_losscut_10x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_buy.index):
+            df_losscut_10x.loc[len(df_losscut_10x)] = {
+                "price": df_buy.iloc[i, 8],
+                "amount": df_buy.iloc[i, 4],
+            }
+
+        df_losscut_25x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_buy.index):
+            df_losscut_25x.loc[len(df_losscut_25x)] = {
+                "price": df_buy.iloc[i, 7],
+                "amount": df_buy.iloc[i, 4],
+            }
+
+        df_losscut_50x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_buy.index):
+            df_losscut_50x.loc[len(df_losscut_50x)] = {
+                "price": df_buy.iloc[i, 6],
+                "amount": df_buy.iloc[i, 4],
+            }
+
+        df_losscut_100x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_buy.index):
+            df_losscut_100x.loc[len(df_losscut_100x)] = {
+                "price": df_buy.iloc[i, 5],
+                "amount": df_buy.iloc[i, 4],
+            }
+
+        current_price = df_merged.iloc[-1, 1]
+
+        df_depth_buy = pd.concat([df_losscut_10x, df_losscut_25x, df_losscut_50x, df_losscut_100x], ignore_index=True)
+        df_depth_buy = df_depth_buy.sort_values(by="price", ascending=False)
+        df_depth_buy = df_depth_buy[df_depth_buy["price"] <= current_price]
+        df_depth_buy = df_depth_buy.reset_index(drop=True)
+        df_depth_buy["price"] = df_depth_buy["price"].astype(float)
+        df_depth_buy["cumsum"] = df_depth_buy["amount"].cumsum().astype(float)
+        ax1.plot(df_depth_buy["price"], df_depth_buy["cumsum"], label="buy", c='b')
+
+        df_losscut_list = [
+            df_losscut_10x,
+            df_losscut_25x,
+            df_losscut_50x,
+            df_losscut_100x,
+        ]
+        labels = ["10x Leveraged", "25x Leveraged", "50x Leveraged", "100x Leveraged"]
+        colors = ["r", "g", "b", "y"]
+        tick_degits = 2 - math.ceil(
+            math.log10(df_merged["price"].max() - df_merged["price"].min())
+        )
+        max_amount = 0
+        for i, df_losscut in enumerate(df_losscut_list):
+            df_losscut = df_losscut[df_losscut["price"] <= current_price]
+            g_ids = int(
+                (
+                        round(df_losscut["price"].max(), tick_degits)
+                        - round(df_losscut["price"].min(), tick_degits)
+                )
+                * 10 ** tick_degits
+            )
+            bins = [
+                round(
+                    round(df_losscut["price"].min(), tick_degits)
+                    + i * 10 ** -tick_degits,
+                    tick_degits,
+                    )
+                for i in range(g_ids)
+            ]
+            df_losscut["group_id"] = pd.cut(df_losscut["price"], bins=bins)
+            agg_df = df_losscut.groupby("group_id").sum()
+            ax2.bar(
+                x=[f.left for f in agg_df.index],
+                height=agg_df["amount"],
+                width=10 ** -tick_degits,
+                color=colors[i],
+                label=labels[i],
+                alpha=0.5,
+            )
+            if agg_df["amount"].max() > max_amount:
+                max_amount = agg_df["amount"].max()
+
+        # Save liquidation map data as csv
+        save_title = f"{self._symbol}_{self._start_datetime.replace(' ', '_').replace(':', '-')}-{self._end_datetime.replace(' ', '_').replace(':', '-')}_{mode}_{threshold_gross_value}_depth.png"
+        for df_l, label in zip(df_losscut_list, labels):
+            df_l.to_csv(
+                f"{save_title.replace('.png', '')}_{label.replace(' ', '_')}_buy.csv"
+            )
+
+        # Sell liquidation map on ax2
+        df_losscut_10x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_sell.index):
+            df_losscut_10x.loc[len(df_losscut_10x)] = {
+                "price": df_sell.iloc[i, 8],
+                "amount": df_sell.iloc[i, 4],
+            }
+
+        df_losscut_25x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_sell.index):
+            df_losscut_25x.loc[len(df_losscut_25x)] = {
+                "price": df_sell.iloc[i, 7],
+                "amount": df_sell.iloc[i, 4],
+            }
+
+        df_losscut_50x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_sell.index):
+            df_losscut_50x.loc[len(df_losscut_50x)] = {
+                "price": df_sell.iloc[i, 6],
+                "amount": df_sell.iloc[i, 4],
+            }
+
+        df_losscut_100x = pd.DataFrame(columns=["price", "amount"])
+        for i, dt in enumerate(df_sell.index):
+            df_losscut_100x.loc[len(df_losscut_100x)] = {
+                "price": df_sell.iloc[i, 5],
+                "amount": df_sell.iloc[i, 4],
+            }
+
+        current_price = df_merged.iloc[-1, 1]
+
+        df_depth_sell = pd.concat([df_losscut_10x, df_losscut_25x, df_losscut_50x, df_losscut_100x], ignore_index=True)
+        df_depth_sell = df_depth_sell.sort_values(by="price")
+        df_depth_sell = df_depth_sell[df_depth_sell["price"] >= current_price]
+        df_depth_sell = df_depth_sell.reset_index(drop=True)
+        df_depth_sell["cumsum"] = df_depth_sell["amount"].cumsum()
+        ax1.plot(df_depth_sell["price"], df_depth_sell["cumsum"], label="sell", c='r')
+
+        df_losscut_list = [
+            df_losscut_10x,
+            df_losscut_25x,
+            df_losscut_50x,
+            df_losscut_100x,
+        ]
+        labels = ["10x Leveraged", "25x Leveraged", "50x Leveraged", "100x Leveraged"]
+        colors = ["r", "g", "b", "y"]
+        tick_degits = 2 - math.ceil(
+            math.log10(df_merged["price"].max() - df_merged["price"].min())
+        )
+        max_amount = 0
+        for i, df_losscut in enumerate(df_losscut_list):
+            df_losscut = df_losscut[df_losscut["price"] >= current_price]
+            g_ids = int(
+                (
+                        round(df_losscut["price"].max(), tick_degits)
+                        - round(df_losscut["price"].min(), tick_degits)
+                )
+                * 10 ** tick_degits
+            )
+            bins = [
+                round(
+                    round(df_losscut["price"].min(), tick_degits)
+                    + i * 10 ** -tick_degits,
+                    tick_degits,
+                    )
+                for i in range(g_ids)
+            ]
+            df_losscut["group_id"] = pd.cut(df_losscut["price"], bins=bins)
+            agg_df = df_losscut.groupby("group_id").sum()
+            ax2.bar(
+                x=[f.left for f in agg_df.index],
+                height=agg_df["amount"],
+                width=10 ** -tick_degits,
+                color=colors[i],
+                alpha=0.5,
+            )
+            if agg_df["amount"].max() > max_amount:
+                max_amount = agg_df["amount"].max()
+
+        ax1.yaxis.set_major_formatter(ticker.FuncFormatter(self.human_format))
+        ax2.yaxis.set_major_formatter(ticker.FuncFormatter(self.human_format))
+        plt.annotate(
+            "",
+            xytext=(current_price, max_amount),
+            xy=(current_price, 0),
+            arrowprops=dict(
+                arrowstyle="->,head_length=1,head_width=0.5", lw=2, linestyle="dashed"
+            ),
+            label="Current Price",
+        )
+        plt.legend(loc="upper right")
+        plt.xlabel("Price [USDT]")
+        plt.tight_layout()
+        plt.savefig(save_title)
+        plt.close()
+
+        # Save liquidation map data as csv
+        for df_l, label in zip(df_losscut_list, labels):
+            df_l.to_csv(
+                f"{save_title.replace('.png', '')}_{label.replace(' ', '_')}_sell.csv"
+            )