{ "cells": [ { "cell_type": "markdown", "id": "79d9aaef", "metadata": {}, "source": [ "# Working with Market Depth and Trades" ] }, { "cell_type": "markdown", "id": "d0585554", "metadata": {}, "source": [ "## Display 3-depth" ] }, { "cell_type": "code", "execution_count": 1, "id": "8bbb4d6f-7a84-4fec-ac56-24a3d2b3d78a", "metadata": {}, "outputs": [], "source": [ "from numba import njit\n", "\n", "@njit\n", "def print_3depth(hbt):\n", " while hbt.elapse(60_000_000_000) == 0:\n", " print('current_timestamp:', hbt.current_timestamp)\n", "\n", " # Gets the market depth for the first asset, in the same order as when you created the backtest.\n", " depth = hbt.depth(0)\n", "\n", " # a key of bid_depth or ask_depth is price in ticks.\n", " # (integer) price_tick = rice / tick_size\n", " i = 0\n", " for price_tick in range(depth.best_ask_tick, depth.best_ask_tick + 100):\n", " qty = depth.ask_qty_at_tick(price_tick)\n", " if qty > 0:\n", " print(\n", " 'ask: ',\n", " qty,\n", " '@',\n", " np.round(price_tick * depth.tick_size, 1)\n", " )\n", " \n", " i += 1\n", " if i == 3:\n", " break\n", " i = 0\n", " for price_tick in range(depth.best_bid_tick, max(depth.best_bid_tick - 100, 0), -1):\n", " qty = depth.bid_qty_at_tick(price_tick)\n", " if qty > 0:\n", " print(\n", " 'bid: ',\n", " qty,\n", " '@',\n", " np.round(price_tick * depth.tick_size, 1)\n", " )\n", " \n", " i += 1\n", " if i == 3:\n", " break\n", " return True" ] }, { "cell_type": "code", "execution_count": 2, "id": "0aab2f88", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "btcusdt_20240809 = np.load('usdm/btcusdt_20240809.npz')['data']\n", "btcusdt_20240808_eod = np.load('usdm/btcusdt_20240808_eod.npz')['data']" ] }, { "cell_type": "code", "execution_count": 3, "id": "79afc7c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "current_timestamp: 1723161661500000000\n", "ask: 1.759 @ 61594.2\n", "ask: 0.006 @ 61594.4\n", "ask: 0.114 @ 61595.2\n", "bid: 3.526 @ 61594.1\n", "bid: 0.016 @ 61594.0\n", "bid: 0.002 @ 61593.9\n", "current_timestamp: 1723161721500000000\n", "ask: 2.575 @ 61576.6\n", "ask: 0.004 @ 61576.7\n", "ask: 0.455 @ 61577.0\n", "bid: 2.558 @ 61576.5\n", "bid: 0.002 @ 61576.0\n", "bid: 0.515 @ 61575.5\n", "current_timestamp: 1723161781500000000\n", "ask: 0.131 @ 61629.7\n", "ask: 0.005 @ 61630.1\n", "ask: 0.005 @ 61630.5\n", "bid: 5.742 @ 61629.6\n", "bid: 0.247 @ 61629.4\n", "bid: 0.034 @ 61629.3\n", "current_timestamp: 1723161841500000000\n", "ask: 0.202 @ 61621.6\n", "ask: 0.002 @ 61622.5\n", "ask: 0.003 @ 61622.6\n", "bid: 3.488 @ 61621.5\n", "bid: 0.86 @ 61620.0\n", "bid: 0.248 @ 61619.6\n", "current_timestamp: 1723161901500000000\n", "ask: 1.397 @ 61584.0\n", "ask: 0.832 @ 61585.1\n", "ask: 0.132 @ 61586.0\n", "bid: 3.307 @ 61583.9\n", "bid: 0.01 @ 61583.8\n", "bid: 0.002 @ 61582.0\n" ] } ], "source": [ "from hftbacktest import BacktestAsset, HashMapMarketDepthBacktest\n", "\n", "asset = (\n", " BacktestAsset()\n", " .data(btcusdt_20240809)\n", " .initial_snapshot(btcusdt_20240808_eod)\n", " .linear_asset(1.0) \n", " .constant_latency(10_000_000, 10_000_000)\n", " .risk_adverse_queue_model() \n", " .no_partial_fill_exchange()\n", " .trading_value_fee_model(0.0002, 0.0007)\n", " .tick_size(0.1)\n", " .lot_size(0.001)\n", ")\n", "\n", "hbt = HashMapMarketDepthBacktest([asset])\n", "\n", "print_3depth(hbt)\n", "\n", "_ = hbt.close()" ] }, { "cell_type": "markdown", "id": "354e8fec-95cb-4205-b820-f934fa2d5836", "metadata": {}, "source": [ "## Efficient Market Depth Access\n", "\n", "`ROIVectorMarketDepth` provides more efficient market depth access through a vector that holds a limited price range of interest. The backtester using this feature can be created by `ROIVectorMarketDepthBacktest`." ] }, { "cell_type": "code", "execution_count": 4, "id": "580a566c-6520-40b0-90ec-c2a933017b52", "metadata": {}, "outputs": [], "source": [ "from numba import njit\n", "\n", "@njit\n", "def print_3depth_fast(hbt):\n", " roi_lb_tick = int(round(30000 / 0.1))\n", " roi_ub_tick = int(round(90000 / 0.1))\n", " \n", " while hbt.elapse(60_000_000_000) == 0:\n", " print('current_timestamp:', hbt.current_timestamp)\n", "\n", " # Gets the market depth for the first asset, in the same order as when you created the backtest.\n", " depth = hbt.depth(0)\n", "\n", " # a key of bid_depth or ask_depth is price in ticks.\n", " # (integer) price_tick = price / tick_size\n", " i = 0\n", " # for price_tick in range(depth.best_ask_tick, depth.best_ask_tick + 100):\n", " # # depth.ask_depth returns the ask depth array, whose length is (roi_ub_tick + 1 - roi_lb_tick),\n", " # # containing the quantities ranging from roi_lb_tick to roi_ub_tick.\n", " # # Checks that the price_tick is in that range and adjust the index by subtracting roi_lb_tick.\n", " # if price_tick < roi_lb_tick or price_tick > roi_ub_tick:\n", " # continue\n", " # t = price_tick - roi_lb_tick\n", " # qty = depth.ask_depth[t]\n", " # if qty > 0:\n", " # print(\n", " # 'ask: ',\n", " # qty,\n", " # '@',\n", " # np.round(price_tick * depth.tick_size, 1)\n", " # )\n", " \n", " # i += 1\n", " # if i == 3:\n", " # break\n", " # i = 0\n", " # for price_tick in range(depth.best_bid_tick, max(depth.best_bid_tick - 100, 0), -1):\n", " # # depth.bid_depth returns the bid depth array, whose length is (roi_ub_tick + 1 - roi_lb_tick),\n", " # # containing the quantities ranging from roi_lb_tick to roi_ub_tick.\n", " # # Checks that the price_tick is in that range and adjust the index by subtracting roi_lb_tick.\n", " # if price_tick < roi_lb_tick or price_tick > roi_ub_tick:\n", " # continue\n", " # t = price_tick - roi_lb_tick\n", " # qty = depth.bid_depth[t]\n", " # if qty > 0:\n", " # print(\n", " # 'bid: ',\n", " # qty,\n", " # '@',\n", " # np.round(price_tick * depth.tick_size, 1)\n", " # )\n", " \n", " # i += 1\n", " # if i == 3:\n", " # break\n", " return True" ] }, { "cell_type": "code", "execution_count": 5, "id": "94d2e7a7-9179-4380-8e18-661833a4b95f", "metadata": {}, "outputs": [], "source": [ "from hftbacktest import ROIVectorMarketDepthBacktest\n", "\n", "asset = (\n", " BacktestAsset()\n", " .data(btcusdt_20240809)\n", " .initial_snapshot(btcusdt_20240808_eod)\n", " .linear_asset(1.0) \n", " .constant_latency(10_000_000, 10_000_000)\n", " .risk_adverse_queue_model() \n", " .no_partial_fill_exchange()\n", " .trading_value_fee_model(0.0002, 0.0007)\n", " .tick_size(0.1)\n", " .lot_size(0.001)\n", " # Sets the lower bound price for the range of interest in the market depth.\n", " .roi_lb(30000)\n", " # Sets the upper bound price for the range of interest in the market depth.\n", " .roi_ub(90000)\n", ")\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "a8e2b8bc-eb4f-4401-9d1c-d4e5428708e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "current_timestamp: 1723161661500000000\n", "current_timestamp: 1723161721500000000\n", "current_timestamp: 1723161781500000000\n", "current_timestamp: 1723161841500000000\n", "current_timestamp: 1723161901500000000\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hbt = ROIVectorMarketDepthBacktest([asset])\n", "\n", "print_3depth_fast(hbt)\n", "\n", "#_ = hbt.close()" ] }, { "cell_type": "markdown", "id": "a8f00571", "metadata": {}, "source": [ "## Order Book Imbalance" ] }, { "cell_type": "code", "execution_count": 7, "id": "e8d55680", "metadata": {}, "outputs": [], "source": [ "@njit\n", "def orderbookimbalance(hbt, out):\n", " roi_lb_tick = int(round(30000 / 0.1))\n", " roi_ub_tick = int(round(90000 / 0.1))\n", "\n", " while hbt.elapse(10 * 1e9) == 0:\n", " depth = hbt.depth(0)\n", " \n", " mid_price = (depth.best_bid + depth.best_ask) / 2.0\n", " \n", " sum_ask_qty_50bp = 0.0\n", " sum_ask_qty = 0.0\n", " for price_tick in range(depth.best_ask_tick, roi_ub_tick + 1):\n", " if price_tick < roi_lb_tick or price_tick > roi_ub_tick:\n", " continue\n", " t = price_tick - roi_lb_tick\n", " \n", " ask_price = price_tick * depth.tick_size\n", " depth_from_mid = (ask_price - mid_price) / mid_price\n", " if depth_from_mid > 0.01:\n", " break\n", " sum_ask_qty += depth.ask_depth[t]\n", " \n", " if depth_from_mid <= 0.005:\n", " sum_ask_qty_50bp = sum_ask_qty\n", " \n", " \n", " sum_bid_qty_50bp = 0.0\n", " sum_bid_qty = 0.0\n", " for price_tick in range(depth.best_bid_tick, roi_lb_tick - 1, -1):\n", " if price_tick < roi_lb_tick or price_tick > roi_ub_tick:\n", " continue\n", " t = price_tick - roi_lb_tick\n", " \n", " bid_price = price_tick * depth.tick_size\n", " depth_from_mid = (mid_price - bid_price) / mid_price\n", " if depth_from_mid > 0.01:\n", " break\n", " sum_bid_qty += depth.bid_depth[t]\n", " \n", " if depth_from_mid <= 0.005:\n", " sum_bid_qty_50bp = sum_bid_qty\n", " \n", " imbalance_50bp = sum_bid_qty_50bp - sum_ask_qty_50bp\n", " imbalance_1pct = sum_bid_qty - sum_ask_qty\n", " imbalance_tob = depth.bid_depth[depth.best_bid_tick - roi_lb_tick] - depth.ask_depth[depth.best_ask_tick - roi_lb_tick]\n", " \n", " out.append((hbt.current_timestamp, imbalance_tob, imbalance_50bp, imbalance_1pct)) \n", " return True" ] }, { "cell_type": "code", "execution_count": 8, "id": "99a684f8", "metadata": {}, "outputs": [], "source": [ "from numba.typed import List\n", "from numba.types import Tuple, float64\n", "\n", "hbt = ROIVectorMarketDepthBacktest([asset])\n", "\n", "tup_ty = Tuple((float64, float64, float64, float64))\n", "out = List.empty_list(tup_ty, allocated=100_000)\n", "\n", "orderbookimbalance(hbt, out)\n", "\n", "_ = hbt.close()" ] }, { "cell_type": "code", "execution_count": 9, "id": "be1b53b2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (30, 4)
Local TimestampTOB Imbalance0.5% Imbalance1% Imbalance
datetime[ns]f64f64f64
2024-08-09 00:00:11.5002.729-1748.101-3908.736
2024-08-09 00:00:21.5004.623-1749.435-3512.845
2024-08-09 00:00:31.500-6.465-1259.897-3357.755
2024-08-09 00:00:41.500-7.922-1174.185-3471.955
2024-08-09 00:00:51.500-2.484-1147.597-3461.48
…………
2024-08-09 00:04:21.5003.828-1186.236-3551.78
2024-08-09 00:04:31.500-1.35-1332.379-3517.854
2024-08-09 00:04:41.500-3.754-1166.521-2693.672
2024-08-09 00:04:51.500-2.525-1188.56-2716.914
2024-08-09 00:05:01.5001.91-594.991-2138.82
" ], "text/plain": [ "shape: (30, 4)\n", "┌─────────────────────────┬───────────────┬────────────────┬──────────────┐\n", "│ Local Timestamp ┆ TOB Imbalance ┆ 0.5% Imbalance ┆ 1% Imbalance │\n", "│ --- ┆ --- ┆ --- ┆ --- │\n", "│ datetime[ns] ┆ f64 ┆ f64 ┆ f64 │\n", "╞═════════════════════════╪═══════════════╪════════════════╪══════════════╡\n", "│ 2024-08-09 00:00:11.500 ┆ 2.729 ┆ -1748.101 ┆ -3908.736 │\n", "│ 2024-08-09 00:00:21.500 ┆ 4.623 ┆ -1749.435 ┆ -3512.845 │\n", "│ 2024-08-09 00:00:31.500 ┆ -6.465 ┆ -1259.897 ┆ -3357.755 │\n", "│ 2024-08-09 00:00:41.500 ┆ -7.922 ┆ -1174.185 ┆ -3471.955 │\n", "│ 2024-08-09 00:00:51.500 ┆ -2.484 ┆ -1147.597 ┆ -3461.48 │\n", "│ … ┆ … ┆ … ┆ … │\n", "│ 2024-08-09 00:04:21.500 ┆ 3.828 ┆ -1186.236 ┆ -3551.78 │\n", "│ 2024-08-09 00:04:31.500 ┆ -1.35 ┆ -1332.379 ┆ -3517.854 │\n", "│ 2024-08-09 00:04:41.500 ┆ -3.754 ┆ -1166.521 ┆ -2693.672 │\n", "│ 2024-08-09 00:04:51.500 ┆ -2.525 ┆ -1188.56 ┆ -2716.914 │\n", "│ 2024-08-09 00:05:01.500 ┆ 1.91 ┆ -594.991 ┆ -2138.82 │\n", "└─────────────────────────┴───────────────┴────────────────┴──────────────┘" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import polars as pl\n", "\n", "df = pl.DataFrame(out).transpose()\n", "df.columns = ['Local Timestamp', 'TOB Imbalance', '0.5% Imbalance', '1% Imbalance']\n", "df = df.with_columns(\n", " pl.from_epoch('Local Timestamp', time_unit='ns')\n", ")\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 10, "id": "f3b43c9f", "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", " var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " var reloading = false;\n", " var Bokeh = root.Bokeh;\n", "\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", " if (js_modules == null) js_modules = [];\n", " if (js_exports == null) js_exports = {};\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", "\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " if (!reloading) {\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", " window._bokeh_on_load = on_load\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " var skip = [];\n", " if (window.requirejs) {\n", " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", " root._bokeh_is_loading = css_urls.length + 0;\n", " } else {\n", " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", " }\n", "\n", " var existing_stylesheets = []\n", " var links = document.getElementsByTagName('link')\n", " for (var i = 0; i < links.length; i++) {\n", " var link = links[i]\n", " if (link.href != null) {\n", "\texisting_stylesheets.push(link.href)\n", " }\n", " }\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " if (existing_stylesheets.indexOf(url) !== -1) {\n", "\ton_load()\n", "\tcontinue;\n", " }\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " } var existing_scripts = []\n", " var scripts = document.getElementsByTagName('script')\n", " for (var i = 0; i < scripts.length; i++) {\n", " var script = scripts[i]\n", " if (script.src != null) {\n", "\texisting_scripts.push(script.src)\n", " }\n", " }\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (var i = 0; i < js_modules.length; i++) {\n", " var url = js_modules[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (const name in js_exports) {\n", " var url = js_exports[name];\n", " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " element.textContent = `\n", " import ${name} from \"${url}\"\n", " window.${name} = ${name}\n", " window._bokeh_on_load()\n", " `\n", " document.head.appendChild(element);\n", " }\n", " if (!js_urls.length && !js_modules.length) {\n", " on_load()\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\"];\n", " var js_modules = [];\n", " var js_exports = {};\n", " var css_urls = [];\n", " var inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {} // ensure no trailing comma for IE\n", " ];\n", "\n", " function run_inline_js() {\n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", "\ttry {\n", " inline_js[i].call(root, root.Bokeh);\n", "\t} catch(e) {\n", "\t if (!reloading) {\n", "\t throw e;\n", "\t }\n", "\t}\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", "\tvar NewBokeh = root.Bokeh;\n", "\tif (Bokeh.versions === undefined) {\n", "\t Bokeh.versions = new Map();\n", "\t}\n", "\tif (NewBokeh.version !== Bokeh.version) {\n", "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", "\t}\n", "\troot.Bokeh = Bokeh;\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " }\n", " root._bokeh_is_initializing = false\n", " }\n", "\n", " function load_or_wait() {\n", " // Implement a backoff loop that tries to ensure we do not load multiple\n", " // versions of Bokeh and its dependencies at the same time.\n", " // In recent versions we use the root._bokeh_is_initializing flag\n", " // to determine whether there is an ongoing attempt to initialize\n", " // bokeh, however for backward compatibility we also try to ensure\n", " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", " // before older versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", " root._bokeh_is_initializing = false;\n", " root._bokeh_onload_callbacks = undefined;\n", " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", " load_or_wait();\n", " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", " setTimeout(load_or_wait, 100);\n", " } else {\n", " root._bokeh_is_initializing = true\n", " root._bokeh_onload_callbacks = []\n", " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", " if (!reloading && !bokeh_loaded) {\n", "\troot.Bokeh = undefined;\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", "\trun_inline_js();\n", " });\n", " }\n", " }\n", " // Give 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\n", "" ], "text/plain": [ ":NdOverlay [Variable]\n", " :Curve [Local Timestamp] (value)" ] }, "execution_count": 10, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1004" } }, "output_type": "execute_result" } ], "source": [ "import holoviews as hv\n", "\n", "hv.extension('bokeh')\n", "\n", "df.plot(x='Local Timestamp')" ] }, { "cell_type": "markdown", "id": "d5a9d4a3", "metadata": {}, "source": [ "## Display last trades between the step" ] }, { "cell_type": "code", "execution_count": 11, "id": "71dd4f1a", "metadata": {}, "outputs": [], "source": [ "from hftbacktest import BUY_EVENT\n", "\n", "@njit\n", "def print_trades(hbt):\n", " while hbt.elapse(60 * 1e9) == 0:\n", " print('-------------------------------------------------------------------------------')\n", " print('current_timestamp:', hbt.current_timestamp)\n", "\n", " # Gets the last trades occurring in the market, not the trades of our orders.\n", " last_trades = hbt.last_trades(0)\n", " \n", " num = 0\n", " for last_trade in last_trades:\n", " if num > 10:\n", " print('...')\n", " break\n", " print(\n", " 'exch_timestamp:',\n", " last_trade.exch_ts,\n", " 'buy' if (last_trade.ev & BUY_EVENT) == BUY_EVENT else 'sell',\n", " last_trade.qty,\n", " '@',\n", " last_trade.px\n", " )\n", " num += 1\n", "\n", " # To prevent accumulating all last trades, which may cause a slowdown,\n", " # clear_last_trades needs to be called.\n", " # After this, accessing `last_trades` will cause a crash.\n", " hbt.clear_last_trades(0)\n", " return True" ] }, { "cell_type": "code", "execution_count": 12, "id": "0d37656a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-------------------------------------------------------------------------------\n", "current_timestamp: 1723161661500000000\n", "exch_timestamp: 1723161602372000000 buy 0.489 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.198 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.006 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.002 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.003 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.011 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.238 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.007 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.005 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.003 @ 61659.8\n", "exch_timestamp: 1723161602372000000 buy 0.002 @ 61659.8\n", "...\n", "-------------------------------------------------------------------------------\n", "current_timestamp: 1723161721500000000\n", "exch_timestamp: 1723161661697000000 sell 0.002 @ 61594.1\n", "exch_timestamp: 1723161661724000000 sell 0.002 @ 61594.1\n", "exch_timestamp: 1723161661751000000 buy 0.135 @ 61594.2\n", "exch_timestamp: 1723161661806000000 sell 1.328 @ 61594.1\n", "exch_timestamp: 1723161661806000000 sell 0.002 @ 61594.1\n", "exch_timestamp: 1723161661806000000 sell 0.002 @ 61594.1\n", "exch_timestamp: 1723161661806000000 sell 0.002 @ 61594.1\n", "exch_timestamp: 1723161661806000000 sell 0.006 @ 61594.1\n", "exch_timestamp: 1723161661806000000 sell 0.32 @ 61594.1\n", "exch_timestamp: 1723161661806000000 sell 0.032 @ 61594.1\n", "exch_timestamp: 1723161661806000000 sell 1.208 @ 61594.1\n", "...\n", "-------------------------------------------------------------------------------\n", "current_timestamp: 1723161781500000000\n", "exch_timestamp: 1723161721541000000 sell 0.002 @ 61576.5\n", "exch_timestamp: 1723161721574000000 buy 0.012 @ 61576.6\n", "exch_timestamp: 1723161721578000000 sell 0.003 @ 61576.5\n", "exch_timestamp: 1723161721583000000 buy 0.275 @ 61576.6\n", "exch_timestamp: 1723161721583000000 buy 0.469 @ 61576.6\n", "exch_timestamp: 1723161721585000000 buy 0.095 @ 61576.6\n", "exch_timestamp: 1723161721585000000 buy 0.102 @ 61576.6\n", "exch_timestamp: 1723161721585000000 buy 0.197 @ 61576.6\n", "exch_timestamp: 1723161721586000000 buy 0.13 @ 61576.6\n", "exch_timestamp: 1723161721587000000 buy 0.425 @ 61576.6\n", "exch_timestamp: 1723161721587000000 buy 0.324 @ 61576.6\n", "...\n", "-------------------------------------------------------------------------------\n", "current_timestamp: 1723161841500000000\n", "exch_timestamp: 1723161781628000000 sell 0.026 @ 61629.6\n", "exch_timestamp: 1723161781727000000 buy 0.011 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.05 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.006 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.002 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.007 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.002 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.075 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.065 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.247 @ 61629.7\n", "exch_timestamp: 1723161781727000000 buy 0.002 @ 61629.7\n", "...\n", "-------------------------------------------------------------------------------\n", "current_timestamp: 1723161901500000000\n", "exch_timestamp: 1723161841561000000 buy 0.01 @ 61621.6\n", "exch_timestamp: 1723161841561000000 buy 0.006 @ 61621.6\n", "exch_timestamp: 1723161841561000000 buy 0.002 @ 61621.6\n", "exch_timestamp: 1723161841561000000 buy 0.022 @ 61621.6\n", "exch_timestamp: 1723161841561000000 buy 0.097 @ 61621.6\n", "exch_timestamp: 1723161841561000000 buy 0.024 @ 61621.6\n", "exch_timestamp: 1723161841564000000 buy 0.024 @ 61621.6\n", "exch_timestamp: 1723161841564000000 buy 0.014 @ 61621.6\n", "exch_timestamp: 1723161841565000000 buy 0.003 @ 61621.6\n", "exch_timestamp: 1723161841613000000 buy 0.002 @ 61622.5\n", "exch_timestamp: 1723161841613000000 buy 0.003 @ 61622.6\n", "...\n" ] } ], "source": [ "asset = (\n", " BacktestAsset()\n", " .data(btcusdt_20240809)\n", " .initial_snapshot(btcusdt_20240808_eod)\n", " .linear_asset(1.0) \n", " .constant_latency(10_000_000, 10_000_000)\n", " .risk_adverse_queue_model() \n", " .no_partial_fill_exchange()\n", " .trading_value_fee_model(0.0002, 0.0007)\n", " .tick_size(0.1)\n", " .lot_size(0.001)\n", " # To retrieve the last trades, `last_trades_capacity` should be set.\n", " .last_trades_capacity(1000)\n", " .roi_lb(30000)\n", " .roi_ub(90000)\n", ")\n", "\n", "hbt = ROIVectorMarketDepthBacktest([asset])\n", "\n", "print_trades(hbt)\n", "\n", "_ = hbt.close()" ] }, { "cell_type": "markdown", "id": "a37694e1", "metadata": {}, "source": [ "## Rolling Volume-Weighted Average Price" ] }, { "cell_type": "code", "execution_count": 13, "id": "002fa805", "metadata": {}, "outputs": [], "source": [ "@njit\n", "def rolling_vwap(hbt, out):\n", " buy_amount_bin = np.zeros(100_000, np.float64)\n", " buy_qty_bin = np.zeros(100_000, np.float64)\n", " sell_amount_bin = np.zeros(100_000, np.float64)\n", " sell_qty_bin = np.zeros(100_000, np.float64)\n", " \n", " idx = 0\n", " last_trade_price = np.nan\n", " \n", " while hbt.elapse(10 * 1e9) == 0:\n", " last_trades = hbt.last_trades(0)\n", " \n", " for last_trade in last_trades:\n", " if (last_trade.ev & BUY_EVENT) == BUY_EVENT:\n", " buy_amount_bin[idx] += last_trade.px * last_trade.qty\n", " buy_qty_bin[idx] += last_trade.qty\n", " else:\n", " sell_amount_bin[idx] += last_trade.px * last_trade.qty\n", " sell_qty_bin[idx] += last_trade.qty\n", " \n", " hbt.clear_last_trades(0)\n", " idx += 1\n", "\n", " if idx >= 1:\n", " vwap10sec = np.divide(\n", " buy_amount_bin[idx - 1] + sell_amount_bin[idx - 1], \n", " buy_qty_bin[idx - 1] + sell_qty_bin[idx - 1]\n", " )\n", " else:\n", " vwap10sec = np.nan\n", " \n", " if idx >= 6:\n", " vwap1m = np.divide(\n", " np.sum(buy_amount_bin[idx - 6:idx]) + np.sum(sell_amount_bin[idx - 6:idx]), \n", " np.sum(buy_qty_bin[idx - 6:idx]) + np.sum(sell_qty_bin[idx - 6:idx])\n", " )\n", " buy_vwap1m = np.divide(np.sum(buy_amount_bin[idx - 6:idx]), np.sum(buy_qty_bin[idx - 6:idx]))\n", " sell_vwap1m = np.divide(np.sum(sell_amount_bin[idx - 6:idx]), np.sum(sell_qty_bin[idx - 6:idx]))\n", " else:\n", " vwap1m = np.nan\n", " buy_vwap1m = np.nan\n", " sell_vwap1m = np.nan\n", " \n", " out.append((hbt.current_timestamp, vwap10sec, vwap1m, buy_vwap1m, sell_vwap1m))\n", " return True" ] }, { "cell_type": "code", "execution_count": 14, "id": "f90739a8", "metadata": {}, "outputs": [], "source": [ "hbt = ROIVectorMarketDepthBacktest([asset])\n", "\n", "tup_ty = Tuple((float64, float64, float64, float64, float64))\n", "out = List.empty_list(tup_ty, allocated=100_000)\n", "\n", "rolling_vwap(hbt, out)\n", "\n", "_ = hbt.close()" ] }, { "cell_type": "code", "execution_count": 15, "id": "f2ab6bd3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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