15097 lines
770 KiB
Plaintext
15097 lines
770 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab64a426-f870-4c7a-8814-14b1f9fc68c6",
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"metadata": {},
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"source": [
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"# Анализ ордербука"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6e6d0daa-c0ad-4e19-8d0d-01e15efe1fa1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Декомпозиция класса"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"id": "74db1419-af56-4ef8-9dc1-464cd80321b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Dict, Any, List\n",
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"\n",
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"class OrderBookOutOfSync(Exception):\n",
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" pass\n",
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"\n",
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"class OrderBook:\n",
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" def __init__(self):\n",
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" # books: symbol -> {'timestamp': float, 'bids': {price_str: volume}, 'asks': {price_str: volume}}\n",
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" self.books: Dict[str, Dict[str, Any]] = {}\n",
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" self.last_update: Dict[str, int] = {}\n",
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"\n",
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" def process_message(self, message: Dict[str, Any]) -> None:\n",
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" data = message['data']\n",
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" symbol = data['s']\n",
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" ts = message.get('ts', 0.0)\n",
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"\n",
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" if message['type'] == 'snapshot':\n",
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" self.books[symbol] = {\n",
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" 'timestamp': ts,\n",
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" 'bids': {str(float(p[0])): float(p[1]) for p in data['b']},\n",
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" 'asks': {str(float(p[0])): float(p[1]) for p in data['a']},\n",
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" }\n",
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" self.last_update[symbol] = int(data.get('u', 0))\n",
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" elif message['type'] == 'delta':\n",
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" update_id = int(data['u'])\n",
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" prev_update = self.last_update.get(symbol)\n",
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" if prev_update is None:\n",
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" return\n",
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" if update_id != prev_update + 1:\n",
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" raise OrderBookOutOfSync(\n",
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" f\"Update out of sequence for {symbol}: expected {prev_update + 1}, got {update_id}\"\n",
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" )\n",
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"\n",
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" if symbol not in self.books:\n",
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" return\n",
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"\n",
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" def update_side(side_name: str, updates: List[List[Any]]):\n",
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" for entry in updates:\n",
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" try:\n",
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" price_f = float(entry[0])\n",
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" volume_f = float(entry[1])\n",
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" except (ValueError, TypeError, IndexError):\n",
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" continue\n",
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" price_key = str(price_f)\n",
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" if volume_f > 0:\n",
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" self.books[symbol][side_name][price_key] = volume_f\n",
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" elif price_key in self.books[symbol][side_name]:\n",
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" del self.books[symbol][side_name][price_key]\n",
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"\n",
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" update_side(\"bids\", data.get(\"b\", []))\n",
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" update_side(\"asks\", data.get(\"a\", []))\n",
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"\n",
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" self.last_update[symbol] = update_id\n",
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" self.books[symbol][\"timestamp\"] = ts\n",
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"\n",
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" def _get_basic_info(self, symbol: str) -> Dict[str, Any]:\n",
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" \"\"\"Получить поля symbol, timestamp, lowest_ask, highest_bid для symbol.\"\"\"\n",
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" if symbol not in self.books:\n",
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" return {\"symbol\": symbol, \"timestamp\": None, \"lowest_ask\": None, \"highest_bid\": None}\n",
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" bids = self.books[symbol][\"bids\"]\n",
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" asks = self.books[symbol][\"asks\"]\n",
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" lowest_ask = min(map(float, asks.keys())) if asks else None\n",
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" highest_bid = max(map(float, bids.keys())) if bids else None\n",
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" timestamp = self.books[symbol].get(\"timestamp\")\n",
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" return {\n",
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" \"symbol\": symbol,\n",
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" \"timestamp\": timestamp,\n",
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" \"lowest_ask\": lowest_ask,\n",
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" \"highest_bid\": highest_bid,\n",
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" }\n",
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"\n",
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" def get_order_book(self, symbol: str) -> Dict[str, Any]:\n",
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" base_info = self._get_basic_info(symbol)\n",
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" if symbol not in self.books:\n",
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" return {**base_info, \"bids\": {}, \"asks\": {}}\n",
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" bids = self.books[symbol][\"bids\"]\n",
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" asks = self.books[symbol][\"asks\"]\n",
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" sorted_bids = dict(sorted(bids.items(), key=lambda x: float(x[0]), reverse=True))\n",
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" sorted_asks = dict(sorted(asks.items(), key=lambda x: float(x[0])))\n",
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" return {**base_info, \"bids\": sorted_bids, \"asks\": sorted_asks}\n",
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"\n",
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" def get_aggregated_levels(self, symbol: str, levels: List[float]) -> Dict[str, Any]:\n",
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" base_info = self._get_basic_info(symbol)\n",
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" if symbol not in self.books:\n",
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" return {**base_info, \"bids\": [], \"asks\": []}\n",
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" bids = self.books[symbol][\"bids\"]\n",
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" asks = self.books[symbol][\"asks\"]\n",
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" if not bids or not asks:\n",
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" return {**base_info, \"bids\": [], \"asks\": []}\n",
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"\n",
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" highest_bid = base_info[\"highest_bid\"]\n",
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" lowest_ask = base_info[\"lowest_ask\"]\n",
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"\n",
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" sorted_bids = sorted(((float(price), volume) for price, volume in bids.items()), reverse=True)\n",
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" sorted_asks = sorted(((float(price), volume) for price, volume in asks.items()))\n",
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"\n",
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" def aggregate_levels(\n",
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" sorted_prices_volumes: List, base_price: float, percents: List[float], is_bid: bool\n",
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" ) -> List[Dict[str, Any]]:\n",
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" agg_results = []\n",
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" for pct in percents:\n",
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" pct = float(pct)\n",
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" if is_bid:\n",
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" lower_bound = base_price * (1 - pct / 100)\n",
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" volume_sum = sum(\n",
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" volume\n",
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" for price, volume in sorted_prices_volumes\n",
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" if base_price > price > lower_bound\n",
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" )\n",
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" else:\n",
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" upper_bound = base_price * (1 + pct / 100)\n",
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" volume_sum = sum(\n",
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" volume\n",
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" for price, volume in sorted_prices_volumes\n",
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" if base_price < price < upper_bound\n",
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" )\n",
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" agg_results.append({\"level\": pct, \"volume\": volume_sum})\n",
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" return agg_results\n",
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"\n",
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" aggregated_bids = aggregate_levels(sorted_bids, highest_bid, levels, True)\n",
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" aggregated_asks = aggregate_levels(sorted_asks, lowest_ask, levels, False)\n",
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"\n",
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" return {**base_info, \"bids\": aggregated_bids, \"asks\": aggregated_asks}\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 139,
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"id": "50d63e2e-1ed8-499e-b710-85976713dd4e",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
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"from typing import Dict, Any, List, Optional\n",
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"import time\n",
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"\n",
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"class OrderBookOutOfSync(Exception):\n",
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" pass\n",
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"\n",
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"class VolumeDeltaOrderBook:\n",
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" def __init__(self, max_depth_sec: int = 900):\n",
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" self.max_depth_sec = max_depth_sec\n",
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" # Сохраняем историю объёмов: symbol -> side -> list записей {timestamp, in, out}\n",
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" self.volume_history: Dict[str, Dict[str, List[Dict[str, float]]]] = {}\n",
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" # Запоминаем последнее время обновления для каждого symbol\n",
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" self.last_update_time: Dict[str, float] = {}\n",
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" # Для хранения текущего объёма по каждому symbol и side (по цене)\n",
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" self.current_volumes: Dict[str, Dict[str, Dict[str, float]]] = {}\n",
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" # Последние id обновления message для контроля последовательности\n",
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" self._last_update_id: Dict[str, int] = {}\n",
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"\n",
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" def process_message(self, message: Dict[str, Any]) -> None:\n",
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" data = message['data']\n",
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" symbol = data['s']\n",
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" # Используем время получения сообщения как timestamp для истории\n",
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" now_ts = time.time()\n",
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"\n",
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" if message['type'] == 'snapshot':\n",
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" self.volume_history[symbol] = {'bids': [], 'asks': []}\n",
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" self.last_update_time[symbol] = now_ts\n",
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" self.current_volumes[symbol] = {'bids': {}, 'asks': {}}\n",
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" self._last_update_id[symbol] = int(data.get('u', 0))\n",
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"\n",
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" # Инициализируем текущие объемы из snapshot\n",
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" for side_name in ['bids', 'asks']:\n",
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" side_updates = data.get(side_name[0], data.get(side_name, [])) # data['b'] or data['a'] обычно\n",
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" for price_str, volume in side_updates:\n",
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" price = str(float(price_str))\n",
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" self.current_volumes[symbol][side_name][price] = float(volume)\n",
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"\n",
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" elif message['type'] == 'delta':\n",
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" update_id = int(data['u'])\n",
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" prev_update = self._last_update_id.get(symbol)\n",
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" if prev_update is None:\n",
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" # Без snapshot не обрабатывать delta\n",
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" return\n",
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" if update_id != prev_update + 1:\n",
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" raise OrderBookOutOfSync(f\"Update out of sequence for {symbol}: expected {prev_update+1}, got {update_id}\")\n",
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"\n",
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" if symbol not in self.volume_history:\n",
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" self.volume_history[symbol] = {'bids': [], 'asks': []}\n",
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" if symbol not in self.current_volumes:\n",
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" self.current_volumes[symbol] = {'bids': {}, 'asks': {}}\n",
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" if symbol not in self.last_update_time or now_ts > self.last_update_time[symbol]:\n",
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" self.last_update_time[symbol] = now_ts\n",
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"\n",
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" def update_side(side_name: str, updates: List[List[float]]):\n",
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" volume_in = 0.0\n",
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" volume_out = 0.0\n",
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" side_current = self.current_volumes[symbol][side_name]\n",
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"\n",
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" for entry in updates:\n",
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" try:\n",
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" price_f = float(entry[0])\n",
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" volume_f = float(entry[1])\n",
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||
" except (ValueError, TypeError, IndexError):\n",
|
||
" continue\n",
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"\n",
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" price_key = str(price_f)\n",
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" prev_volume = side_current.get(price_key, 0.0)\n",
|
||
" delta_volume = volume_f - prev_volume\n",
|
||
" delta_value = price_f * delta_volume\n",
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"\n",
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" if delta_volume > 0:\n",
|
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" volume_in += delta_value\n",
|
||
" elif delta_volume < 0:\n",
|
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" volume_out += abs(delta_value)\n",
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"\n",
|
||
" if volume_f > 0:\n",
|
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" side_current[price_key] = volume_f\n",
|
||
" elif price_key in side_current:\n",
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" del side_current[price_key]\n",
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"\n",
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" # Запись с теперь актуальным timestamp\n",
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" self.volume_history[symbol][side_name].append({\n",
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" 'timestamp': now_ts,\n",
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" 'in': volume_in,\n",
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" 'out': volume_out\n",
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" })\n",
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"\n",
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" cutoff = now_ts - self.max_depth_sec\n",
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" self.volume_history[symbol][side_name] = [\n",
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" rec for rec in self.volume_history[symbol][side_name]\n",
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" if rec['timestamp'] >= cutoff\n",
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" ]\n",
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"\n",
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" update_side('bids', data.get('b', []))\n",
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" update_side('asks', data.get('a', []))\n",
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"\n",
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" self._last_update_id[symbol] = update_id\n",
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"\n",
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" def get_volume_delta(self, symbol: str, depth_sec: int) -> Optional[Dict[str, Any]]:\n",
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" if symbol not in self.volume_history:\n",
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" return None\n",
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" depth_sec = min(depth_sec, self.max_depth_sec)\n",
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" now_ts = self.last_update_time.get(symbol, time.time())\n",
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" cutoff = now_ts - depth_sec\n",
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"\n",
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" def sum_volume(records: List[Dict[str, float]]):\n",
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" volume_in = sum(r['in'] for r in records if r['timestamp'] >= cutoff)\n",
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" volume_out = sum(r['out'] for r in records if r['timestamp'] >= cutoff)\n",
|
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" return {'in': volume_in, 'out': volume_out}\n",
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"\n",
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" bids = sum_volume(self.volume_history[symbol]['bids'])\n",
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" asks = sum_volume(self.volume_history[symbol]['asks'])\n",
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"\n",
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" return {\n",
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" 'symbol': symbol,\n",
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" 'timestamp': now_ts,\n",
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" 'depth': depth_sec,\n",
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" 'bids': bids,\n",
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" 'asks': asks,\n",
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" }\n"
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]
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},
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{
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"cell_type": "code",
|
||
"execution_count": 140,
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||
"id": "66a16f4a-9c2a-499b-880b-5662ebe8b07f",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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||
"text/plain": [
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||
"{'symbol': 'BTCUSDT',\n",
|
||
" 'timestamp': 1756684801829,\n",
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||
" 'lowest_ask': 108241.8,\n",
|
||
" 'highest_bid': 108241.7,\n",
|
||
" 'bids': [{'level': 0.005, 'volume': 0.024432999999999996},\n",
|
||
" {'level': 0.01, 'volume': 0.720067},\n",
|
||
" {'level': 0.015, 'volume': 2.188207},\n",
|
||
" {'level': 0.02, 'volume': 3.6439869999999983}],\n",
|
||
" 'asks': [{'level': 0.005, 'volume': 0.12923800000000002},\n",
|
||
" {'level': 0.01, 'volume': 1.229285},\n",
|
||
" {'level': 0.015, 'volume': 2.2872159999999995},\n",
|
||
" {'level': 0.02, 'volume': 3.177030999999999}]}"
|
||
]
|
||
},
|
||
"execution_count": 140,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"levels[3]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 176,
|
||
"id": "08e0b593-47aa-4d59-b14a-8733d37d9027",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import json\n",
|
||
"\n",
|
||
"def process_json_lines(file_path, handler):\n",
|
||
" with open(file_path, 'r', encoding='utf-8') as f:\n",
|
||
" for line in f:\n",
|
||
" line = line.strip()\n",
|
||
" if not line:\n",
|
||
" continue # пропуск пустых строк\n",
|
||
" try:\n",
|
||
" obj = json.loads(line)\n",
|
||
" except json.JSONDecodeError as e:\n",
|
||
" print(f\"Ошибка декодирования JSON: {e}, строка: {line}\")\n",
|
||
" continue\n",
|
||
" handler(obj)\n",
|
||
"\n",
|
||
"\n",
|
||
"ob = OrderBook()\n",
|
||
"volob = VolumeDeltaOrderBook()\n",
|
||
"\n",
|
||
"orderbooks = []\n",
|
||
"levels = []\n",
|
||
"volumes = []\n",
|
||
"# пример обработчика\n",
|
||
"def my_handler(json_obj):\n",
|
||
" #print(json_obj) # здесь можно делать что угодно с объектом\n",
|
||
" ob.process_message(json_obj)\n",
|
||
" orderbooks.append(ob.get_order_book(\"BTCUSDT\"))\n",
|
||
" levels.append(ob.get_aggregated_levels(\"BTCUSDT\", [0.005,0.01, 0.015, 0.02]))\n",
|
||
" volob.process_message(json_obj)\n",
|
||
" volumes.append(volob.get_volume_delta(\"BTCUSDT\", 60))\n",
|
||
"\n",
|
||
"\n",
|
||
"# пример вызова\n",
|
||
"#process_json_lines('./data/2025-09-01_BTCUSDT_ob200.data', my_handler)\n",
|
||
"process_json_lines('./data/testob2.csv', my_handler)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 142,
|
||
"id": "d7c702ff-03e1-4f40-b53b-ec04a6a822f7",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[{'symbol': 'BTCUSDT',\n",
|
||
" 'timestamp': 1760385335.823819,\n",
|
||
" 'depth': 10,\n",
|
||
" 'bids': {'in': 0, 'out': 0},\n",
|
||
" 'asks': {'in': 0, 'out': 0}},\n",
|
||
" {'symbol': 'BTCUSDT',\n",
|
||
" 'timestamp': 1760385335.824225,\n",
|
||
" 'depth': 10,\n",
|
||
" 'bids': {'in': 267825.1921611, 'out': 144300.90123040005},\n",
|
||
" 'asks': {'in': 162848.7712041, 'out': 193219.8091764}},\n",
|
||
" {'symbol': 'BTCUSDT',\n",
|
||
" 'timestamp': 1760385335.824451,\n",
|
||
" 'depth': 10,\n",
|
||
" 'bids': {'in': 434006.80804489995, 'out': 353783.707776},\n",
|
||
" 'asks': {'in': 403914.7605707, 'out': 277329.900226}},\n",
|
||
" {'symbol': 'BTCUSDT',\n",
|
||
" 'timestamp': 1760385335.824672,\n",
|
||
" 'depth': 10,\n",
|
||
" 'bids': {'in': 707685.6404085999, 'out': 1136577.1175236},\n",
|
||
" 'asks': {'in': 657794.1375483, 'out': 613306.2864753001}},\n",
|
||
" {'symbol': 'BTCUSDT',\n",
|
||
" 'timestamp': 1760385335.824901,\n",
|
||
" 'depth': 10,\n",
|
||
" 'bids': {'in': 1087650.2383775997, 'out': 1401142.0233726},\n",
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||
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]
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},
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||
"execution_count": 142,
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||
"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"volumes"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 177,
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||
"id": "a5c1507c-f012-4600-b3cf-e1da9bc4be14",
|
||
"metadata": {},
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||
"outputs": [],
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"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd"
|
||
]
|
||
},
|
||
{
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||
"cell_type": "code",
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||
"execution_count": 191,
|
||
"id": "f0d9f779-c974-44c4-9f0b-72f42fa4ddd8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"prices = np.array([volume[\"lowest_ask\"] for volume in orderbooks])\n",
|
||
"\n",
|
||
"bids_in = np.array([volume[\"bids\"][\"in\"] for volume in volumes])\n",
|
||
"bids_out = np.array([volume[\"bids\"][\"out\"] for volume in volumes])\n",
|
||
"asks_in = np.array([volume[\"asks\"][\"in\"] for volume in volumes])\n",
|
||
"asks_out = np.array([volume[\"asks\"][\"out\"] for volume in volumes])\n",
|
||
"\n",
|
||
"\n",
|
||
"bids_balance = bids_in - bids_out\n",
|
||
"asks_balance = asks_in - asks_out\n",
|
||
"balance = bids_in - asks_out\n",
|
||
"\n",
|
||
"x = np.array([vol[\"timestamp\"] for vol in volumes])\n",
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||
"\n",
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||
"data=np.concatenate([prices.reshape(-1,1), bids_in.reshape(-1,1), bids_out.reshape(-1,1), asks_in.reshape(-1,1), asks_out.reshape(-1,1)], axis=1)\n",
|
||
"df = pd.DataFrame(data=data,index=x, columns= [\"prices\", \"bids_in\", \"bids_out\", \"asks_in\", \"asks_out\"] )\n"
|
||
]
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||
},
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{
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"cell_type": "code",
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"id": "ce0f8c96-1d15-48d9-9b13-f0f88a3a48a9",
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{
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",
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{\"responsive\": true} ).then(function(){\n",
|
||
" \n",
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||
"var gd = document.getElementById('5b1f0102-7e36-4480-bd1d-e31438890b7a');\n",
|
||
"var x = new MutationObserver(function (mutations, observer) {{\n",
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||
" var display = window.getComputedStyle(gd).display;\n",
|
||
" if (!display || display === 'none') {{\n",
|
||
" console.log([gd, 'removed!']);\n",
|
||
" Plotly.purge(gd);\n",
|
||
" observer.disconnect();\n",
|
||
" }}\n",
|
||
"}});\n",
|
||
"\n",
|
||
"// Listen for the removal of the full notebook cells\n",
|
||
"var notebookContainer = gd.closest('#notebook-container');\n",
|
||
"if (notebookContainer) {{\n",
|
||
" x.observe(notebookContainer, {childList: true});\n",
|
||
"}}\n",
|
||
"\n",
|
||
"// Listen for the clearing of the current output cell\n",
|
||
"var outputEl = gd.closest('.output');\n",
|
||
"if (outputEl) {{\n",
|
||
" x.observe(outputEl, {childList: true});\n",
|
||
"}}\n",
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||
"\n",
|
||
" }) }; }); </script> </div>"
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||
]
|
||
},
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||
"metadata": {},
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||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import plotly.graph_objects as go\n",
|
||
"from plotly.subplots import make_subplots\n",
|
||
"\n",
|
||
"\n",
|
||
"start_idx = 12200\n",
|
||
"end_idx = start_idx+500\n",
|
||
"\n",
|
||
"# Создаем субграфики с двойной вертикальной осью\n",
|
||
"fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
|
||
"fig.add_trace(go.Scatter(x=x[start_idx:end_idx], y=prices[start_idx:end_idx], mode='lines+markers', name=\"Line\"),secondary_y=False,)\n",
|
||
"fig.add_trace(go.Bar(x=x[start_idx:end_idx], y=balance[start_idx:end_idx], name=\"Bar\", marker_color='red',), secondary_y=True,)\n",
|
||
"fig.show()\n"
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||
]
|
||
},
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||
{
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"cell_type": "code",
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||
"execution_count": null,
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"id": "ecf34523-9e9a-4f3f-a0c8-308cedefefff",
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||
"metadata": {},
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"outputs": [],
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||
"source": []
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||
},
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{
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||
"cell_type": "code",
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"execution_count": null,
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"id": "15459f27-bff5-403a-b0c9-22018d7acd51",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
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"id": "7ed61f2e-47ca-4a22-b95b-077d3d678cea",
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|
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|
||
" '108363.4': 0.545042}}]"
|
||
]
|
||
},
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
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|
||
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|
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|
||
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|
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|
||
"id": "b9a123ba-abbf-4263-8f5a-c66ffce69326",
|
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"metadata": {},
|
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|
||
{
|
||
"data": {
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||
"text/plain": [
|
||
"[{'symbol': 'BTCUSDT',\n",
|
||
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||
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||
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||
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