{
"cells": [
{
"cell_type": "markdown",
"id": "e25713dc",
"metadata": {},
"source": [
"# 注文遅延の影響\n",
"\n",
"この例では、戦略のパフォーマンスに対する注文遅延の影響を示します。\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5a2db8f6",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from numba import njit, uint64\n",
"from numba.typed import Dict\n",
"\n",
"from hftbacktest import (\n",
" BacktestAsset,\n",
" ROIVectorMarketDepthBacktest,\n",
" GTX,\n",
" LIMIT,\n",
" BUY,\n",
" SELL,\n",
" BUY_EVENT,\n",
" Recorder\n",
")\n",
"from hftbacktest.stats import LinearAssetRecord\n",
"\n",
"\n",
"@njit\n",
"def measure_trading_intensity(order_arrival_depth, out):\n",
" max_tick = 0\n",
" for depth in order_arrival_depth:\n",
" if not np.isfinite(depth):\n",
" continue\n",
" \n",
" # Sets the tick index to 0 for the nearest possible best price \n",
" # as the order arrival depth in ticks is measured from the mid-price\n",
" tick = round(depth / .5) - 1\n",
" \n",
" # In a fast-moving market, buy trades can occur below the mid-price (and vice versa for sell trades) \n",
" # since the mid-price is measured in a previous time-step; \n",
" # however, to simplify the problem, we will exclude those cases.\n",
" if tick < 0 or tick >= len(out):\n",
" continue\n",
" \n",
" # All of our possible quotes within the order arrival depth, \n",
" # excluding those at the same price, are considered executed.\n",
" out[:tick] += 1\n",
" \n",
" max_tick = max(max_tick, tick)\n",
" return out[:max_tick]\n",
"\n",
"@njit\n",
"def linear_regression(x, y):\n",
" sx = np.sum(x)\n",
" sy = np.sum(y)\n",
" sx2 = np.sum(x ** 2)\n",
" sxy = np.sum(x * y)\n",
" w = len(x)\n",
" slope = (w * sxy - sx * sy) / (w * sx2 - sx**2)\n",
" intercept = (sy - slope * sx) / w\n",
" return slope, intercept\n",
"\n",
"@njit\n",
"def compute_coeff(xi, gamma, delta, A, k):\n",
" inv_k = np.divide(1, k)\n",
" c1 = 1 / (xi * delta) * np.log(1 + xi * delta * inv_k)\n",
" c2 = np.sqrt(np.divide(gamma, 2 * A * delta * k) * ((1 + xi * delta * inv_k) ** (k / (xi * delta) + 1)))\n",
" return c1, c2\n",
"\n",
"@njit\n",
"def gridtrading_glft_mm(hbt, order_qty, recorder):\n",
" asset_no = 0\n",
" tick_size = hbt.depth(asset_no).tick_size\n",
" \n",
" arrival_depth = np.full(10_000_000, np.nan, np.float64)\n",
" mid_price_chg = np.full(10_000_000, np.nan, np.float64)\n",
"\n",
" t = 0\n",
" prev_mid_price_tick = np.nan\n",
" mid_price_tick = np.nan\n",
"\n",
" tmp = np.zeros(500, np.float64)\n",
" ticks = np.arange(len(tmp)) + 0.5\n",
" \n",
" A = np.nan\n",
" k = np.nan\n",
" volatility = np.nan\n",
" gamma = 0.05\n",
" delta = 1\n",
" adj1 = 1\n",
" \n",
" # adj2 is determined according to the order quantity.\n",
" grid_num = 20\n",
" max_position = grid_num * order_qty\n",
" adj2 = 1 / max_position\n",
" \n",
" # Checks every 100 milliseconds.\n",
" while hbt.elapse(100_000_000) == 0:\n",
" #--------------------------------------------------------\n",
" # Records market order's arrival depth from the mid-price.\n",
" if not np.isnan(mid_price_tick):\n",
" depth = -np.inf\n",
" for last_trade in hbt.last_trades(asset_no):\n",
" trade_price_tick = last_trade.px / tick_size\n",
" \n",
" if last_trade.ev & BUY_EVENT == BUY_EVENT:\n",
" depth = max(trade_price_tick - mid_price_tick, depth)\n",
" else:\n",
" depth = max(mid_price_tick - trade_price_tick, depth)\n",
" arrival_depth[t] = depth\n",
" \n",
" hbt.clear_last_trades(asset_no)\n",
" hbt.clear_inactive_orders(asset_no)\n",
" \n",
" depth = hbt.depth(asset_no)\n",
" position = hbt.position(asset_no)\n",
" orders = hbt.orders(asset_no)\n",
"\n",
" best_bid_tick = depth.best_bid_tick\n",
" best_ask_tick = depth.best_ask_tick\n",
" \n",
" prev_mid_price_tick = mid_price_tick\n",
" mid_price_tick = (best_bid_tick + best_ask_tick) / 2.0\n",
" \n",
" # Records the mid-price change for volatility calculation.\n",
" mid_price_chg[t] = mid_price_tick - prev_mid_price_tick\n",
" \n",
" #--------------------------------------------------------\n",
" # Calibrates A, k and calculates the market volatility.\n",
" \n",
" # Updates A, k, and the volatility every 5-sec.\n",
" if t % 50 == 0:\n",
" # Window size is 10-minute.\n",
" if t >= 6_000 - 1:\n",
" # Calibrates A, k\n",
" tmp[:] = 0\n",
" lambda_ = measure_trading_intensity(arrival_depth[t + 1 - 6_000:t + 1], tmp)\n",
" if len(lambda_) > 2:\n",
" lambda_ = lambda_[:70] / 600\n",
" x = ticks[:len(lambda_)]\n",
" y = np.log(lambda_)\n",
" k_, logA = linear_regression(x, y)\n",
" A = np.exp(logA)\n",
" k = -k_\n",
" \n",
" # Updates the volatility.\n",
" volatility = np.nanstd(mid_price_chg[t + 1 - 6_000:t + 1]) * np.sqrt(10)\n",
"\n",
" #--------------------------------------------------------\n",
" # Computes bid price and ask price.\n",
" \n",
" c1, c2 = compute_coeff(gamma, gamma, delta, A, k)\n",
" \n",
" half_spread_tick = (c1 + delta / 2 * c2 * volatility) * adj1\n",
" skew = c2 * volatility * adj2\n",
" \n",
" reservation_price_tick = mid_price_tick - skew * position\n",
"\n",
" bid_price_tick = min(np.round(reservation_price_tick - half_spread_tick), best_bid_tick)\n",
" ask_price_tick = max(np.round(reservation_price_tick + half_spread_tick), best_ask_tick)\n",
" \n",
" bid_price = bid_price_tick * tick_size\n",
" ask_price = ask_price_tick * tick_size\n",
"\n",
" grid_interval = max(np.round(half_spread_tick) * tick_size, tick_size)\n",
"\n",
" bid_price = np.floor(bid_price / grid_interval) * grid_interval\n",
" ask_price = np.ceil(ask_price / grid_interval) * grid_interval\n",
" \n",
" #--------------------------------------------------------\n",
" # Updates quotes.\n",
" \n",
" # Creates a new grid for buy orders.\n",
" new_bid_orders = Dict.empty(np.uint64, np.float64)\n",
" if position < max_position and np.isfinite(bid_price):\n",
" for i in range(grid_num):\n",
" bid_price_tick = round(bid_price / tick_size)\n",
" \n",
" # order price in tick is used as order id.\n",
" new_bid_orders[uint64(bid_price_tick)] = bid_price\n",
" \n",
" bid_price -= grid_interval\n",
"\n",
" # Creates a new grid for sell orders.\n",
" new_ask_orders = Dict.empty(np.uint64, np.float64)\n",
" if position > -max_position and np.isfinite(ask_price):\n",
" for i in range(grid_num):\n",
" ask_price_tick = round(ask_price / tick_size)\n",
" \n",
" # order price in tick is used as order id.\n",
" new_ask_orders[uint64(ask_price_tick)] = ask_price\n",
"\n",
" ask_price += grid_interval\n",
" \n",
" order_values = orders.values();\n",
" while order_values.has_next():\n",
" order = order_values.get()\n",
" # Cancels if a working order is not in the new grid.\n",
" if order.cancellable:\n",
" if (\n",
" (order.side == BUY and order.order_id not in new_bid_orders)\n",
" or (order.side == SELL and order.order_id not in new_ask_orders)\n",
" ):\n",
" hbt.cancel(asset_no, order.order_id, False)\n",
" \n",
" for order_id, order_price in new_bid_orders.items():\n",
" # Posts a new buy order if there is no working order at the price on the new grid.\n",
" if order_id not in orders:\n",
" hbt.submit_buy_order(asset_no, order_id, order_price, order_qty, GTX, LIMIT, False)\n",
" \n",
" for order_id, order_price in new_ask_orders.items():\n",
" # Posts a new sell order if there is no working order at the price on the new grid.\n",
" if order_id not in orders:\n",
" hbt.submit_sell_order(asset_no, order_id, order_price, order_qty, GTX, LIMIT, False)\n",
" \n",
" #--------------------------------------------------------\n",
" # Records variables and stats for analysis.\n",
" \n",
" t += 1\n",
" \n",
" if t >= len(arrival_depth) or t >= len(mid_price_chg):\n",
" raise Exception\n",
" \n",
" # Records the current state for stat calculation.\n",
" recorder.record(hbt)"
]
},
{
"cell_type": "markdown",
"id": "66942376",
"metadata": {},
"source": [
"## フィードレイテンシからの注文遅延\n",
"\n",
"[チュートリアル](https://hftbacktest.readthedocs.io/en/latest/tutorials/Order%20Latency%20Data.html) を参照して、フィードレイテンシから人工的な注文遅延データを生成する方法を確認してください。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c2fced5b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
shape: (1, 11)| start | end | SR | Sortino | Return | MaxDrawdown | DailyNumberOfTrades | DailyTurnover | ReturnOverMDD | ReturnOverTrade | MaxPositionValue |
|---|
| datetime[μs] | datetime[μs] | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 2023-04-01 00:00:00 | 2023-04-05 23:59:50 | -0.197608 | -0.224204 | -0.001021 | 0.060794 | 4459.903239 | 328.415763 | -0.016794 | -6.2176e-7 | 75431.07 |
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats.plot()"
]
},
{
"cell_type": "markdown",
"id": "c1b28eb7",
"metadata": {},
"source": [
"## ライブ注文遅延"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7e590c9f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
shape: (1, 11)| start | end | SR | Sortino | Return | MaxDrawdown | DailyNumberOfTrades | DailyTurnover | ReturnOverMDD | ReturnOverTrade | MaxPositionValue |
|---|
| datetime[μs] | datetime[μs] | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 2023-04-01 00:00:00 | 2023-04-05 23:59:50 | 1.536293 | 1.741565 | 0.007814 | 0.051916 | 4563.105627 | 336.150295 | 0.150518 | 0.000005 | 67694.55 |
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats.plot()"
]
},
{
"cell_type": "markdown",
"id": "173d190c",
"metadata": {},
"source": [
"## 増幅されたフィードレイテンシからの注文遅延\n",
"\n",
"注文エントリの遅延はフィードレイテンシの4倍、注文応答の遅延はフィードレイテンシの3倍です。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b7b50743",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
shape: (1, 11)| start | end | SR | Sortino | Return | MaxDrawdown | DailyNumberOfTrades | DailyTurnover | ReturnOverMDD | ReturnOverTrade | MaxPositionValue |
|---|
| datetime[μs] | datetime[μs] | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 2023-04-01 00:00:00 | 2023-04-05 23:59:50 | -0.376802 | -0.430111 | -0.002163 | 0.053785 | 4366.301072 | 321.501683 | -0.040224 | -0.000001 | 75711.93 |