\n",
" \n",
"**Note:** This example is for educational purposes only and demonstrates effective strategies for high-frequency market-making schemes. All backtests are based on a 0.005% rebate, the highest market maker rebate available on Binance Futures. See Binance Upgrades USDⓢ-Margined Futures Liquidity Provider Program for more details.\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "b6dc63c7",
"metadata": {},
"source": [
"## Plain High-Frequency Grid Trading\n",
"\n",
"This is a high-frequency version of Grid Trading that keeps posting orders on grids centered around the mid-price, maintaining a fixed interval and a set number of grids."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "641721c6",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from numba import njit, uint64, float64\n",
"from numba.typed import Dict\n",
"\n",
"from hftbacktest import BUY, SELL, GTX, LIMIT\n",
"\n",
"@njit\n",
"def gridtrading(hbt, recorder):\n",
" asset_no = 0\n",
" tick_size = hbt.depth(asset_no).tick_size\n",
" grid_num = 20\n",
" max_position = 5\n",
" grid_interval = tick_size * 10\n",
" half_spread = tick_size * 20\n",
"\n",
" # Running interval in nanoseconds.\n",
" while hbt.elapse(100_000_000) == 0:\n",
" # Clears cancelled, filled or expired orders. \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 = depth.best_bid\n",
" best_ask = depth.best_ask\n",
" \n",
" mid_price = (best_bid + best_ask) / 2.0\n",
"\n",
" order_qty = 0.1 # np.round(notional_order_qty / mid_price / hbt.depth(asset_no).lot_size) * hbt.depth(asset_no).lot_size\n",
" \n",
" # Aligns the prices to the grid.\n",
" bid_price = np.floor((mid_price - half_spread) / grid_interval) * grid_interval\n",
" ask_price = np.ceil((mid_price + half_spread) / 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): # position * mid_price < max_notional_position\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): # position * mid_price > -max_notional_position\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",
" # Records the current state for stat calculation.\n",
" recorder.record(hbt)\n",
" return True"
]
},
{
"cell_type": "markdown",
"id": "86a0d5a1-e5c9-484a-b270-d5473f9122ca",
"metadata": {},
"source": [
"For generating order latency from the feed data file, which uses feed latency as order latency, please see [Order Latency Data](https://hftbacktest.readthedocs.io/en/latest/tutorials/Order%20Latency%20Data.html)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9ecfecc8",
"metadata": {},
"outputs": [],
"source": [
"from hftbacktest import BacktestAsset, ROIVectorMarketDepthBacktest, Recorder\n",
"\n",
"asset = (\n",
" BacktestAsset()\n",
" .data([\n",
" 'data/ethusdt_20221003.npz',\n",
" 'data/ethusdt_20221004.npz',\n",
" 'data/ethusdt_20221005.npz',\n",
" 'data/ethusdt_20221006.npz',\n",
" 'data/ethusdt_20221007.npz'\n",
" ])\n",
" .initial_snapshot('data/ethusdt_20221002_eod.npz')\n",
" .linear_asset(1.0) \n",
" .intp_order_latency([\n",
" 'latency/feed_latency_20221003.npz',\n",
" 'latency/feed_latency_20221004.npz',\n",
" 'latency/feed_latency_20221005.npz',\n",
" 'latency/feed_latency_20221006.npz',\n",
" 'latency/feed_latency_20221007.npz'\n",
" ])\n",
" .power_prob_queue_model(2.0) \n",
" .no_partial_fill_exchange()\n",
" .trading_value_fee_model(-0.00005, 0.0007)\n",
" .tick_size(0.01)\n",
" .lot_size(0.001)\n",
" .roi_lb(0.0) \n",
" .roi_ub(3000.0)\n",
")\n",
"hbt = ROIVectorMarketDepthBacktest([asset])\n",
"\n",
"recorder = Recorder(1, 5_000_000)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7ca98776",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6min 5s, sys: 9.08 s, total: 6min 15s\n",
"Wall time: 6min 16s\n"
]
}
],
"source": [
"%%time\n",
"gridtrading(hbt, recorder.recorder)\n",
"\n",
"_ = hbt.close()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b9b5797c-27ea-4ff8-a3c6-5b5af1e292d1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats.plot()"
]
},
{
"cell_type": "markdown",
"id": "4cbd9b92",
"metadata": {},
"source": [
"## High-Frequency Grid Trading with Skewing\n",
"\n",
"By incorporating position-based skewing, the strategy's risk-adjusted returns can be improved."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e643b0df",
"metadata": {},
"outputs": [],
"source": [
"@njit\n",
"def gridtrading(hbt, recorder, skew):\n",
" asset_no = 0\n",
" tick_size = hbt.depth(asset_no).tick_size\n",
" grid_num = 20\n",
" max_position = 5\n",
" grid_interval = tick_size * 10\n",
" half_spread = tick_size * 20\n",
"\n",
" # Running interval in nanoseconds.\n",
" while hbt.elapse(100_000_000) == 0:\n",
" # Clears cancelled, filled or expired orders. \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 = depth.best_bid\n",
" best_ask = depth.best_ask\n",
" \n",
" mid_price = (best_bid + best_ask) / 2.0\n",
" \n",
" order_qty = 0.1 # np.round(notional_order_qty / mid_price / hbt.depth(asset_no).lot_size) * hbt.depth(asset_no).lot_size\n",
" \n",
" # The personalized price that considers skewing based on inventory risk is introduced, \n",
" # which is described in the well-known Stokov-Avalleneda market-making paper.\n",
" # https://math.nyu.edu/~avellane/HighFrequencyTrading.pdf\n",
" reservation_price = mid_price - skew * tick_size * position\n",
"\n",
" # Since our price is skewed, it may cross the spread. To ensure market making and avoid crossing the spread, \n",
" # limit the price to the best bid and best ask.\n",
" bid_price = np.minimum(reservation_price - half_spread, best_bid)\n",
" ask_price = np.maximum(reservation_price + half_spread, best_ask)\n",
"\n",
" # Aligns the prices to the grid.\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): # position * mid_price < max_notional_position\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): # position * mid_price > -max_notional_position\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",
" # Records the current state for stat calculation.\n",
" recorder.record(hbt)\n",
" return True"
]
},
{
"cell_type": "markdown",
"id": "851322e3",
"metadata": {},
"source": [
"### Weak skew"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6022d9f0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats.plot()"
]
},
{
"cell_type": "markdown",
"id": "a1a26c28",
"metadata": {},
"source": [
"### Strong skew\n",
"Under strong skew, the position is more limited compared to the weak skew case. You may also observe a spike in equity when the market moves sharply. However, in reality, this might not be realized due to order latency. Later, we will explore the impact of order latency and highlight the importance of using actual historical order latency data."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f747552e",
"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
2022-10-03 00:00:00
2022-10-07 23:59:50
27.282302
47.25453
0.042574
0.005391
11838.874048
158.842253
7.897853
0.000054
8270.01
"
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"text/plain": [
"shape: (1, 11)\n",
"┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
"│ start ┆ end ┆ SR ┆ Sortino ┆ … ┆ DailyTurn ┆ ReturnOve ┆ ReturnOve ┆ MaxPositi │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ over ┆ rMDD ┆ rTrade ┆ onValue │\n",
"│ datetime[ ┆ datetime[ ┆ f64 ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ μs] ┆ μs] ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n",
"╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
"│ 2022-10-0 ┆ 2022-10-0 ┆ 27.282302 ┆ 47.25453 ┆ … ┆ 158.84225 ┆ 7.897853 ┆ 0.000054 ┆ 8270.01 │\n",
"│ 3 ┆ 7 ┆ ┆ ┆ ┆ 3 ┆ ┆ ┆ │\n",
"│ 00:00:00 ┆ 23:59:50 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hbt = ROIVectorMarketDepthBacktest([asset])\n",
"\n",
"skew = 10\n",
"\n",
"recorder = Recorder(1, 5_000_000)\n",
"\n",
"gridtrading(hbt, recorder.recorder, skew)\n",
"\n",
"hbt.close()\n",
"\n",
"stats = LinearAssetRecord(recorder.get(0)).stats(book_size=10_000)\n",
"stats.summary()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a8e58e3c-a2f1-4e62-a648-b6e58342d23a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats.plot()"
]
},
{
"cell_type": "markdown",
"id": "d4ff955e",
"metadata": {},
"source": [
"## Multiple Assets\n",
"\n",
"You might need to find the proper parameters for each asset to achieve better performance. As an example, here it uses single parameters set to demonstrate how the performance of a combination of multiple assets will be."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ab58d41d",
"metadata": {},
"outputs": [],
"source": [
"@njit\n",
"def gridtrading(hbt, recorder, half_spread, grid_interval, skew, order_qty):\n",
" asset_no = 0\n",
" tick_size = hbt.depth(asset_no).tick_size\n",
" grid_num = 20\n",
" max_position = grid_num * order_qty\n",
" \n",
" # Running interval in nanoseconds.\n",
" while hbt.elapse(100_000_000) == 0: \n",
" # Clears cancelled, filled or expired orders. \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 = depth.best_bid\n",
" best_ask = depth.best_ask\n",
" \n",
" mid_price = (best_bid + best_ask) / 2.0\n",
"\n",
" normalized_position = position / order_qty\n",
"\n",
" # The personalized price that considers skewing based on inventory risk is introduced, \n",
" # which is described in the well-known Stokov-Avalleneda market-making paper.\n",
" # https://math.nyu.edu/~avellane/HighFrequencyTrading.pdf\n",
" reservation_price = mid_price - skew * normalized_position\n",
"\n",
" # Since our price is skewed, it may cross the spread. To ensure market making and avoid crossing the spread, \n",
" # limit the price to the best bid and best ask.\n",
" bid_price = np.minimum(reservation_price - half_spread, best_bid)\n",
" ask_price = np.maximum(reservation_price + half_spread, best_ask)\n",
"\n",
" # Ensures the grid interval aligns with the tick size, with the minimum set to the tick size.\n",
" grid_interval = max(np.round(grid_interval / tick_size) * tick_size, tick_size)\n",
"\n",
" # Aligns the prices to the grid.\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): # position * mid_price < max_notional_position\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): # position * mid_price > -max_notional_position\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",
" # Records the current state for stat calculation.\n",
" recorder.record(hbt)\n",
" return True"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "85517083",
"metadata": {},
"outputs": [],
"source": [
"from hftbacktest import BUY_EVENT, SELL_EVENT\n",
"\n",
"latency_data = np.concatenate(\n",
" [np.load('latency/live_latency_{}.npz'.format(date))['data'] for date in range(20230701, 20230732)]\n",
")\n",
" \n",
"def backtest(args):\n",
" asset_name, asset_info = args\n",
"\n",
" # Obtains the mid-price of the assset to determine the order quantity.\n",
" snapshot = np.load('data/{}_20230630_eod.npz'.format(asset_name))['data']\n",
" best_bid = max(snapshot[snapshot['ev'] & BUY_EVENT == BUY_EVENT]['px'])\n",
" best_ask = min(snapshot[snapshot['ev'] & SELL_EVENT == SELL_EVENT]['px'])\n",
" mid_price = (best_bid + best_ask) / 2.0\n",
" \n",
" asset = (\n",
" BacktestAsset()\n",
" .data(['data/{}_{}.npz'.format(asset_name, date) for date in range(20230701, 20230732)])\n",
" .initial_snapshot('data/{}_20230630_eod.npz'.format(asset_name))\n",
" .linear_asset(1.0) \n",
" .intp_order_latency(latency_data)\n",
" .log_prob_queue_model2() \n",
" .no_partial_fill_exchange()\n",
" .trading_value_fee_model(-0.00005, 0.0007)\n",
" .tick_size(asset_info['tick_size'])\n",
" .lot_size(asset_info['lot_size'])\n",
" .roi_lb(0)\n",
" .roi_ub(mid_price * 5)\n",
" )\n",
" hbt = ROIVectorMarketDepthBacktest([asset])\n",
"\n",
" # Sets the order quantity to be equivalent to a notional value of $100.\n",
" order_qty = max(round((100 / mid_price) / asset_info['lot_size']), 1) * asset_info['lot_size']\n",
"\n",
" half_spread = mid_price * 0.0008\n",
" grid_interval = mid_price * 0.0008\n",
" skew = mid_price * 0.000025\n",
"\n",
" recorder = Recorder(1, 50_000_000)\n",
" \n",
" gridtrading(hbt, recorder.recorder, half_spread, grid_interval, skew, order_qty)\n",
"\n",
" hbt.close()\n",
"\n",
" recorder.to_npz('stats/gridtrading_{}.npz'.format(asset_name))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "59c50c74-2005-46b7-8318-9fb6bdc9a044",
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"\n",
"import json\n",
"from multiprocessing import Pool\n",
"\n",
"with open('assets.json', 'r') as f:\n",
" assets = json.load(f)\n",
"\n",
"with Pool(16) as p:\n",
" print(p.map(backtest, list(assets.items())))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "01c1fe7a-1574-4706-9c84-97e1696a9108",
"metadata": {},
"outputs": [],
"source": [
"import polars as pl\n",
"from hftbacktest.stats import LinearAssetRecord\n",
"\n",
"equity_values = {}\n",
"for asset_name in assets.keys():\n",
" data = np.load('stats/gridtrading_{}.npz'.format(asset_name))['0']\n",
" stats = (\n",
" LinearAssetRecord(data)\n",
" .resample('5m')\n",
" .stats()\n",
" )\n",
"\n",
" equity = stats.entire.with_columns(\n",
" (pl.col('equity_wo_fee') - pl.col('fee')).alias('equity')\n",
" ).select(['timestamp', 'equity'])\n",
" equity_values[asset_name] = equity"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3407726b-00eb-415f-86b6-8b2f05871767",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Cumulative Returns (%)')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"fig = plt.figure()\n",
"fig.set_size_inches(10, 3)\n",
"\n",
"legend = []\n",
"net_equity = None\n",
"for i, equity in enumerate(list(equity_values.values())):\n",
" asset_number = i + 1\n",
" if net_equity is None:\n",
" net_equity = equity['equity'].clone()\n",
" else:\n",
" net_equity += equity['equity'].clone()\n",
"\n",
" if asset_number % 10 == 0:\n",
" # 2_000 is capital for each trading asset.\n",
" net_equity_df = pl.DataFrame({\n",
" 'cum_ret': (net_equity / asset_number) / 2_000 * 100,\n",
" 'timestamp': equity['timestamp']\n",
" })\n",
" net_equity_rs_df = net_equity_df.group_by_dynamic(\n",
" index_column='timestamp',\n",
" every='1d'\n",
" ).agg([\n",
" pl.col('cum_ret').last()\n",
" ])\n",
" pnl = net_equity_rs_df['cum_ret'].diff()\n",
" sr = pnl.mean() / pnl.std()\n",
" ann_sr = sr * np.sqrt(365)\n",
"\n",
" plt.plot(net_equity_df['timestamp'], net_equity_df['cum_ret'])\n",
" legend.append('{} assets, SR={:.2f} (Daily SR={:.2f})'.format(asset_number, ann_sr, sr))\n",
"\n",
"plt.legend(\n",
" legend,\n",
" loc='upper center', bbox_to_anchor=(0.5, -0.15),\n",
" fancybox=True, shadow=True, ncol=3\n",
")\n",
"\n",
"plt.grid()\n",
"plt.ylabel('Cumulative Returns (%)')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}