Working with Market Depth and Trades
Display 3-depth
[1]:
from numba import njit
@njit
def print_3depth(hbt):
while hbt.elapse(60 * 1e6):
# a key of bid_depth or ask_depth is price in tick format.
# (integer) price_tick = price / tick_size
print('current_timestamp:', hbt.current_timestamp)
i = 0
for tick_price in range(hbt.best_ask_tick, hbt.high_ask_tick + 1):
if tick_price in hbt.ask_depth:
print(
'ask: ',
hbt.ask_depth[tick_price],
'@',
round(tick_price * hbt.tick_size, 3)
)
i += 1
if i == 3:
break
i = 0
for tick_price in range(hbt.best_bid_tick, hbt.low_bid_tick - 1, -1):
if tick_price in hbt.bid_depth:
print(
'bid: ',
hbt.bid_depth[tick_price],
'@',
round(tick_price * hbt.tick_size, 3)
)
i += 1
if i == 3:
break
return True
[2]:
import numpy as np
btcusdt_20230405 = np.load('btcusdt_20230405.npz')['data']
btcusdt_20230404_eod = np.load('btcusdt_20230404_eod.npz')['data']
[3]:
from hftbacktest import HftBacktest, FeedLatency, Linear
hbt = HftBacktest(
btcusdt_20230405,
tick_size=0.1,
lot_size=0.001,
maker_fee=0.0002,
taker_fee=0.0007,
order_latency=FeedLatency(),
asset_type=Linear,
snapshot=btcusdt_20230404_eod
)
print_3depth(hbt)
current_timestamp: 1680652860032116
ask: 9.228 @ 28150.8
ask: 0.387 @ 28150.9
ask: 3.996 @ 28151.0
bid: 3.135 @ 28150.7
bid: 0.002 @ 28150.6
bid: 0.813 @ 28150.5
current_timestamp: 1680652920032116
ask: 1.224 @ 28144.2
ask: 0.223 @ 28144.3
ask: 0.001 @ 28144.5
bid: 10.529 @ 28144.1
bid: 0.168 @ 28144.0
bid: 0.29 @ 28143.9
current_timestamp: 1680652980032116
ask: 3.397 @ 28150.0
ask: 1.282 @ 28150.1
ask: 0.003 @ 28150.4
bid: 7.951 @ 28149.9
bid: 0.02 @ 28149.8
bid: 0.02 @ 28149.7
current_timestamp: 1680653040032116
ask: 3.905 @ 28145.8
ask: 1.695 @ 28145.9
ask: 0.003 @ 28146.0
bid: 5.793 @ 28145.7
bid: 0.059 @ 28145.6
bid: 0.044 @ 28145.5
current_timestamp: 1680653100032116
ask: 6.8 @ 28140.6
ask: 0.001 @ 28140.7
ask: 0.004 @ 28141.1
bid: 2.416 @ 28140.5
bid: 0.004 @ 28140.4
bid: 0.012 @ 28140.3
current_timestamp: 1680653160032116
ask: 3.666 @ 28143.9
ask: 1.422 @ 28144.0
ask: 1.455 @ 28144.1
bid: 3.189 @ 28143.8
bid: 5.136 @ 28143.7
bid: 0.012 @ 28143.5
[3]:
True
Order Book Imbalance
[4]:
@njit
def orderbookimbalance(hbt, out):
while hbt.elapse(10 * 1e6):
mid_price = (hbt.best_bid + hbt.best_ask) / 2.0
sum_ask_qty_50bp = 0.0
sum_ask_qty = 0.0
for tick_price in range(hbt.best_ask_tick, hbt.high_ask_tick + 1):
if tick_price in hbt.ask_depth:
ask_price = tick_price * hbt.tick_size
depth_from_mid = (ask_price - mid_price) / mid_price
if depth_from_mid > 0.01:
break
sum_ask_qty += hbt.ask_depth[tick_price]
if depth_from_mid <= 0.005:
sum_ask_qty_50bp = sum_ask_qty
sum_bid_qty_50bp = 0.0
sum_bid_qty = 0.0
for tick_price in range(hbt.best_bid_tick, hbt.low_bid_tick - 1, -1):
if tick_price in hbt.bid_depth:
bid_price = tick_price * hbt.tick_size
depth_from_mid = (mid_price - bid_price) / mid_price
if depth_from_mid > 0.01:
break
sum_bid_qty += hbt.bid_depth[tick_price]
if depth_from_mid <= 0.005:
sum_bid_qty_50bp = sum_bid_qty
imbalance_50bp = sum_bid_qty_50bp - sum_ask_qty_50bp
imbalance_1pct = sum_bid_qty - sum_ask_qty
imbalance_tob = hbt.bid_depth[hbt.best_bid_tick] - hbt.ask_depth[hbt.best_ask_tick]
out.append((hbt.current_timestamp, imbalance_tob, imbalance_50bp, imbalance_1pct))
return True
[5]:
from numba.typed import List
from numba.types import Tuple, float64
hbt = HftBacktest(
btcusdt_20230405,
tick_size=0.1,
lot_size=0.001,
maker_fee=0.0002,
taker_fee=0.0007,
order_latency=FeedLatency(),
asset_type=Linear,
snapshot=btcusdt_20230404_eod
)
tup_ty = Tuple((float64, float64, float64, float64))
out = List.empty_list(tup_ty, allocated=100_000)
orderbookimbalance(hbt, out)
[5]:
True
[6]:
import polars as pl
df = pl.DataFrame(out).transpose()
df.columns = ['Local Timestamp', 'TOB Imbalance', '0.5% Imbalance', '1% Imbalance']
df = df.with_columns(
pl.from_epoch('Local Timestamp', time_unit='us')
)
df
[6]:
shape: (41, 4)
| Local Timestamp | TOB Imbalance | 0.5% Imbalance | 1% Imbalance |
|---|---|---|---|
| datetime[μs] | f64 | f64 | f64 |
| 2023-04-05 00:00:10.032116 | 10.62 | 79.522 | -521.026 |
| 2023-04-05 00:00:20.032116 | -4.868 | -152.592 | -684.247 |
| 2023-04-05 00:00:30.032116 | 0.59 | -161.95 | -701.843 |
| 2023-04-05 00:00:40.032116 | 3.962 | -142.51 | -669.033 |
| 2023-04-05 00:00:50.032116 | 4.912 | -114.827 | -653.331 |
| 2023-04-05 00:01:00.032116 | -6.093 | 273.067 | -755.551 |
| 2023-04-05 00:01:10.032116 | 10.201 | -36.007 | -701.998 |
| 2023-04-05 00:01:20.032116 | 3.009 | 121.205 | -735.241 |
| 2023-04-05 00:01:30.032116 | 8.383 | 381.521 | -704.022 |
| 2023-04-05 00:01:40.032116 | 8.672 | 166.934 | -691.313 |
| 2023-04-05 00:01:50.032116 | -2.575 | 393.212 | -695.99 |
| 2023-04-05 00:02:00.032116 | 9.305 | 165.543 | -701.034 |
| … | … | … | … |
| 2023-04-05 00:05:00.032116 | -4.384 | 504.205 | -1005.883 |
| 2023-04-05 00:05:10.032116 | -10.432 | 506.615 | -952.28 |
| 2023-04-05 00:05:20.032116 | 20.341 | 542.513 | -917.164 |
| 2023-04-05 00:05:30.032116 | 3.113 | 587.247 | -858.536 |
| 2023-04-05 00:05:40.032116 | 1.212 | 542.287 | -901.735 |
| 2023-04-05 00:05:50.032116 | 3.997 | 184.424 | -833.991 |
| 2023-04-05 00:06:00.032116 | -0.477 | 180.863 | -825.373 |
| 2023-04-05 00:06:10.032116 | -3.77 | 525.716 | -887.492 |
| 2023-04-05 00:06:20.032116 | -5.273 | 434.96 | -1004.985 |
| 2023-04-05 00:06:30.032116 | -4.487 | 570.354 | -837.517 |
| 2023-04-05 00:06:40.032116 | -6.186 | 565.936 | -838.518 |
| 2023-04-05 00:06:50.032116 | 3.351 | 534.445 | -870.112 |
[7]:
import matplotlib.pyplot as plt
plt.plot(df['Local Timestamp'], df['TOB Imbalance'])
plt.plot(df['Local Timestamp'], df['0.5% Imbalance'])
plt.plot(df['Local Timestamp'], df['1% Imbalance'])
plt.legend(['TOB Imbalance', '0.5% Imbalance', '1% Imbalance'])
[7]:
<matplotlib.legend.Legend at 0x7f271704caf0>
Display last trades between the step
[31]:
from hftbacktest import COL_EXCH_TIMESTAMP, COL_SIDE, COL_PRICE, COL_QTY
@njit
def print_trades(hbt):
while hbt.elapse(60 * 1e6):
print('-------------------------------------------------------------------------------')
print('current_timestamp:', hbt.current_timestamp)
num = 0
for trade in hbt.last_trades:
if num > 10:
print('...')
break
print(
'exch_timestamp:',
trade[COL_EXCH_TIMESTAMP],
'buy' if trade[COL_SIDE] == 1 else 'sell',
trade[COL_QTY],
'@',
trade[COL_PRICE]
)
num += 1
hbt.clear_last_trades()
return True
[32]:
hbt = HftBacktest(
btcusdt_20230405,
tick_size=0.1,
lot_size=0.001,
maker_fee=0.0002,
taker_fee=0.0007,
order_latency=FeedLatency(),
asset_type=Linear,
snapshot=btcusdt_20230404_eod,
trade_list_size=10_000
)
print_trades(hbt)
-------------------------------------------------------------------------------
current_timestamp: 1680652860032116
exch_timestamp: 1680652804962000.0 sell 0.001 @ 28155.1
exch_timestamp: 1680652804964000.0 buy 0.012 @ 28155.2
exch_timestamp: 1680652804966000.0 buy 0.002 @ 28155.2
exch_timestamp: 1680652804968000.0 buy 0.003 @ 28155.2
exch_timestamp: 1680652804981000.0 sell 0.019 @ 28155.1
exch_timestamp: 1680652804981000.0 sell 0.004 @ 28155.1
exch_timestamp: 1680652804981000.0 sell 0.001 @ 28155.1
exch_timestamp: 1680652804981000.0 sell 0.02 @ 28155.1
exch_timestamp: 1680652804981000.0 sell 0.013 @ 28155.1
exch_timestamp: 1680652804981000.0 sell 0.002 @ 28155.1
exch_timestamp: 1680652804981000.0 sell 0.001 @ 28155.0
...
-------------------------------------------------------------------------------
current_timestamp: 1680652920032116
exch_timestamp: 1680652860008000.0 buy 1.887 @ 28150.8
exch_timestamp: 1680652860008000.0 buy 0.139 @ 28150.8
exch_timestamp: 1680652860009000.0 buy 0.053 @ 28150.8
exch_timestamp: 1680652860580000.0 buy 0.007 @ 28150.8
exch_timestamp: 1680652860605000.0 buy 0.063 @ 28150.8
exch_timestamp: 1680652860659000.0 sell 0.006 @ 28150.7
exch_timestamp: 1680652860659000.0 sell 0.011 @ 28150.7
exch_timestamp: 1680652860674000.0 buy 0.018 @ 28150.8
exch_timestamp: 1680652860696000.0 sell 0.009 @ 28150.7
exch_timestamp: 1680652860696000.0 sell 0.061 @ 28150.7
exch_timestamp: 1680652860821000.0 sell 0.05 @ 28150.7
...
-------------------------------------------------------------------------------
current_timestamp: 1680652980032116
exch_timestamp: 1680652920308000.0 buy 0.013 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.001 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.02 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.036 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.002 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.011 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.004 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.028 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.026 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.035 @ 28144.2
exch_timestamp: 1680652920308000.0 buy 0.026 @ 28144.2
...
-------------------------------------------------------------------------------
current_timestamp: 1680653040032116
exch_timestamp: 1680652980140000.0 buy 0.086 @ 28150.0
exch_timestamp: 1680652980140000.0 buy 0.002 @ 28150.0
exch_timestamp: 1680652980140000.0 buy 0.012 @ 28150.0
exch_timestamp: 1680652980140000.0 sell 0.02 @ 28149.9
exch_timestamp: 1680652980140000.0 sell 0.488 @ 28149.9
exch_timestamp: 1680652980140000.0 sell 0.195 @ 28149.9
exch_timestamp: 1680652980140000.0 sell 0.791 @ 28149.9
exch_timestamp: 1680652980213000.0 buy 0.007 @ 28150.0
exch_timestamp: 1680652980239000.0 sell 0.062 @ 28149.9
exch_timestamp: 1680652980266000.0 buy 0.001 @ 28150.0
exch_timestamp: 1680652980271000.0 buy 0.006 @ 28150.0
...
-------------------------------------------------------------------------------
current_timestamp: 1680653100032116
exch_timestamp: 1680653040100000.0 buy 0.007 @ 28145.8
exch_timestamp: 1680653040117000.0 buy 0.001 @ 28145.8
exch_timestamp: 1680653040117000.0 buy 0.006 @ 28145.8
exch_timestamp: 1680653040119000.0 buy 0.014 @ 28145.8
exch_timestamp: 1680653040120000.0 buy 0.008 @ 28145.8
exch_timestamp: 1680653040535000.0 sell 0.2 @ 28145.7
exch_timestamp: 1680653040652000.0 buy 0.004 @ 28145.8
exch_timestamp: 1680653040652000.0 buy 0.02 @ 28145.8
exch_timestamp: 1680653040652000.0 buy 0.216 @ 28145.8
exch_timestamp: 1680653040652000.0 buy 0.904 @ 28145.8
exch_timestamp: 1680653040838000.0 buy 0.008 @ 28145.8
...
-------------------------------------------------------------------------------
current_timestamp: 1680653160032116
exch_timestamp: 1680653100101000.0 sell 0.02 @ 28140.5
exch_timestamp: 1680653100182000.0 buy 0.007 @ 28140.6
exch_timestamp: 1680653100197000.0 buy 0.005 @ 28140.6
exch_timestamp: 1680653100230000.0 sell 0.02 @ 28140.5
exch_timestamp: 1680653100303000.0 buy 0.007 @ 28140.6
exch_timestamp: 1680653100341000.0 buy 0.017 @ 28140.6
exch_timestamp: 1680653100358000.0 sell 0.009 @ 28140.5
exch_timestamp: 1680653100358000.0 sell 0.041 @ 28140.5
exch_timestamp: 1680653100628000.0 buy 0.008 @ 28140.6
exch_timestamp: 1680653100706000.0 sell 0.004 @ 28140.5
exch_timestamp: 1680653100707000.0 sell 0.001 @ 28140.5
...
[32]:
True
Rolling Volume-Weighted Average Price
[10]:
@njit
def rolling_vwap(hbt, out):
buy_amount_bin = np.zeros(100_000, np.float64)
buy_qty_bin = np.zeros(100_000, np.float64)
sell_amount_bin = np.zeros(100_000, np.float64)
sell_qty_bin = np.zeros(100_000, np.float64)
idx = 0
last_trade_price = np.nan
while hbt.elapse(10 * 1e6):
for trade in hbt.last_trades:
if trade[COL_SIDE] == 1:
buy_amount_bin[idx] += trade[COL_PRICE] * trade[COL_QTY]
buy_qty_bin[idx] += trade[COL_QTY]
else:
sell_amount_bin[idx] += trade[COL_PRICE] * trade[COL_QTY]
sell_qty_bin[idx] += trade[COL_QTY]
hbt.clear_last_trades()
idx += 1
if idx >= 1:
vwap10sec = np.divide(
buy_amount_bin[idx - 1] + sell_amount_bin[idx - 1],
buy_qty_bin[idx - 1] + sell_qty_bin[idx - 1]
)
else:
vwap10sec = np.nan
if idx >= 6:
vwap1m = np.divide(
np.sum(buy_amount_bin[idx - 6:idx]) + np.sum(sell_amount_bin[idx - 6:idx]),
np.sum(buy_qty_bin[idx - 6:idx]) + np.sum(sell_qty_bin[idx - 6:idx])
)
buy_vwap1m = np.divide(np.sum(buy_amount_bin[idx - 6:idx]), np.sum(buy_qty_bin[idx - 6:idx]))
sell_vwap1m = np.divide(np.sum(sell_amount_bin[idx - 6:idx]), np.sum(sell_qty_bin[idx - 6:idx]))
else:
vwap1m = np.nan
buy_vwap1m = np.nan
sell_vwap1m = np.nan
out.append((hbt.current_timestamp, vwap10sec, vwap1m, buy_vwap1m, sell_vwap1m))
return True
[11]:
hbt = HftBacktest(
btcusdt_20230405,
tick_size=0.1,
lot_size=0.001,
maker_fee=0.0002,
taker_fee=0.0007,
order_latency=FeedLatency(),
asset_type=Linear,
snapshot=btcusdt_20230404_eod,
trade_list_size=1_000_000
)
tup_ty = Tuple((float64, float64, float64, float64, float64))
out = List.empty_list(tup_ty, allocated=100_000)
rolling_vwap(hbt, out)
[11]:
True
[12]:
df = pl.DataFrame(out).transpose()
df.columns = ['Local Timestamp', '10-sec VWAP', '1-min VWAP', '1-min Buy VWAP', '1-min Sell VWAP']
df = df.with_columns(
pl.from_epoch('Local Timestamp', time_unit='us')
)
df
[12]:
shape: (41, 5)
| Local Timestamp | 10-sec VWAP | 1-min VWAP | 1-min Buy VWAP | 1-min Sell VWAP |
|---|---|---|---|---|
| datetime[μs] | f64 | f64 | f64 | f64 |
| 2023-04-05 00:00:10.032116 | 28152.252939 | NaN | NaN | NaN |
| 2023-04-05 00:00:20.032116 | 28155.780263 | NaN | NaN | NaN |
| 2023-04-05 00:00:30.032116 | 28158.015906 | NaN | NaN | NaN |
| 2023-04-05 00:00:40.032116 | 28155.813019 | NaN | NaN | NaN |
| 2023-04-05 00:00:50.032116 | 28156.783983 | NaN | NaN | NaN |
| 2023-04-05 00:01:00.032116 | 28154.537949 | 28155.548141 | 28155.915523 | 28155.223338 |
| 2023-04-05 00:01:10.032116 | 28148.396356 | 28153.368132 | 28154.890947 | 28152.319296 |
| 2023-04-05 00:01:20.032116 | 28146.825218 | 28152.039289 | 28153.901905 | 28150.83132 |
| 2023-04-05 00:01:30.032116 | 28144.455386 | 28150.627706 | 28151.934098 | 28149.81655 |
| 2023-04-05 00:01:40.032116 | 28144.432187 | 28149.560181 | 28150.540685 | 28148.888516 |
| 2023-04-05 00:01:50.032116 | 28144.340373 | 28147.623107 | 28148.057738 | 28147.411383 |
| 2023-04-05 00:02:00.032116 | 28142.735181 | 28146.012256 | 28146.175163 | 28145.918324 |
| … | … | … | … | … |
| 2023-04-05 00:05:00.032116 | 28140.810824 | 28142.411037 | 28142.7071 | 28142.244012 |
| 2023-04-05 00:05:10.032116 | 28139.182176 | 28141.264196 | 28142.570677 | 28140.811206 |
| 2023-04-05 00:05:20.032116 | 28138.95427 | 28140.263581 | 28140.273185 | 28140.260421 |
| 2023-04-05 00:05:30.032116 | 28139.49472 | 28139.946069 | 28139.43771 | 28140.107281 |
| 2023-04-05 00:05:40.032116 | 28139.720917 | 28139.82033 | 28139.223683 | 28140.005444 |
| 2023-04-05 00:05:50.032116 | 28140.151155 | 28139.602697 | 28139.723243 | 28139.493739 |
| 2023-04-05 00:06:00.032116 | 28143.477257 | 28139.927132 | 28140.206712 | 28139.635456 |
| 2023-04-05 00:06:10.032116 | 28142.323272 | 28140.338009 | 28140.35086 | 28140.321614 |
| 2023-04-05 00:06:20.032116 | 28138.54843 | 28140.391805 | 28140.716124 | 28140.025369 |
| 2023-04-05 00:06:30.032116 | 28137.611515 | 28139.958313 | 28139.962781 | 28139.951255 |
| 2023-04-05 00:06:40.032116 | 28140.107487 | 28139.965883 | 28139.964373 | 28139.9681 |
| 2023-04-05 00:06:50.032116 | 28139.449719 | 28139.746092 | 28139.841886 | 28139.673659 |
[13]:
plt.plot(df['Local Timestamp'], df['10-sec VWAP'])
plt.plot(df['Local Timestamp'], df['1-min VWAP'])
plt.plot(df['Local Timestamp'], df['1-min Buy VWAP'])
plt.plot(df['Local Timestamp'], df['1-min Sell VWAP'])
plt.legend(['10-sec VWAP', '1-min VWAP', '1-min Buy VWAP', '1-min Sell VWAP'])
[13]:
<matplotlib.legend.Legend at 0x7f271ad2bac0>
