python-market-break 3.1.1

A set of tools to help create algorithmic trading strategies and test them, in a vectorized way.

market-break

a set of utilities for backtesting vectorized trading algorithms, high-frequency trading strategies and investing strategies.

Installation

---

pip install python-market-break

Connect to a live database for real-time data.

from sqlalchemy import create_engine

engine = create_engine('sqlite:///database/2024-06-14.sqlite')

Extract The data from the database into a DataFrame.

from cryptocore.market.database import extract_dataframe, table_name

EXCHANGE = "binance"
SYMBOL = "ETH/USDT"

df = extract_dataframe(engine, table_name(exchange=EXCHANGE, symbol=SYMBOL))
df = df.iloc[3500:7000].reset_index()

df

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	index	exchange	symbol	timestamp	datetime	received_datetime	open	high	low	close	bid	ask	bid_volume	ask_volume	side
0	3500	binance	ETH/USDT	1.718353e+12	2024-06-14 11:19:38.208	2024-06-14 08:19:38.156	3510.78	3539.83	3428.0	3521.99	3521.98	3521.99	59.7590	21.0623	buy
1	3501	binance	ETH/USDT	1.718353e+12	2024-06-14 11:19:39.031	2024-06-14 08:19:39.155	3510.78	3539.83	3428.0	3521.98	3521.98	3521.99	58.2914	28.2213	sell
2	3502	binance	ETH/USDT	1.718353e+12	2024-06-14 11:19:39.857	2024-06-14 08:19:40.158	3510.78	3539.83	3428.0	3521.99	3521.98	3521.99	58.2418	24.8653	buy
3	3503	binance	ETH/USDT	1.718353e+12	2024-06-14 11:19:40.574	2024-06-14 08:19:41.179	3510.78	3539.83	3428.0	3521.99	3521.98	3521.99	63.9093	24.8653	buy
4	3504	binance	ETH/USDT	1.718353e+12	2024-06-14 11:19:42.080	2024-06-14 08:19:42.161	3510.78	3539.83	3428.0	3521.99	3521.98	3521.99	64.6069	14.6805	buy
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3495	6995	binance	ETH/USDT	1.718357e+12	2024-06-14 12:17:55.995	2024-06-14 09:17:55.971	3493.19	3539.83	3428.0	3518.19	3518.19	3518.20	125.1496	5.5759	sell
3496	6996	binance	ETH/USDT	1.718357e+12	2024-06-14 12:17:56.069	2024-06-14 09:17:56.976	3493.19	3539.83	3428.0	3518.20	3518.19	3518.20	125.1496	5.5728	buy
3497	6997	binance	ETH/USDT	1.718357e+12	2024-06-14 12:17:57.779	2024-06-14 09:17:58.075	3492.59	3539.83	3428.0	3518.20	3518.19	3518.20	121.2325	7.5728	buy
3498	6998	binance	ETH/USDT	1.718357e+12	2024-06-14 12:17:58.886	2024-06-14 09:17:58.973	3492.59	3539.83	3428.0	3518.20	3518.19	3518.20	83.2826	7.7234	buy
3499	6999	binance	ETH/USDT	1.718357e+12	2024-06-14 12:18:00.028	2024-06-14 09:17:59.973	3492.60	3539.83	3428.0	3518.20	3518.19	3518.20	75.6586	9.3705	buy

3500 rows × 15 columns

Import column names to handle the data.

from market_break.labels import BID, ASK, DATETIME, ENTRY, EXIT, TYPE, LONG

Import a class of backtesting functions.

from market_break.backtest import Trades

Generate trades record, both short and long as a DataFrame, from definition of up-trends and down-trends.

trades = Trades.generate(
    up=df[ASK] > df[ASK].shift(1),
    down=df[BID] < df[BID].shift(1),
    adjust=True
)

Process the results of the generated trades.

FEE = 0.001

returns = Trades.returns(trades, bid=df[BID], ask=df[ASK], fee=FEE)

Imports a class of Plotting trading results.

from market_break.backtest import Plot

Plot a histogram of the trades returns.

Plot.returns_histogram(returns, bins=35)

Plot a graph of the trades returns.

Plot.returns_signals(returns, index=df[DATETIME].iloc)

Plot pie graphs of the winning and losing trades and their profits and losses.

Plot.returns_pie(returns)

Plot the signals for long and short entries and exits, with the bid-ask spread. Also plotting The balance, profits and losses.

long_trades = trades[trades[TYPE] == LONG]

Plot.price_signals(
    bid=df[BID], ask=df[ASK], index=df[DATETIME].iloc,
    long=long_trades[ENTRY], short=long_trades[EXIT]
)
Plot.returns_balance(returns, index=df[DATETIME].iloc)

Import a class for generating a report from the results.

from market_break.backtest import Report

Generating and displaying the results with the report.

report = Report.generate(
    index=df[DATETIME], returns=returns, 
    long=trades[EXIT].values, short=trades[EXIT].values
)

print(Report.repr(report))

[Index]
start                   2024-06-14 11:19:38.20
end                     2024-06-14 12:18:00.02
total duration              0 days 00:58:21.82
min. tick duration                  0:00:00.06
max. tick duration                  0:00:01.90
avg. tick duration                  0:00:01.00
ticks                                     3500

[Trades]
long trades                                125
short trades                               125
min. TUW                                   0:0
max. TUW                                   0:0
avg. TUW                                   0:0

[Gains]
[%] min. gain                           0.0003
[%] max. gain                           0.1482
[%] avg. gain                           0.0291
[%] total gains                         2.4411
winning trades                              84

[Losses]
[%] min. loss                          -0.0003
[%] max. loss                           -0.017
[%] avg. loss                          -0.0063
[%] total losses                       -0.2383
losing trades                               38

[Performance]
PnL factor                              10.245
avg. profit factor                      1.0002
[%] win rate                              67.2
[%] total profit                        2.2029