Tags: synthetic-data, ema, trend-analysis, stop-loss

Synthetic data w/stop loss backtesting example#

This is an example notebook how to create and run backtests where stop loss is being used. It is based on synthetic EMA example.

  • Synthetic trading data is used, as the purpose of this notebook is show how to stop loss functions

  • Stop loss is set to 95% when a position is opened with open_1x_long

Set up#

Set up strategy paramets that will decide its behavior

[1]:

import datetime

import pandas as pd

from tradingstrategy.chain import ChainId
from tradingstrategy.timebucket import TimeBucket
from tradeexecutor.strategy.cycle import CycleDuration
from tradeexecutor.strategy.strategy_module import TradeRouting, ReserveCurrency

trading_strategy_cycle = CycleDuration.cycle_1d

# Strategy keeps its cash in BUSD
reserve_currency = ReserveCurrency.busd

# How much of the cash to put on a single trade
position_size = 0.10

#
# Strategy thinking specific parameter
#

slow_ema_candle_count = 20

fast_ema_candle_count = 5

# How many candles to extract from the dataset once
batch_size = 90

# Set stop loss to 5% of opening price
stop_loss_pct = 0.95

# Range of backtesting and synthetic data generation.
# Because we are using synthetic data actual dates do not really matter -
# only the duration

start_at = datetime.datetime(2021, 6, 1)
start_at_data = datetime.datetime(2021,1,1)
end_at = datetime.datetime(2022, 1, 1)

Strategy logic and trade decisions#

decide_trades function decide what trades to take. In this example, we calculate two exponential moving averages (EMAs) and make decisions based on those.

[2]:

from typing import List, Dict

from pandas_ta.overlap import ema

from tradingstrategy.universe import Universe

from tradeexecutor.state.visualisation import PlotKind
from tradeexecutor.state.trade import TradeExecution
from tradeexecutor.strategy.pricing_model import PricingModel
from tradeexecutor.strategy.pandas_trader.position_manager import PositionManager
from tradeexecutor.state.state import State



def decide_trades(
        timestamp: pd.Timestamp,
        universe: Universe,
        state: State,
        pricing_model: PricingModel,
        cycle_debug_data: Dict) -> List[TradeExecution]:
    """The brain function to decide the trades on each trading strategy cycle."""

    # The pair we are trading
    pair = universe.pairs.get_single()

    # How much cash we have in the hand
    cash = state.portfolio.get_current_cash()

    # Get OHLCV candles for our trading pair as Pandas Dataframe.
    # We could have candles for multiple trading pairs in a different strategy,
    # but this strategy only operates on single pair candle.
    # We also limit our sample size to N latest candles to speed up calculations.
    candles: pd.DataFrame = universe.candles.get_single_pair_data(timestamp, sample_count=batch_size)

    # We have data for open, high, close, etc.
    # We only operate using candle close values in this strategy.
    close = candles["close"]

    # Calculate exponential moving averages based on slow and fast sample numbers.
    # https://github.com/twopirllc/pandas-ta
    # https://github.com/twopirllc/pandas-ta/blob/bc3b292bf1cc1d5f2aba50bb750a75209d655b37/pandas_ta/overlap/ema.py#L7
    slow_ema_series = ema(close, length=slow_ema_candle_count)
    fast_ema_series = ema(close, length=fast_ema_candle_count)

    if slow_ema_series is None or fast_ema_series is None:
        # Cannot calculate EMA, because
        # not enough samples in backtesting
        return []

    slow_ema = slow_ema_series.iloc[-1]
    fast_ema = fast_ema_series.iloc[-1]

    # Get the last close price from close time series
    # that's Pandas's Series object
    # https://pandas.pydata.org/docs/reference/api/pandas.Series.iat.html
    current_price = close.iloc[-1]

    # List of any trades we decide on this cycle.
    # Because the strategy is simple, there can be
    # only zero (do nothing) or 1 (open or close) trades
    # decides
    trades = []

    # Create a position manager helper class that allows us easily to create
    # opening/closing trades for different positions
    position_manager = PositionManager(timestamp, universe, state, pricing_model)

    if not position_manager.is_any_open():

        if current_price >= slow_ema:
        # Entry condition:
        # Close price is higher than the slow EMA
            buy_amount = cash * position_size
            trades += position_manager.open_1x_long(pair, buy_amount, stop_loss_pct=stop_loss_pct)
    else:

        if fast_ema >= slow_ema:
        # Exit condition:
        # Fast EMA crosses slow EMA
            trades += position_manager.close_all()

    # Visualize strategy
    # See available Plotly colours here
    # https://community.plotly.com/t/plotly-colours-list/11730/3?u=miohtama
    visualisation = state.visualisation
    visualisation.plot_indicator(timestamp, "Slow EMA", PlotKind.technical_indicator_on_price, slow_ema, colour="darkblue")
    visualisation.plot_indicator(timestamp, "Fast EMA", PlotKind.technical_indicator_on_price, fast_ema, colour="#003300")

    return trades

Defining trading universe#

We create a trading universe with a single blockchain, exchange and trading pair. For the sake of easier understanding the code, we name this “Uniswap v2” like exchange with a single ETH-USDC trading pair.

The trading pair contains generated noise-like OHLCV trading data.

[3]:


import random
from tradeexecutor.state.identifier import AssetIdentifier, TradingPairIdentifier
from tradingstrategy.candle import GroupedCandleUniverse
from tradeexecutor.testing.synthetic_ethereum_data import generate_random_ethereum_address
from tradeexecutor.testing.synthetic_exchange_data import generate_exchange
from tradeexecutor.testing.synthetic_price_data import generate_ohlcv_candles
from tradeexecutor.strategy.trading_strategy_universe import TradingStrategyUniverse, \
    create_pair_universe_from_code

def create_trading_universe() -> TradingStrategyUniverse:

    # Set up fake assets
    mock_chain_id = ChainId.ethereum
    mock_exchange = generate_exchange(
        exchange_id=random.randint(1, 1000),
        chain_id=mock_chain_id,
        address=generate_random_ethereum_address())

    usdc = AssetIdentifier(ChainId.ethereum.value, generate_random_ethereum_address(), "USDC", 6, 1)
    weth = AssetIdentifier(ChainId.ethereum.value, generate_random_ethereum_address(), "WETH", 18, 2)
    weth_usdc = TradingPairIdentifier(weth, usdc, generate_random_ethereum_address(), mock_exchange.address, internal_id=random.randint(1, 1000), internal_exchange_id=mock_exchange.exchange_id, fee=0.0005)

    time_bucket = TimeBucket.d1

    pair_universe = create_pair_universe_from_code(mock_chain_id, [weth_usdc])

    # Generate candle data with 15% daily movement
    candles = generate_ohlcv_candles(
        time_bucket,
        start_at_data,
        end_at,
        pair_id=weth_usdc.internal_id,
        daily_drift=(0.85, 1.15)
    )
    candle_universe = GroupedCandleUniverse.create_from_single_pair_dataframe(candles, time_bucket)

    universe = Universe(
        time_bucket=time_bucket,
        chains={mock_chain_id},
        exchanges={mock_exchange},
        pairs=pair_universe,
        candles=candle_universe,
        liquidity=None
    )

    # As we are using synthetic data,
    # we need to slip in stop loss data feed.
    # In this case, it is the same as normal price feed,
    # but usually should be finer granularity than our strategy candles.
    # E.g. if strategy candles are 1h you can use 15m candles for stop loss.
    return TradingStrategyUniverse(
        universe=universe,
        reserve_assets=[usdc],
        backtest_stop_loss_time_bucket=universe.candles.time_bucket,
        backtest_stop_loss_candles=universe.candles,
    )

Running the backtest#

Run backtest using giving trading universe and strategy function.

Running the backtest outputs state object that contains all the information on the backtesting position and trades.

[4]:

from tradeexecutor.testing.synthetic_exchange_data import generate_simple_routing_model
from tradeexecutor.backtest.backtest_runner import run_backtest_inline

universe = create_trading_universe()

# Check that synthetic trading data has price feed
# to check stop losses
assert universe.has_stop_loss_data()

start_candle, end_candle = universe.universe.candles.get_timestamp_range()
print(f"Our universe has synthetic candle data for the period {start_candle} - {end_candle}")

state, universe,  debug_dump = run_backtest_inline(
    name="Stop loss example with synthetic data",
    start_at=start_at,
    end_at=end_at,
    client=None,  # None of downloads needed, because we are using synthetic data
    cycle_duration=trading_strategy_cycle,
    decide_trades=decide_trades,
    universe=universe,
    initial_deposit=10_000,
    reserve_currency=ReserveCurrency.busd,
    trade_routing=TradeRouting.user_supplied_routing_model,
    routing_model=generate_simple_routing_model(universe),
)


Our universe has synthetic candle data for the period 2021-01-01 00:00:00 - 2021-12-31 00:00:00

Examine backtest results#

Examine state that contains all actions the trade executor took.

We plot out a chart that shows - The price action - When the strategy made buys or sells - When sell was a stop loss sell

[5]:

print(f"Positions taken: {len(list(state.portfolio.get_all_positions()))}")
print(f"Trades made: {len(list(state.portfolio.get_all_trades()))}")

stop_loss_trades = [t for t in state.portfolio.get_all_trades() if t.is_stop_loss()]
print(f"Trades w/stop loss triggered: {len(stop_loss_trades)}")

Positions taken: 89
Trades made: 178
Trades w/stop loss triggered: 27

[6]:

from tradeexecutor.visual.single_pair import visualise_single_pair

figure = visualise_single_pair(state, universe.universe.candles)

figure.show()
### Equity curve and drawdown Visualise equity curve and related performnace over time. - Returns - Drawdown - Daily returns
[7]:

# Set Jupyter Notebook output mode parameters
# Used to avoid warnings
from tradeexecutor.backtest.notebook import setup_charting_and_output
setup_charting_and_output()

# Needed to improve the resolution of matplotlib chart used here
%config InlineBackend.figure_format = 'svg'

from tradeexecutor.visual.equity_curve import calculate_equity_curve, calculate_returns
from tradeexecutor.visual.equity_curve import visualise_equity_curve

curve = calculate_equity_curve(state)
returns = calculate_returns(curve)
visualise_equity_curve(returns)

[7]:
../../_images/programming_strategy-examples_synthetic-ema-stop-loss_14_0.svg

Returns monthly breakdown#

  • Monthly returns

  • Best day/week/month/year

[8]:

from tradeexecutor.visual.equity_curve import visualise_returns_over_time

visualise_returns_over_time(returns)

[8]:
../../_images/programming_strategy-examples_synthetic-ema-stop-loss_16_0.svg

Benchmarking the strategy performance#

Here we benchmark the strategy performance against some baseline scenarios.

  • Buy and hold US dollar

  • Buy and hold the underlying trading pair base asset

[9]:

close = universe.universe.candles.get_single_pair_data()["close"]

[10]:

from tradeexecutor.visual.benchmark import visualise_benchmark

traded_pair = universe.universe.pairs.get_single()

fig = visualise_benchmark(
    state.name,
    portfolio_statistics=state.stats.portfolio,
    all_cash=state.portfolio.get_initial_deposit(),
    buy_and_hold_asset_name=traded_pair.base_token_symbol,
    buy_and_hold_price_series=universe.universe.candles.get_single_pair_data()["close"],
)

fig.show()

Analysing the strategy success#

Here we calculate statistics on how well the strategy performed.

  • Won/lost trades

  • Timeline of taken positions with color coding of trade performance

[11]:

from tradeexecutor.analysis.trade_analyser import build_trade_analysis

analysis = build_trade_analysis(state.portfolio)

Strategy summary#

Overview of strategy performance

[12]:

from IPython.core.display_functions import display

summary = analysis.calculate_summary_statistics(TimeBucket.d1, state)

with pd.option_context("display.max_row", None):
    summary.display()
Returns
Annualised return % 10.80%
Lifetime return % 6.30%
Realised PnL $630.02
Trade period 213 days 0 hours
Holdings
Total assets $10,630.02
Cash left $10,630.02
Open position value $0.00
Open positions 0
Winning Losing Total
Closed Positions
Number of positions 50 39 89
% of total 56.18% 43.82% 100.00%
Average PnL % 7.04% -7.37% 0.72%
Median PnL % 6.81% -6.14% 1.16%
Biggest PnL % 14.76% -14.98% -
Average duration 1 bars 1 bars 1 bars
Max consecutive streak 5 4 -
Max runup / drawdown 9.65% -4.40% -
Stop losses Take profits
Position Exits
Triggered exits 27 0
Percent winning 0.00% -
Percent losing 100.00% -
Percent of total 30.34% 0.00%
Risk Analysis
Biggest realized risk 0.50%
Average realized risk -0.74%
Max pullback of capital -4.04%
Sharpe Ratio 104.48%
Sortino Ratio 156.59%
Profit Factor 121.46%

Performance metrics#

Here is an example how to use Quantstats library to calculate the tearsheet metrics for the strategy with advanced metrics. The metrics include popular risk-adjusted return comparison metrics.

This includes metrics like:

  • Sharpe

  • Sortino

  • Max drawdown

Note: These metrics are based on equity curve and returns. Analysis here does not go down to the level of an individual trade or a position. Any consecutive wins and losses are measured in days, not in trade or candle counts.

[13]:

from tradeexecutor.visual.equity_curve import calculate_equity_curve, calculate_returns
from tradeexecutor.analysis.advanced_metrics import visualise_advanced_metrics, AdvancedMetricsMode

equity = calculate_equity_curve(state)
returns = calculate_returns(equity)
metrics = visualise_advanced_metrics(returns, mode=AdvancedMetricsMode.full)

with pd.option_context("display.max_row", None):
    display(metrics)
Strategy
Start Period 2021-06-01
End Period 2021-12-31
Risk-Free Rate 0.0%
Time in Market 77.0%
Cumulative Return 6.31%
CAGR﹪ 11.06%
Sharpe 1.04
Prob. Sharpe Ratio 78.8%
Smart Sharpe 0.93
Sortino 1.57
Smart Sortino 1.39
Sortino/√2 1.11
Smart Sortino/√2 0.98
Omega 1.21
Max Drawdown -4.4%
Longest DD Days 75
Volatility (ann.) 10.52%
Calmar 2.51
Skew 0.08
Kurtosis 1.58
Expected Daily 0.03%
Expected Monthly 0.88%
Expected Yearly 6.31%
Kelly Criterion 5.75%
Risk of Ruin 0.0%
Daily Value-at-Risk -0.88%
Expected Shortfall (cVaR) -0.88%
Max Consecutive Wins 2
Max Consecutive Losses 7
Gain/Pain Ratio 0.21
Gain/Pain (1M) 1.65
Payoff Ratio 2.52
Profit Factor 1.21
Common Sense Ratio 1.22
CPC Index 1.0
Tail Ratio 1.0
Outlier Win Ratio 4.17
Outlier Loss Ratio 4.9
MTD 2.21%
3M -1.41%
6M 5.8%
YTD 6.31%
1Y 6.31%
3Y (ann.) 11.06%
5Y (ann.) 11.06%
10Y (ann.) 11.06%
All-time (ann.) 11.06%
Best Day 1.49%
Worst Day -1.58%
Best Month 3.11%
Worst Month -3.89%
Best Year 6.31%
Worst Year 6.31%
Avg. Drawdown -1.14%
Avg. Drawdown Days 13
Recovery Factor 1.44
Ulcer Index 0.02
Serenity Index 0.77
Avg. Up Month 1.7%
Avg. Down Month -3.89%
Win Days 32.52%
Win Month 85.71%
Win Quarter 66.67%
Win Year 100.0%

Position and trade timeline#

Display all positions and how much profit they made.

[14]:

from tradeexecutor.analysis.trade_analyser import expand_timeline

timeline = analysis.create_timeline()

expanded_timeline, apply_styles = expand_timeline(
        universe.universe.exchanges,
        universe.universe.pairs,
        timeline)

# Do not truncate the row output
with pd.option_context("display.max_row", None):
    display(apply_styles(expanded_timeline))
Remarks Type Opened at Duration Exchange Base asset Quote asset Position max value PnL USD PnL % Open mid price USD Close mid price USD Trade count LP fees
SL Long 2021-06-01 1 days WETH USDC $1,000.00 $-53.31 -5.33% $1,038.733888 $983.362118 2 $0.97
Long 2021-06-02 1 days WETH USDC $994.67 $135.96 13.67% $984.345972 $1,118.896189 2 $1.06
SL Long 2021-06-04 1 days WETH USDC $1,008.27 $-77.09 -7.65% $1,089.178511 $1,005.900911 2 $0.97
SL Long 2021-06-05 1 days WETH USDC $1,000.56 $-94.01 -9.40% $1,006.907315 $912.303452 2 $0.95
Long 2021-06-11 1 days WETH USDC $991.16 $104.67 10.56% $971.047911 $1,073.593633 2 $1.04
Long 2021-06-13 1 days WETH USDC $1,001.62 $91.32 9.12% $1,037.029583 $1,131.578690 2 $1.05
Long 2021-06-15 1 days WETH USDC $1,010.75 $142.43 14.09% $1,148.099988 $1,309.883866 2 $1.08
Long 2021-06-17 1 days WETH USDC $1,025.00 $-45.01 -4.39% $1,339.743679 $1,280.914277 2 $1.00
Long 2021-06-19 1 days WETH USDC $1,020.50 $-31.79 -3.11% $1,260.006535 $1,220.759206 2 $1.00
SL Long 2021-06-21 1 days WETH USDC $1,017.32 $-91.29 -8.97% $1,219.157483 $1,109.754553 2 $0.97
Long 2021-06-23 1 days WETH USDC $1,008.19 $2.30 0.23% $1,143.579011 $1,146.192404 2 $1.01
Long 2021-06-25 1 days WETH USDC $1,008.42 $146.35 14.51% $1,262.506834 $1,445.736726 2 $1.08
SL Long 2021-06-27 1 days WETH USDC $1,023.05 $-153.25 -14.98% $1,499.296416 $1,274.709522 2 $0.95
Long 2021-06-28 1 days WETH USDC $1,007.73 $-29.62 -2.94% $1,275.984870 $1,238.476696 2 $0.99
Long 2021-06-30 1 days WETH USDC $1,004.77 $-3.20 -0.32% $1,326.680911 $1,322.453181 2 $1.00
Long 2021-07-02 1 days WETH USDC $1,004.45 $37.91 3.77% $1,382.848000 $1,435.037168 2 $1.02
Long 2021-07-04 1 days WETH USDC $1,008.24 $94.27 9.35% $1,558.202663 $1,703.900389 2 $1.06
Long 2021-07-06 1 days WETH USDC $1,017.67 $12.48 1.23% $1,585.146741 $1,604.582636 2 $1.02
Long 2021-07-08 1 days WETH USDC $1,018.91 $2.12 0.21% $1,598.740005 $1,602.068093 2 $1.02
Long 2021-07-10 1 days WETH USDC $1,019.13 $1.17 0.11% $1,533.996581 $1,535.756736 2 $1.02
SL Long 2021-07-12 1 days WETH USDC $1,019.24 $-89.94 -8.82% $1,492.061664 $1,360.394095 2 $0.97
Long 2021-07-17 1 days WETH USDC $1,010.25 $32.19 3.19% $1,454.391535 $1,500.739380 2 $1.03
Long 2021-07-22 1 days WETH USDC $1,013.47 $-21.74 -2.14% $1,528.614795 $1,495.826996 2 $1.00
Long 2021-07-25 1 days WETH USDC $1,011.29 $68.17 6.74% $1,550.752417 $1,655.283075 2 $1.05
Long 2021-07-27 1 days WETH USDC $1,018.11 $64.81 6.37% $1,670.157745 $1,776.481218 2 $1.05
SL Long 2021-07-29 1 days WETH USDC $1,024.59 $-60.75 -5.93% $1,639.817745 $1,542.588992 2 $0.99
Long 2021-07-30 1 days WETH USDC $1,018.52 $-3.35 -0.33% $1,544.132353 $1,539.054073 2 $1.02
Long 2021-08-10 2 days WETH USDC $1,018.18 $135.22 13.28% $1,380.498600 $1,563.831445 2 $1.09
Long 2021-08-13 1 days WETH USDC $1,031.70 $126.88 12.30% $1,743.865378 $1,958.326698 2 $1.10
SL Long 2021-08-15 1 days WETH USDC $1,044.39 $-116.86 -11.19% $2,057.209139 $1,827.017945 2 $0.99
Long 2021-08-16 1 days WETH USDC $1,032.71 $124.65 12.07% $1,828.845877 $2,049.594964 2 $1.10
SL Long 2021-08-18 1 days WETH USDC $1,045.17 $-120.88 -11.57% $2,261.297524 $1,999.769933 2 $0.98
Long 2021-08-19 1 days WETH USDC $1,033.08 $41.54 4.02% $2,001.770703 $2,082.268249 2 $1.05
Long 2021-08-21 1 days WETH USDC $1,037.24 $39.71 3.83% $2,300.471368 $2,388.533659 2 $1.06
Long 2021-08-23 1 days WETH USDC $1,041.21 $27.20 2.61% $2,206.648994 $2,264.291110 2 $1.06
Long 2021-08-25 1 days WETH USDC $1,043.93 $-46.96 -4.50% $2,307.529708 $2,203.720350 2 $1.02
Long 2021-08-27 1 days WETH USDC $1,039.23 $65.47 6.30% $2,457.470282 $2,612.291900 2 $1.07
SL Long 2021-08-29 1 days WETH USDC $1,045.78 $-110.43 -10.56% $2,812.755918 $2,515.751594 2 $0.99
Long 2021-08-30 1 days WETH USDC $1,034.74 $108.18 10.45% $2,518.268604 $2,781.538957 2 $1.09
Long 2021-09-01 1 days WETH USDC $1,045.55 $150.83 14.43% $3,045.816324 $3,485.210075 2 $1.12
Long 2021-09-03 1 days WETH USDC $1,060.64 $64.95 6.12% $3,429.406388 $3,639.397371 2 $1.09
SL Long 2021-09-05 1 days WETH USDC $1,067.13 $-133.54 -12.51% $3,669.825369 $3,210.584472 2 $1.00
Long 2021-09-06 1 days WETH USDC $1,053.78 $72.41 6.87% $3,213.796662 $3,434.625003 2 $1.09
SL Long 2021-09-08 1 days WETH USDC $1,061.02 $-63.47 -5.98% $3,741.888557 $3,518.058846 2 $1.03
Long 2021-09-09 1 days WETH USDC $1,054.67 $17.02 1.61% $3,521.578664 $3,578.404981 2 $1.06
SL Long 2021-09-11 1 days WETH USDC $1,056.37 $-126.21 -11.95% $3,886.763491 $3,422.394029 2 $0.99
Long 2021-09-12 1 days WETH USDC $1,043.75 $151.62 14.53% $3,425.818135 $3,923.463888 2 $1.12
Long 2021-09-14 1 days WETH USDC $1,058.91 $104.18 9.84% $4,164.548167 $4,574.282168 2 $1.11
Long 2021-09-16 1 days WETH USDC $1,069.33 $-50.62 -4.73% $4,400.766595 $4,192.437159 2 $1.04
SL Long 2021-09-18 1 days WETH USDC $1,064.27 $-65.39 -6.14% $4,593.148773 $4,310.933162 2 $1.03
Long 2021-09-19 1 days WETH USDC $1,057.73 $77.72 7.35% $4,315.246252 $4,632.331047 2 $1.10
SL Long 2021-09-21 1 days WETH USDC $1,065.50 $-56.13 -5.27% $4,148.354476 $3,929.825552 2 $1.04
Long 2021-09-22 1 days WETH USDC $1,059.89 $-7.51 -0.71% $3,933.757343 $3,905.890370 2 $1.06
Long 2021-09-24 1 days WETH USDC $1,059.14 $132.99 12.56% $4,460.789041 $5,020.888717 2 $1.13
Long 2021-09-26 1 days WETH USDC $1,072.44 $90.19 8.41% $5,061.664027 $5,487.349581 2 $1.12
Long 2021-09-28 1 days WETH USDC $1,081.46 $-32.66 -3.02% $5,264.740330 $5,105.756204 2 $1.07
Long 2021-09-30 1 days WETH USDC $1,078.19 $59.43 5.51% $5,160.125680 $5,444.536400 2 $1.11
SL Long 2021-10-02 1 days WETH USDC $1,084.13 $-147.26 -13.58% $4,934.306769 $4,264.064638 2 $1.01
Long 2021-10-13 1 days WETH USDC $1,069.41 $78.03 7.30% $4,262.432327 $4,573.425127 2 $1.11
Long 2021-10-15 1 days WETH USDC $1,077.21 $94.56 8.78% $4,560.250759 $4,960.555216 2 $1.12
SL Long 2021-10-17 1 days WETH USDC $1,086.67 $-66.43 -6.11% $5,488.598251 $5,153.063478 2 $1.05
SL Long 2021-10-18 1 days WETH USDC $1,080.02 $-124.06 -11.49% $5,158.219119 $4,565.683263 2 $1.02
Long 2021-10-19 1 days WETH USDC $1,067.62 $77.92 7.30% $4,570.251230 $4,903.809236 2 $1.11
Long 2021-10-21 1 days WETH USDC $1,075.41 $12.47 1.16% $5,014.696964 $5,072.867776 2 $1.08
SL Long 2021-10-23 1 days WETH USDC $1,076.66 $-64.99 -6.04% $4,938.250137 $4,640.142854 2 $1.04
Long 2021-10-24 1 days WETH USDC $1,070.16 $14.96 1.40% $4,644.785318 $4,709.737295 2 $1.08
Long 2021-10-26 1 days WETH USDC $1,071.65 $20.10 1.88% $4,907.738626 $4,999.774878 2 $1.08
Long 2021-10-28 1 days WETH USDC $1,073.66 $127.23 11.85% $5,660.187394 $6,330.949614 2 $1.14
Long 2021-10-30 1 days WETH USDC $1,086.39 $-42.56 -3.92% $6,115.636331 $5,876.078293 2 $1.07
Long 2021-11-01 1 days WETH USDC $1,082.13 $-48.27 -4.46% $5,593.520191 $5,344.010907 2 $1.06
SL Long 2021-11-08 1 days WETH USDC $1,077.30 $-120.71 -11.20% $5,519.988238 $4,901.488134 2 $1.02
Long 2021-11-12 1 days WETH USDC $1,065.23 $157.21 14.76% $5,259.303669 $6,035.509288 2 $1.14
SL Long 2021-11-14 1 days WETH USDC $1,080.95 $-129.64 -11.99% $5,763.701634 $5,072.463456 2 $1.02
SL Long 2021-11-16 1 days WETH USDC $1,067.99 $-71.88 -6.73% $5,339.000452 $4,979.647570 2 $1.03
SL Long 2021-11-18 1 days WETH USDC $1,060.80 $-132.30 -12.47% $5,519.750911 $4,831.339210 2 $0.99
SL Long 2021-11-25 2 days WETH USDC $1,047.57 $-85.83 -8.19% $5,009.159746 $4,598.729368 2 $1.00
Long 2021-11-28 1 days WETH USDC $1,038.99 $11.58 1.11% $5,191.377565 $5,249.256949 2 $1.05
Long 2021-11-30 1 days WETH USDC $1,040.15 $63.26 6.08% $5,794.229383 $6,146.617706 2 $1.07
Long 2021-12-02 1 days WETH USDC $1,046.47 $143.25 13.69% $5,590.362475 $6,355.593756 2 $1.12
Long 2021-12-04 1 days WETH USDC $1,060.80 $61.07 5.76% $5,691.761715 $6,019.429342 2 $1.09
Long 2021-12-06 1 days WETH USDC $1,066.90 $32.74 3.07% $5,786.273400 $5,963.845120 2 $1.08
SL Long 2021-12-08 1 days WETH USDC $1,070.18 $-56.87 -5.31% $6,317.561909 $5,981.812956 2 $1.04
Long 2021-12-09 1 days WETH USDC $1,064.49 $98.46 9.25% $5,987.797762 $6,541.628419 2 $1.11
SL Long 2021-12-11 1 days WETH USDC $1,074.34 $-116.95 -10.89% $6,535.857547 $5,824.387220 2 $1.02
Long 2021-12-12 1 days WETH USDC $1,062.64 $11.30 1.06% $5,830.214520 $5,892.227701 2 $1.07
Long 2021-12-14 1 days WETH USDC $1,063.77 $20.48 1.93% $6,225.055971 $6,344.905057 2 $1.07
Long 2021-12-18 1 days WETH USDC $1,065.82 $31.24 2.93% $5,896.996175 $6,069.848152 2 $1.08
SL Long 2021-12-21 1 days WETH USDC $1,068.94 $-136.46 -12.77% $6,232.739865 $5,437.066801 2 $1.00
Long 2021-12-29 2 days WETH USDC $1,055.30 $77.03 7.30% $5,610.205605 $6,019.736085 2 $1.09

Finishing notes#

Print out a line to signal the notebook finished the execution successfully.

[15]:

print("All ok")

All ok