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

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)
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.

from typing import List, Dict

from pandas_ta.overlap import ema

from tradingstrategy.universe import Universe

from tradeexecutor.state.visualisation import PlotKind
from 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.
    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
    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 current_price >= slow_ema:
        # Entry condition:
        # Close price is higher than the slow EMA
        if not position_manager.is_any_open():
            buy_amount = cash * position_size
            trades += position_manager.open_1x_long(pair, buy_amount, stop_loss_pct=stop_loss_pct)
    elif fast_ema >= slow_ema:
        # Exit condition:
        # Fast EMA crosses slow EMA
        if position_manager.is_any_open():
            trades += position_manager.close_all()

    # Visualize strategy
    # See available Plotly colours here
    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.


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) 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, end_at, pair_id=weth_usdc.internal_id, daily_drift=(0.85, 1.15) ) candle_universe = GroupedCandleUniverse.create_from_single_pair_dataframe(candles) 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.

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",
    client=None,  # None of downloads needed, because we are using synthetic data

Our universe has synthetic candle data for the period 2021-06-01 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

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: 16
Trades made: 32
Trades w/stop loss triggered: 10
from tradeexecutor.visual.single_pair import visualise_single_pair

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