Tags: advanced, multipair, alpha-model, trend-analysis, stop-loss, sma

Portfolio construction model example 2 (Trader Joe)

This is an example notebook how to construct a momentum based portfolio construction strategy using Trading Strategy framework and backtest it for DeFi tokens.

This backtest uses alpha model approach where each trading pair has a signal and basede on the signal strenghts we construct new portfolio weightings for the upcoming week.

Some highlights of this notebook:

• Not a realistic trading strategy, but more of an code example - this may generate profits or loss but this is outside the scode of this example

• Make sure you have studied some simpler backtesting examples first

• The backtest has all its code within a single Jupyter notebook

  • The backtest code and charts are self-contained in a single file

  • The example code is easy to read

• Runs a backtest for a momentum strategy

  • Long only

  • Because the strategy is long only, it trades only in a bull market, defined by a moving average signal

  • Automatically chooses tokens that enter and exit market at Trader Joe DEX on Avalanche

  • Support pairs that trade against AVAX and USDC (quote tokens)

  • Check trading pair available liquidity before taking any positions - uses coarse (resampled) liquidity data for liquidity aware backtesting

  • Pick some top tokens for each strategy cycle

  • Based on using a trading pair momentum as an alpha signal

  • Uses take profit / stop loss to close the positions outside the rebalance cycle

  • Ignores price impact, and thus may cause unrealistic results

  • Ignores extra fees on a three leg trade of USDC->AVAX->target asset when opening a position

• Demostrates statistics and performance analyses

  • Equity curve with comparison to buy and hold AVAX

  • Bull/bear market indicator when the strategy is trading

  • Summary statistics of the strategy

  • Summary statistics of individual pairs traded

• You need a Trading Strategy API key to run the notebook

• The notebook will download more than 10GB data

• You will need a powerful computer to run this notebook (> 16GB RAM)

Strategy parameter set up

Set up the parameters used in in this strategy backtest study.

• Backtested blockchain, exchange and trading pair

• Backtesting period

• Different lookback and technical indicator periods

• Take profit and stop loss thresholds

• Minimu liquidity thresholds

• Upwards momentum thresholds

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 StrategyType, TradeRouting, ReserveCurrency

# Tell what trade execution engine version this strategy needs to use
trading_strategy_engine_version = "0.1"

# What kind of strategy we are running.
# This tells we are going to use
trading_strategy_type = StrategyType.managed_positions

# How our trades are routed.
# PancakeSwap basic routing supports two way trades with BUSD
# and three way trades with BUSD-BNB hop.
trade_routing = TradeRouting.ignore

# Set cycle to 7 days and look back the momentum of the previous candle
trading_strategy_cycle = CycleDuration.cycle_4d
momentum_lookback_period = datetime.timedelta(days=4)

# Hold N top coins for every cycle
max_assets_in_portfolio = 3

# Leave 20% cash buffer
value_allocated_to_positions = 0.50

#
# Set the take profit/stop loss for our postions.
# Aim for asymmetric opportunities - upside is higher than downside
#

# Set % stop loss over mid price
stop_loss = 0.95

# Set % take profit over mid price
take_profit = 1.33

# The momentum period price must be up % for us to take a long position
#minimum_mometum_threshold = 0.025
minimum_mometum_threshold = 0.015

# The amount of XY liquidity a pair must have on Trader Joe before
# we are happy to take any position.
minimum_liquidity_threshold = 100_000

# Don't bother with trades that would move position
# less than 300 USD
minimum_rebalance_trade_threshold = 300

# decide_trades() operates on 1d candles
candle_data_time_frame = TimeBucket.d1

# Use hourly candles to trigger the stop loss
stop_loss_data_granularity = TimeBucket.h1

# Strategy keeps its cash in USDC
reserve_currency = ReserveCurrency.usdc

# Define the periods when the native asset price is
# above its simple moving average (SMA)
bull_market_moving_average_window = pd.Timedelta(days=20)

# The duration of the backtesting period
start_at = datetime.datetime(2021, 12, 1)
end_at = datetime.datetime(2023, 2, 20)

# Start with 10,000 USD
initial_deposit = 10_000

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 tradeexecutor.state.visualisation import PlotKind
from tradeexecutor.strategy.trading_strategy_universe import translate_trading_pair
from typing import List, Dict

from pandas_ta.overlap import sma

from tradingstrategy.universe import Universe
from tradeexecutor.strategy.weighting import weight_by_1_slash_n
from tradeexecutor.strategy.alpha_model import AlphaModel
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]:

    # 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)

    alpha_model = AlphaModel(timestamp)

    # Watch out for the inclusive range and include and avoid peeking in the future
    adjusted_timestamp = timestamp - pd.Timedelta(seconds=1)
    start = adjusted_timestamp - momentum_lookback_period - datetime.timedelta(seconds=1)
    end = adjusted_timestamp

    candle_universe = universe.candles
    pair_universe = universe.pairs

    # First figure out are we in a bear or a bull market condition.
    # Our rule for a bull market is that the price of the native token of the blockchain
    # is above its simple moving average.
    # Please see the notebook commments why we define this condition like this.
    bullish = False

    # Plot the AVAX simple moving average.
    avax = pair_universe.get_pair(
        ChainId.avalanche,
        "trader-joe",
        "WAVAX",
        "USDC"
    )

    avax_candles = candle_universe.get_last_entries_by_pair_and_timestamp(avax.pair_id, timestamp)

    if len(avax_candles) > 0:
        avax_close = avax_candles["close"]
        avax_price_now = avax_close.iloc[-1]

        # Count how many candles worth of data needed
        avax_sma = sma(avax_close, length=bull_market_moving_average_window / candle_data_time_frame.to_timedelta())
        if avax_sma is not None:
            # SMA cannot be forward filled at the beginning of the backtest period
            sma_now = avax_sma[-1]
            assert sma_now > 0, f"SMA was zero for {timestamp}, probably issue with the data?"
            state.visualisation.plot_indicator(
                timestamp,
                "Native token SMA",
                PlotKind.technical_indicator_on_price,
                sma_now,
            )

            if avax_price_now > sma_now:
                bullish = True

        # Get candle data for all candles, inclusive time range
    candle_data = candle_universe.iterate_samples_by_pair_range(start, end)

    # Because this is long only strategy, we will honour our momentum signals only in a bull market
    if bullish:

        # Iterate over all candles for all pairs in this timestamp (ts)
        for pair_id, pair_df in candle_data:

            last_candle = pair_df.iloc[-1]

            assert last_candle["timestamp"] < timestamp, "Something wrong with the data - we should not be able to peek the candle of the current timestamp, but always use the previous candle"

            open = last_candle["open"]
            close = last_candle["close"]

            # Get the pair information and translate it to a serialisable strategy object
            dex_pair = pair_universe.get_pair_by_id(pair_id)
            pair = translate_trading_pair(dex_pair)

            available_liquidity = universe.resampled_liquidity.get_liquidity_fast(pair_id, adjusted_timestamp)
            if available_liquidity < minimum_liquidity_threshold:
                # Too limited liquidity, skip this pair
                continue

            # We define momentum as how many % the trading pair price gained during
            # the momentum window
            momentum = (close - open) / open

            # This pair has not positive momentum,
            # we only buy when stuff goes up
            if momentum <= minimum_mometum_threshold:
                continue

            alpha_model.set_signal(
                pair,
                momentum,
                stop_loss=stop_loss,
                take_profit=take_profit,
            )

    # Select max_assets_in_portfolio assets in which we are going to invest
    # Calculate a weight for ecah asset in the portfolio using 1/N method based on the raw signal
    alpha_model.select_top_signals(max_assets_in_portfolio)
    alpha_model.assign_weights(method=weight_by_1_slash_n)
    alpha_model.normalise_weights()

    # Load in old weight for each trading pair signal,
    # so we can calculate the adjustment trade size
    alpha_model.update_old_weights(state.portfolio)

    # Calculate how much dollar value we want each individual position to be on this strategy cycle,
    # based on our total available equity
    portfolio = position_manager.get_current_portfolio()
    portfolio_target_value = portfolio.get_total_equity() * value_allocated_to_positions
    alpha_model.calculate_target_positions(position_manager, portfolio_target_value)

    # Shift portfolio from current positions to target positions
    # determined by the alpha signals (momentum)
    trades = alpha_model.generate_rebalance_trades_and_triggers(
        position_manager,
        min_trade_threshold=minimum_rebalance_trade_threshold,  # Don't bother with trades under 300 USD
    )

    # Record alpha model state so we can later visualise our alpha model thinking better
    state.visualisation.add_calculations(timestamp, alpha_model.to_dict())

    return trades

Set up the market data client

The Trading Strategy market data client is the Python library responsible for managing the data feeds needed to run the backtest.None

We set up the market data client with an API key.

If you do not have an API key yet, you can register one.

from tradingstrategy.client import Client

client = Client.create_jupyter_client()

Started Trading Strategy in Jupyter notebook environment, configuration is stored in /home/alex/.tradingstrategy

Setup trading universe

We setup the trading universe for the backtesting.

• Read in a handwritten allowed trading pair universe list

• Download candle data

• Print out trading pair addresses and volumes as the sanity check the pair defintions look correct

• Normal, it is more efficient to use load_partial_data instead of all load_all_data, but in this particular example, we include logic to check all pairs in the universe instead of a list of predefined pairs, so we need to use load_all_data

from tradingstrategy.client import Client

from tradeexecutor.strategy.trading_strategy_universe import TradingStrategyUniverse, load_all_data
from tradeexecutor.strategy.execution_context import ExecutionContext
from tradeexecutor.strategy.execution_context import ExecutionMode
from tradeexecutor.strategy.universe_model import UniverseOptions
from tradeexecutor.ethereum.routing_data import get_trader_joe_default_routing_parameters


def create_trading_universe(
        ts: datetime.datetime,
        client: Client,
        execution_context: ExecutionContext,
        universe_options: UniverseOptions,
) -> TradingStrategyUniverse:

    assert not execution_context.mode.is_live_trading(), \
        f"Only strategy backtesting supported, got {execution_context.mode}"

    # Load data for our trading pair whitelist
    dataset = load_all_data(
        client,
        execution_context=execution_context,
        time_frame=candle_data_time_frame,
        universe_options=universe_options,
        liquidity_time_frame=TimeBucket.d1,
        stop_loss_time_frame=stop_loss_data_granularity,
    )

    routing_parameters = get_trader_joe_default_routing_parameters(reserve_currency)

    quote_tokens = {
        "0xb97ef9ef8734c71904d8002f8b6bc66dd9c48a6e",  # USDC
        "0xb31f66aa3c1e785363f0875a1b74e27b85fd66c7",  # WAVAX
    }

    universe = TradingStrategyUniverse.create_multipair_universe(
        dataset,
        [ChainId.avalanche],
        ["trader-joe"],
        quote_tokens=quote_tokens,
        reserve_token=routing_parameters["reserve_token_address"],
        factory_router_map=routing_parameters["factory_router_map"],
        liquidity_resample_frequency="1D",
    )

    return universe

universe = create_trading_universe(
    datetime.datetime.utcnow(),
    client,
    ExecutionContext(mode=ExecutionMode.backtesting),
    UniverseOptions(),
)

print(f"The trading univers has {universe.get_pair_count()} trading pairs")

Resamping liquidity data to 1D, this may take a long time
The trading univers has 1360 trading pairs

Run 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.

• The trade execution engine will download the necessary datasets to run the backtest. The datasets may be large, several gigabytes.

import logging

from tradeexecutor.backtest.backtest_runner import run_backtest_inline

state, universe, debug_dump = run_backtest_inline(
    name="Example strategy",
    start_at=start_at,
    end_at=end_at,
    client=client,
    cycle_duration=trading_strategy_cycle,
    decide_trades=decide_trades,
    create_trading_universe=create_trading_universe,
    initial_deposit=initial_deposit,
    reserve_currency=reserve_currency,
    trade_routing=trade_routing,
    log_level=logging.WARNING,
    universe=universe,
    data_delay_tolerance=pd.Timedelta("7d"),
)

trade_count = len(list(state.portfolio.get_all_trades()))
print(f"Backtesting completed, backtested strategy made {trade_count} trades")

WARNING:tradeexecutor.backtest.backtest_execution:Fixing token sell amount to be within the epsilon. Wallet balance: 4018.74341810006636405069546, sell order quantity: -4018.743418100066364050695461, diff: 1E-24
Backtesting completed, backtested strategy made 195 trades

Examine backtest results

Examine state that contains - All actions the trade executor took - Visualisation and diagnostics data associated with the actity

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

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

Positions taken: 95
Trades made: 195

Benchmarking the strategy performance

Here we benchmark the strategy performance against some baseline scenarios.

• Buy and hold AVAX

• Buy and hold US Dollar (do nothing)

from tradeexecutor.visual.benchmark import visualise_benchmark

avax_usd = universe.get_pair_by_human_description((ChainId.avalanche, "trader-joe", "WAVAX", "USDC"))
avax_candles = universe.universe.candles.get_candles_by_pair(avax_usd.internal_id)

fig = visualise_benchmark(
    state.name,
    portfolio_statistics=state.stats.portfolio,
    all_cash=state.portfolio.get_initial_deposit(),
    buy_and_hold_asset_name="AVAX",
    buy_and_hold_price_series=avax_candles["close"],
    start_at=start_at,
    end_at=end_at
)

fig.show()