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ETH-MATIC-USDC momentum beta

MATIC and ETH momentum strategy to maximize gains in bull market and avoid losses in bear market, on Polygon

Source code

The source code of the ETH-MATIC-USDC momentum strategy

"""MATIC-ETH-USDC rebalance strategy.

- See for the strategy development information

To backtest this strategy module locally:

.. code-block:: console

    trade-executor \
        backtest \
        --strategy-file=strategies/ \

To see the backtest for longer history, refer to the notebook doing backtest with Binance data.
import datetime

import pandas_ta
import pandas as pd

from import TradeExecution
from tradeexecutor.state.visualisation import PlotKind, PlotLabel, PlotShape
from tradeexecutor.strategy.alpha_model import AlphaModel
from tradeexecutor.strategy.cycle import CycleDuration
from tradeexecutor.strategy.default_routing_options import TradeRouting
from tradeexecutor.strategy.execution_context import ExecutionContext
from tradeexecutor.strategy.pandas_trader.indicator import IndicatorSet, IndicatorSource
from tradeexecutor.strategy.pandas_trader.strategy_input import StrategyInput
from tradeexecutor.strategy.parameters import StrategyParameters
from tradeexecutor.strategy.tag import StrategyTag
from tradeexecutor.strategy.trading_strategy_universe import TradingStrategyUniverse, load_partial_data
from tradeexecutor.strategy.universe_model import UniverseOptions
from tradeexecutor.strategy.weighting import weight_passthrouh
from tradingstrategy.chain import ChainId
from tradingstrategy.client import Client
from tradingstrategy.timebucket import TimeBucket
from tradingstrategy.utils.groupeduniverse import resample_price_series

# The pairs we are rading
pair_ids = [
    (ChainId.polygon, "uniswap-v3", "WETH", "USDC", 0.0005),
    (ChainId.polygon, "uniswap-v3", "WMATIC", "USDC", 0.0005),

# See v37-matic-eth-robustness search for parameters details
class Parameters:
    """Parameteres for this strategy.

    - Collect parameters used for this strategy here

    - Both live trading and backtesting parameters

    cycle_duration = CycleDuration.cycle_8h  # Run decide_trades() every 8h
    source_time_bucket = TimeBucket.h1  # Use 1h candles as the raw data
    target_time_bucket = TimeBucket.h8  # Create synthetic 8h candles
    clock_shift_bars = 0  # Do not do shifted candles

    rsi_bars = 12  # Number of bars to calculate RSI for each tradingbar
    matic_eth_rsi_bars = 5  # Number of bars for the momentum factor
    rsi_entry = 80  # Single pair entry level - when RSI crosses above this value open a position
    rsi_exit = 55  # Single pair exit level - when RSI crosses below this value exit a position
    allocation = 0.98  # How much cash allocate for volatile positions
    rebalance_threshold = 0.10  # How much position mix % must change when we rebalance between two open positions
    initial_cash = 10_000  # Backtesting start cash
    trailing_stop_loss = 0.990000  # Trailing stop loss as 1 - x
    trailing_stop_loss_activation_level = 1.07  # How much above opening price we must be before starting to use trailing stop loss
    stop_loss = 0.80  # Hard stop loss when opening a new position
    momentum_exponent = 2  # How much momentum we capture when rebalancing between open positions

    # Live trading only
    chain_id = ChainId.polygon
    routing = TradeRouting.default  # Pick default routes for trade execution
    required_history_period = datetime.timedelta(days=60)  # How much data a live trade execution needs to load to be able to calculate indicators

    # Backtesting only

    backtest_start = datetime.datetime(2022, 1, 1)
    backtest_end = datetime.datetime(2024, 3, 15)
    stop_loss_time_bucket = TimeBucket.h1  # use 1h close as the stop loss signal

def calculate_matic_eth(strategy_universe: TradingStrategyUniverse):
    """Calculate MATIC/ETH price used as a rebalance factor."""
    eth = strategy_universe.get_pair_by_human_description(pair_ids[0])
    matic = strategy_universe.get_pair_by_human_description(pair_ids[1])
    matic_price = strategy_universe.data_universe.candles.get_candles_by_pair(matic.internal_id)
    eth_price = strategy_universe.data_universe.candles.get_candles_by_pair(eth.internal_id)
    series = matic_price["close"] / eth_price["close"]  # Divide two series
    return series

def calculate_matic_eth_rsi(strategy_universe: TradingStrategyUniverse, length: int):
    """Calculate x hours RSI for MATIC/ETH price used as the rebalancing factor."""
    matic_eth_series = calculate_matic_eth(strategy_universe)
    return pandas_ta.rsi(matic_eth_series, length=length)

def calculate_resampled_rsi(pair_close_price_series: pd.Series, length: int, upsample: TimeBucket, shift: int):
    """Calculate x hours RSI for a particular trading pair"""
    resampled_close = resample_price_series(pair_close_price_series, upsample.to_pandas_timedelta(), shift=shift)
    return pandas_ta.rsi(resampled_close, length=length)

def calculate_resampled_matic_eth(strategy_universe: TradingStrategyUniverse, upsample: TimeBucket, shift: int):
    """Caclulate MATIC/ETH price series for x hours."""
    eth = strategy_universe.get_pair_by_human_description(pair_ids[0])
    matic = strategy_universe.get_pair_by_human_description(pair_ids[1])
    eth_price = strategy_universe.data_universe.candles.get_candles_by_pair(eth.internal_id)
    matic_price = strategy_universe.data_universe.candles.get_candles_by_pair(matic.internal_id)
    resampled_eth = resample_price_series(eth_price["close"], upsample.to_pandas_timedelta(), shift=shift)
    resampled_matic = resample_price_series(matic_price["close"], upsample.to_pandas_timedelta(), shift=shift)
    series = resampled_matic / resampled_eth
    return series

def calculate_resampled_matic_eth_rsi(strategy_universe: TradingStrategyUniverse, length: int, upsample: TimeBucket, shift: int):
    """Caclulate RSI for MATIC/ETH price series for x hours."""
    matic_eth = calculate_resampled_matic_eth(strategy_universe, upsample, shift)
    return pandas_ta.rsi(matic_eth, length=length)

def create_indicators(
    timestamp: datetime.datetime,
    parameters: StrategyParameters,
    strategy_universe: TradingStrategyUniverse,
    execution_context: ExecutionContext
    """Create indicators for this trading strategy.

    - Because we use non-standard candle time bucket, we do upsamplign from 1h candles
    indicators = IndicatorSet()
        "rsi", calculate_resampled_rsi,
        {"length": parameters.rsi_bars, "upsample": parameters.target_time_bucket, "shift": parameters.clock_shift_bars}
        {"upsample": parameters.target_time_bucket, "shift": parameters.clock_shift_bars},
        {"length": parameters.matic_eth_rsi_bars, "upsample": parameters.target_time_bucket, "shift": parameters.clock_shift_bars},
    return indicators

def decide_trades(
    input: StrategyInput,
) -> list[TradeExecution]:
    """Strategy entry/exit logic.

    - Indicator reading for the input]
    - The trade logic
    - Visualisation for the strategy diagnostics

    # Resolve our pair metadata for our two pair strategy
    parameters = input.parameters
    position_manager = input.get_position_manager()
    state = input.state
    timestamp = input.timestamp
    indicators = input.indicators
    mode = input.execution_context.mode
    clock_shift = parameters.clock_shift_bars * parameters.source_time_bucket.to_pandas_timedelta()

    alpha_model = AlphaModel(input.timestamp)
    eth_pair = position_manager.get_trading_pair(pair_ids[0])
    matic_pair = position_manager.get_trading_pair(pair_ids[1])
    position_manager.log("decide_trades() start")

    # Indicators
    # Calculate indicators for each pair.

    # Per-trading pair calcualted data
    current_rsi_values = {}  # RSI yesterday
    previous_rsi_values = {}  # RSI day before yesterday
    current_price = {}  # Close price yesterday
    momentum = {matic_pair: 0, eth_pair: 0}

    for pair in [matic_pair, eth_pair]:
        current_price[pair] = indicators.get_price(pair)

        current_rsi_values[pair] = indicators.get_indicator_value("rsi", index=-1, pair=pair, clock_shift=clock_shift)
        previous_rsi_values[pair] = indicators.get_indicator_value("rsi", index=-2, pair=pair, clock_shift=clock_shift)

    matic_eth_yesterday = indicators.get_indicator_value("matic_eth", clock_shift=clock_shift)
    matic_eth_rsi_yesterday = indicators.get_indicator_value("matic_eth_rsi", clock_shift=clock_shift)
    if matic_eth_rsi_yesterday is not None:
        matic_momentum = (matic_eth_rsi_yesterday / 100) + 0.5
        eth_momentum = (1 - (matic_eth_rsi_yesterday / 100)) + 0.5
        momentum[eth_pair] = eth_momentum ** parameters.momentum_exponent
        momentum[matic_pair] = matic_momentum ** parameters.momentum_exponent

    # Trading logic

    for pair in [matic_pair, eth_pair]:
        existing_position = position_manager.get_current_position_for_pair(pair)
        pair_open = existing_position is not None
        closed_positions = position_manager.get_closed_positions_for_pair(pair)
        has_no_position = not pair_open and len(closed_positions) == 0 and mode.is_live_trading()
        pair_momentum = momentum.get(pair, 0)
        signal_strength = max(pair_momentum, 0.1)  # Singal strength must be positive, as we do long-only
        if pd.isna(signal_strength):
            signal_strength = 0
        alpha_model.set_signal(pair, 0)

        if pair_open:
            # We have existing open position for this pair,
            # keep it open by default unless we get a trigger condition below
            position_manager.log(f"Pair {pair} already open")
            alpha_model.set_signal(pair, signal_strength, stop_loss=parameters.stop_loss)

        if current_rsi_values[pair] and previous_rsi_values[pair]:

            # Check for RSI crossing our threshold values in this cycle, compared to the previous cycle
            rsi_cross_above = current_rsi_values[pair] >= parameters.rsi_entry and previous_rsi_values[pair] < parameters.rsi_entry
            rsi_cross_below = current_rsi_values[pair] < parameters.rsi_exit and previous_rsi_values[pair] > parameters.rsi_exit

            if not pair_open:
                # Check for opening a position if no position is open
                if rsi_cross_above or has_no_position:
                    position_manager.log(f"Pair {pair} crossed above")
                    alpha_model.set_signal(pair, signal_strength, stop_loss=parameters.stop_loss)
                # We have open position, check for the close condition
                if rsi_cross_below:
                    position_manager.log(f"Pair {pair} crossed below")
                    alpha_model.set_signal(pair, 0)

    # Enable trailing stop loss if we have reached the activation level
    if parameters.trailing_stop_loss_activation_level is not None:
       for p in state.portfolio.open_positions.values():
           if p.trailing_stop_loss_pct is None:
               if current_price[p.pair] >= p.get_opening_price() * parameters.trailing_stop_loss_activation_level:
                   p.trailing_stop_loss_pct = parameters.trailing_stop_loss

    # Use alpha model and construct a portfolio of two assets
    portfolio = position_manager.get_current_portfolio()
    portfolio_target_value = portfolio.get_total_equity() * parameters.allocation
    alpha_model.calculate_target_positions(position_manager, portfolio_target_value)
    trades = alpha_model.generate_rebalance_trades_and_triggers(
        min_trade_threshold=parameters.rebalance_threshold * portfolio.get_total_equity(),

    # Visualisations

    if input.is_visualisation_enabled():

        visualisation = state.visualisation  # Helper class to visualise strategy output

        if current_rsi_values[matic_pair]:
                f"RSI MATIC",

            # Low (vertical line)
                f"RSI low trigger",
                detached_overlay_name=f"RSI MATIC",

            # High (vertical line)
                f"RSI high trigger",
                detached_overlay_name=f"RSI MATIC",

        # ETH RSI daily
        if current_rsi_values[eth_pair]:
                f"RSI ETH",
                detached_overlay_name=f"RSI MATIC",

        # comment out these 2 plots until we can control what price charts are shown
        # if matic_eth_yesterday is not None:
        #     visualisation.plot_indicator(
        #         timestamp,
        #         f"MATIC/ETH",
        #         PlotKind.technical_indicator_detached,
        #         matic_eth_yesterday,
        #         colour="grey",
        #         pair=matic_pair,
        #     )

        # if matic_eth_rsi_yesterday is not None:
        #     visualisation.plot_indicator(
        #         timestamp,
        #         f"MATIC/ETH RSI",
        #         PlotKind.technical_indicator_detached,
        #         matic_eth_rsi_yesterday,
        #         colour="grey",
        #         pair=matic_pair,
        #     )

        state.visualisation.add_calculations(timestamp, alpha_model.to_dict())  # Record alpha model thinking

        f"MATIC RSI: {current_rsi_values[matic_pair]}, MATIC RSI yesterday: {previous_rsi_values[matic_pair]}",

    return trades

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

    # Load data for our trading pair whitelist
    if execution_context.mode.is_backtesting():
        # For backtesting, we use a specific time range from the strategy parameters
        start_at = universe_options.start_at
        end_at = universe_options.end_at
        required_history_period = None
        stop_loss_time_bucket = Parameters.stop_loss_time_bucket
        start_at = None
        end_at = None
        required_history_period = Parameters.required_history_period  # We need 21 days run up for RSI indicator
        stop_loss_time_bucket = None

    dataset = load_partial_data(

    # Filter down the dataset to the pairs we specified
    universe = TradingStrategyUniverse.create_from_dataset(dataset)
    return universe

trading_strategy_engine_version = "0.5"

tags = {StrategyTag.beta,}

name = "ETH-MATIC-USDC momentum"

short_description = "MATIC and ETH momentum strategy to maximize gains in bull market and avoid losses in bear market, on Polygon"

icon = ""

long_description = """
# Strategy description

This strategy is a momentum and breakout strategy.

- The strategy trades ETH and MATIC over long term time horizon, doing only few trades per a month.
- The strategy delivers similar profits as buying and holding MATIC and ETH, but with much less severe drawdowns.
- The strategy performs well in long-term Bitcoin [bull market](
- In [bear]( and sideways markets the strategy does not perform well.
- It is based on [RSI technical indicator](, the strategy is buying when others are buying, and the strategy is selling when others are selling.

**Past performance is not indicative of future results**.

## Assets and trading venues

- The strategy trades only spot market
- We trade two trading asset: MATIC and ETH
- The strategy keeps reserves in USDC stablecoin
- The trading happens on QuickSwap and Uniswap on Polygon blockchain
- The strategy decision cycle is daily rebalances

## Backtesting

The backtesting was performed with Binance ETH-USDT and MATIC-USDT data of 2019-2024.

- [See backtesting results](./backtest)
- [Read more about what is backtesting](

The backtesting trading venue (Binance) is different from the live trading venue (Quickswap, Uniswap), because DEX markets
do not have long enough history to result to a meaningful backtest.

The backtesting period saw one bull market rally that is unlikely to repeat in the same magnitude 
for the assets we trade.

Past peformance is no guarantee of future results. Like with manual trading, automated trading is unlikely to be perfect.
There will be variance in the range of 30% - 50% in the results.

## Further information

- Any questions are welcome in [the Discord community chat](
- See the blog post [on how this strategy is constructed](