Osmosis ATOM-OSMO mean reversion#

This is an example notebook how to backtest trading strategies on Osmosis Cosmos DEX. It is based on work done in HackAtom Seoul 2022 hackathon.

Some highlights of this notebook:

Set up#

Set up strategy paramets that will decide its behavior

[1]:
import sys

import datetime
import logging

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

# Rebalance every 8h
trading_strategy_cycle = CycleDuration.cycle_8h

# How much of the cash to put on a single trade
position_size = 0.90

#
# Strategy thinking specific parameter
#

# 14 days
slow_ema_candle_count = 14*24

# 5 days
fast_ema_candle_count = 5*24

# How many candles to extract from the dataset once
batch_size = slow_ema_candle_count * 2

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

# Osmosis launched
start_at = datetime.datetime(2021, 12, 25)

# When our data ends
end_at = datetime.datetime(2022, 4, 25)

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()

    assert pair.token0_symbol == "ATOM", f"Got pair {pair}"
    assert pair.token1_symbol == "OSMO", f"Got pair {pair}"

    # 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 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)
    elif slow_ema >= fast_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
    # 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]:

from pathlib import Path import os 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, load_ohlcv_parquet_file 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.osmosis mock_exchange = generate_exchange( exchange_id=random.randint(1, 1000), chain_id=mock_chain_id, address=generate_random_ethereum_address()) # Cosmos tokens use micro token (u-token) as the smallest unit osmo = AssetIdentifier(ChainId.osmosis.value, generate_random_ethereum_address(), "OSMO", 6, 1) atom = AssetIdentifier(ChainId.osmosis.value, generate_random_ethereum_address(), "ATOM", 6, 2) atom_osmo = TradingPairIdentifier( atom, osmo, 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, [atom_osmo]) # Hack import tradeexecutor ohlcv_sample_path = os.path.join(os.path.dirname(tradeexecutor.__file__), "..", "notebooks", "pool_1_hourly_candles.parquet") ohlcv_sample_path = Path(ohlcv_sample_path) print(f"Loading data from {ohlcv_sample_path.as_posix()}") # Load candles for backtesting candles = load_ohlcv_parquet_file( ohlcv_sample_path, mock_chain_id, mock_exchange.exchange_id, atom_osmo.internal_id) # Does not work properly for Osmosis data del candles["volume"] 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 ) return TradingStrategyUniverse(universe=universe, reserve_assets=[osmo])

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()

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}")

# This function set ups trade routing for our synthetic trading universe.
# Because we have only one trading pair, there is no complicated
# routing needed
routing_model = generate_simple_routing_model(universe)

state, universe,    debug_dump = run_backtest_inline(
    name="ATOM/OSMO backtest",
    start_at=start_at,
    end_at=end_at,
    client=None,  # None of downloads needed, because we are using synthetic data
    cycle_duration=CycleDuration.cycle_24h,  # Override to use 24h cycles despite what strategy file says
    decide_trades=decide_trades,
    universe=universe,
    initial_deposit=10_000,
    reserve_currency=ReserveCurrency.busd,
    trade_routing=TradeRouting.user_supplied_routing_model,
    routing_model=routing_model,
    log_level=logging.WARNING,
)

Loading data from /Users/moo/code/executor/docs/deps/trade-executor/tradeexecutor/../notebooks/pool_1_hourly_candles.parquet
Our universe has synthetic candle data for the period 2021-12-25 00:00:00 - 2022-04-28 12:00:00

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

[5]:
print(f"Positions taken: {len(list(state.portfolio.get_all_positions()))}")
print(f"Trades made: {len(list(state.portfolio.get_all_trades()))}")
print(f"Visualisation entries: {len(list(state.visualisation.plots))}")
Positions taken: 1
Trades made: 2
Visualisation entries: 2
[6]:
from tradeexecutor.visual.single_pair import visualise_single_pair

figure = visualise_single_pair(state, universe.universe.candles)
figure.update_layout(template="plotly_dark")  # Dark color theme https://plotly.com/python/templates/
figure.show()