convert-to-optimiser

name	convert-to-optimiser
description	Convert a backtesting notebook into a parameter optimisation notebook using the bundled transformation mapping to choose searchable parameters and rewrite the notebook.

Convert backtest to optimiser

Convert a single backtesting notebook into an optimiser notebook that optimises strategy parameters using scikit-optimize's Gaussian Process.

Input

• Path to a backtesting notebook (.ipynb)

Reference files

• Reference optimiser notebook: getting-started/scratchpad/vault-of-vaults/32-waterfall-diversified-larger-universe-grid-search-4d-rebalance-profit.ipynb
• Reference backtest notebook: getting-started/scratchpad/bnb-ath-2/11-bnb-ath-2h-rerun.ipynb

Steps

1. Read the input backtest notebook and the cell-by-cell mapping section below.

2. Read the reference optimiser notebook (getting-started/scratchpad/vault-of-vaults/32-waterfall-diversified-larger-universe-grid-search-4d-rebalance-profit.ipynb) — you will copy output cells from this.

3. Identify tunable parameters: Look at the Parameters class in the backtest notebook. Identify strategy-specific parameters that could be searched (signal or indicator params, portfolio sizing, risk params). Exclude structural parameters like chain_id, exchanges, candle_time_bucket, cycle_duration, routing, backtest_start/end, initial_cash, min_volume, min_tvl, rolling window calculations, and yield settings.

4. Ask the user which parameters to make searchable and what value ranges to use. Suggest ranges centred on the current fixed values. The original value must be included in every range.

5. Create the optimiser notebook with these cells in order:

   a. Title cell (markdown): Change title to # {Strategy name} parameter search

   b. Setup cell (code): Copy as-is from backtest

   c. Chain config cell (code): Copy as-is from backtest

   d. Parameters cell (code): Transform from the backtest version:

   • Add from skopt.space import Categorical to imports
   • Replace chosen tunable parameters with Categorical([...]) ranges
   • Set use_managed_yield = False
   • Add at the end:

     from tradeexecutor.strategy.parameters import display_parameters
     display_parameters(parameters)
     

   e. Trading universe cell (code): Copy as-is from backtest (the create_trading_universe() function)

   f. Token map cell (code): Copy as-is from backtest

   g. Indicators cell (code): Copy from backtest but remove the calculate_and_load_indicators_inline() call at the end of the cell (and its preceding comment). Keep display_indicators(indicators) if present. The optimiser calculates indicators itself for each parameter combination — calling calculate_and_load_indicators_inline() with Categorical parameter values will crash with AssertionError: Detected scikit-optimize Dimension as a parameter value.

   h. Backtest time range cell (markdown + code): Copy from backtest, but remove any reference to indicator_data since that variable no longer exists (the inline indicator calculation was removed in step g). Simplify to use Parameters.backtest_start and Parameters.backtest_end directly. Example:

   backtest_start = Parameters.backtest_start
   backtest_end = Parameters.backtest_end
   print(f"Time range is {backtest_start} - {backtest_end}")
   

   i. Strategy cell (code): Copy decide_trades() (and create_yield_rules() if present) from the backtest. : If is in the same cell as , remove the call and everything after it. The strategy function must be in its own cell without the backtest execution.

6. Write the output notebook as {original-notebook-name}-optimiser.ipynb in the same directory as the input notebook.

7. Verify the notebook structure:

   • Parameters class has Categorical values for the chosen searchable params
   • create_trading_universe() and decide_trades() are unchanged from the backtest
   • Optimiser cell uses perform_optimisation with prepare_optimiser_parameters(Parameters)
   • All output cells match the reference optimiser structure
   • Test run with the observable runner: poetry run jupyter-execute-agent {output-notebook}.ipynb (omit --timeout for long optimisers). See notebook-execution.md.

Cell-by-cell mapping

Reference notebooks (in getting-started/scratchpad/bnb-ath-2/):

• Backtest: 11-bnb-ath-2h-rerun.ipynb
• Optimiser: 10-bnb-ath-optimise-sharpe.ipynb

What stays the same

These cells are copied verbatim from the backtest notebook:

• Setup cell: Client.create_jupyter_client() + setup_charting_and_output()
• Chain config cell: Exchange/pair/vault configuration
• Trading universe cell: create_trading_universe() function
• Token map cell: token_map, benchmark_pair_ids, category_pair_ids
• Indicators cell: All indicator functions (local_high, full_history_ath, volatility, signal, etc.) — but see "Remove inline indicator calculation" below
• Backtest time range cell: Start/end date calculation — but see "Fix indicator_data references" below
• decide_trades() function: Core strategy logic (but separated from run_backtest_inline call) — but see "numpy.float64 cast" below

What changes

1. Title cell

• Backtest: # BNB local high strategy with description
• Optimiser: # {Strategy name} parameter search

2. Parameters cell

Add import:

from skopt.space import Categorical

Wrap tunable strategy parameters in Categorical([...]). The original value must be included in the list. Example:

# Backtest (fixed)
local_high_delay_bars = 12
local_high_window_bars = 2160

# Optimiser (searchable)
local_high_delay_bars = Categorical([12, 60, 144, 200, 360, 500])
local_high_window_bars = Categorical([10, 25, 50, 100, 150, 360, 800, 720, 2160, 4320])

Other changes:

• Set use_managed_yield = False
• Add at end: display_parameters(parameters) (with import from tradeexecutor.strategy.parameters import display_parameters)

Parameters that are not searchable (structural):

• id, candle_time_bucket, cycle_duration, chain_id, exchanges
• rolling_volume_bars, rolling_volatility_bars, tvl_ewm_span
• min_volume, min_tvl, min_tvl_prefilter, min_token_sniffer_score
• routing, required_history_period, slippage_tolerance
• backtest_start, backtest_end, initial_cash
• yield_flow_dust_threshold, directional_trade_yield_buffer_pct
• assummed_liquidity_when_data_missings

Parameters typically made searchable:

• Signal/indicator parameters (delay, window, threshold, span)
• Portfolio sizing parameters (max_assets, allocation, max_concentration)
• Risk parameters (stop_loss, per_position_cap)
• Filter thresholds (min_from_full_history_ath)

3. Remove inline indicator calculation

The backtest indicators cell typically ends with:

indicator_data = calculate_and_load_indicators_inline(
    strategy_universe=strategy_universe,
    create_indicators=indicators.create_indicators,
    parameters=parameters,
)

Remove this call (and any preceding comment like # Calculate all indicators...). The optimiser calculates indicators for each parameter combination internally. Passing Categorical parameter values to calculate_and_load_indicators_inline() causes:

AssertionError: Detected scikit-optimize Dimension as a parameter value: rolling_returns_bars: Categorical(...)

Keep display_indicators(indicators) if present — it only describes the indicator definitions, it does not calculate them.

4. Fix indicator_data references in time range cell

The backtest time range cell may reference indicator_data (e.g. indicator_data.get_indicator_series(...)). Since indicator_data no longer exists after step 3, simplify the cell to use static Parameters values:

backtest_start = Parameters.backtest_start
backtest_end = Parameters.backtest_end
print(f"Time range is {backtest_start} - {backtest_end}")

5. Remove charts cell and pre-backtest visualisations

The backtest notebook has:

• Charts cell: ChartRegistry setup with 20+ chart registrations
• Pre-backtest visualisation cells: available pairs, inclusion criteria, signals, volatility, price vs signal, local high

All of these are removed from the optimiser notebook.

6. Replace run_backtest_inline with perform_optimisation

The backtest runs a single backtest:

result = run_backtest_inline(
    name=parameters.id,
    engine_version="0.5",
    decide_trades=decide_trades,
    ...
)
state = result.state

The optimiser replaces this with:

from tradeexecutor.backtest.optimiser import perform_optimisation, prepare_optimiser_parameters, MinTradeCountFilter
from tradeexecutor.backtest.optimiser_functions import optimise_sharpe

iterations = 4
search_func = optimise_sharpe

optimiser_result = perform_optimisation(
    iterations=iterations,
    search_func=search_func,
    decide_trades=decide_trades,
    strategy_universe=strategy_universe,
    parameters=prepare_optimiser_parameters(Parameters),
    create_indicators=indicators.create_indicators,
    result_filter=MinTradeCountFilter(50),
    timeout=70*60,
    batch_size=5,
    ignore_wallet_errors=True,
)

7. Cast numpy.float64 to float in decide_trades

scikit-optimize's Categorical returns numpy.float64 for float values, not native Python float. The alpha_model.normalise_weights() waterfall code path has a strict type check:

assert type(max_weight) == float, f"Got {type(max_weight)} instead of float"

If decide_trades() passes a Categorical float parameter (e.g. parameters.max_concentration) as max_weight, wrap it with float():

# Before (crashes with numpy.float64)
max_weight = parameters.max_concentration

# After
max_weight = float(parameters.max_concentration)

Apply the same float() cast to any parameter value passed to functions that assert native Python types. Common cases: max_weight, per_position_cap, allocation.

8. Replace backtest output cells with optimiser analysis cells

All backtest output cells (performance metrics, equity curves, weights, positions, trading metrics, vault performance) are removed.

Replaced with these heading + code cell pairs (copied from reference optimiser notebook cells 18-48):

#	Heading	Description
1	# Optimiser	perform_optimisation() call
2	# Results	analyse_optimiser_result() + render_grid_search_result_table()
3	# Parameter analysis	Section heading only
4	## Decision tree visualisation	sklearn DecisionTreeRegressor on parameter combinations
5	## Feature importance analysis	sklearn RandomForestRegressor feature importances
6	## Heatmaps for parameter pairs	plotly go.Heatmap for parameter pair interactions
7	## Cluster analysis	sklearn KMeans + PCA 3D scatter
8	## Parallel coordinates plot	plotly px.parallel_coordinates
9	# The best candidate equity curve	visualise_single_grid_search_result_benchmark()
10	# Portfolio performance (best pick)	compare_strategy_backtest_to_multiple_assets()
11	# Trade summary (best pick)	best_pick.summary.to_dataframe()
12	# Trading pair performance breakdown	analyse_multipair()
13	# Best positions	Top 5 positions sorted by profit
14	# Rolling sharpe	calculate_rolling_sharpe() with 180-day window
15	# Data diagnostics	Commented-out debug cells