Convert To Optimiser | Skills Pool

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. Important: If run_backtest_inline() is in the same cell as decide_trades(), remove the run_backtest_inline() call and everything after it. The strategy function must be in its own cell without the backtest execution.

numpy.float64 fix: scikit-optimize's Categorical returns numpy.float64 instead of native Python float. If decide_trades() passes any Categorical float parameter to alpha_model.normalise_weights() as max_weight, the waterfall code path will crash with AssertionError: Got <class 'numpy.float64'> instead of float. Wrap such values with float(). For example, change:

max_weight = parameters.max_concentration

to:

max_weight = float(parameters.max_concentration)

Apply the same float() cast to any other parameter value that ends up passed to a function expecting a native Python float (common with max_weight, per_position_cap, etc.).

j. Optimiser and output cells: Copy cells from the reference optimiser notebook (32-waterfall-diversified-larger-universe-grid-search-4d-rebalance-profit.ipynb). These are heading + code cell pairs:

Heading	Cells
`# Optimiser`	markdown + code
`# Results`	markdown + code
`## Equity curves`	markdown + code
`# Parameter analysis`	markdown only
`## Decision tree visualisation`	markdown + code
`## Feature importance analysis`	markdown + code
`## Heatmaps for parameter pairs`	markdown + code
`## Cluster analysis`	markdown + code
`## Parallel coordinates plot`	markdown + code
`# The best candidate equity curve`	markdown + code
`# Portfolio performance (best pick)`	markdown + code
`# Trade summary (best pick)`	markdown + code
`# Trading pair performance breakdown`	markdown + code
`# Best positions`	markdown + code
`# Rolling sharpe`	markdown + code
`# Data diagnostics`	markdown + 3 code cells

Equity curves cell: The ## Equity curves code cell overlays all optimiser results on a single chart with buy-and-hold benchmarks. It must extract GridSearchResult instances from the optimiser results and pass them to visualise_grid_search_equity_curves. Use group_by and group_by_secondary to colour and style curves by parameter values — pick two of the searchable Categorical parameters from the strategy's Parameters class:

from tradeexecutor.visual.grid_search_basic import visualise_grid_search_equity_curves
from tradeexecutor.analysis.multi_asset_benchmark import get_benchmark_data

benchmark_indexes = get_benchmark_data(
    strategy_universe,
    cumulative_with_initial_cash=Parameters.initial_cash,
)

grid_search_results = [r.result for r in optimiser_result.results if not r.filtered]

fig = visualise_grid_search_equity_curves(
    grid_search_results,
    benchmark_indexes=benchmark_indexes,
    log_y=False,
    group_by="<primary_categorical_param>",       # colour family per value
    group_by_secondary="<secondary_categorical_param>",  # dash style per value
)
fig.show()

Do not copy any cells from the backtest that are not listed above (no chart registry, no pre-backtest visualisations, no single-backtest output cells like equity curves, weight charts, positions, trading metrics, vault performance).

from skopt.space import Categorical

# 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])

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

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

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

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

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,
)

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

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

# After
max_weight = float(parameters.max_concentration)

#	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

Convert To Optimiser

Convert backtest to optimiser

Input

Reference files

Steps

Convert To Optimiser

Convert backtest to optimiser

Input

Reference files

Steps

Cell-by-cell mapping

What stays the same

What changes

1. Title cell

2. Parameters cell

3. Remove inline indicator calculation

4. Fix indicator_data references in time range cell

5. Remove charts cell and pre-backtest visualisations

6. Replace `run_backtest_inline` with `perform_optimisation`

7. Cast numpy.float64 to float in decide_trades

8. Replace backtest output cells with optimiser analysis cells

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns

Convert To Optimiser

Convert backtest to optimiser

Input

Reference files

Steps

Convert To Optimiser

Convert backtest to optimiser

Input

Reference files

Steps

Cell-by-cell mapping

What stays the same

What changes

1. Title cell

2. Parameters cell

3. Remove inline indicator calculation

4. Fix indicator_data references in time range cell

5. Remove charts cell and pre-backtest visualisations

6. Replace run_backtest_inline with perform_optimisation

7. Cast numpy.float64 to float in decide_trades

8. Replace backtest output cells with optimiser analysis cells

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns

6. Replace `run_backtest_inline` with `perform_optimisation`