Optimizers

from qtradex.optimizers import QPSO, LSGA, IPSE, AION, MouseWheelTuner

All optimizers follow the same interface:

opt = Optimizer(data, wallet=wallet, options=options)
results = opt.optimize(bot, **kwargs)

Common interface

__init__(data, wallet=None, options=None)

Parameter	Type	Default
data	Data	required
wallet	WalletBase	varies by optimizer
options	options class instance	OptionsClass()

optimize(bot, **kwargs)

Runs the optimization. bot is a BaseBot subclass instance. Extra **kwargs pass through to backtest(). Returns a dict mapping metric names to (score_dict, bot) tuples, or None for MouseWheelTuner.

Optimizers

QPSO — Quantum Particle Swarm Optimization

Particle-swarm optimizer. Particles mutate across epochs with neuroplastic memory and cyclic simulated annealing.

from qtradex.optimizers import QPSO, QPSOoptions

opts = QPSOoptions()
opt = QPSO(data, options=opts)
best = opt.optimize(bot)

QPSOoptions parameters:

Parameter	Type	Default	Description
epochs	float	inf	Max iterations
improvements	int	100000	Stop after this many score improvements
cooldown	int	0	Iterations after an improvement before checking improvement limit
lag	float	0.5	Feed-back lag positions for score trends
top_percent	float	0.9	Top fraction of candidates to keep in plot
plot_period	int	100	Iterations between live score plots (0 = never)
fitness_ratios	list or None	None	Tradeoff between early vs late fitness scores
fitness_period	int	200	Split point for fitness ratio scoring
fitness_inversion	callable	rotation lambda	Rotates metric priorities each iteration
cyclic_amplitude	float	3	Mutation step oscillation amplitude
cyclic_freq	int	1000	Iterations per hot/cold cycle
digress	float	0.99	Best-score decay factor (multiply every digress_freq)
digress_freq	int	2500	Iterations between best-score degradation
temperature	float	2.0	Base mutation step size
synapses	int	50	Number of successful parameter sets to remember
neurons	list	[]	Active parameter indices (empty = all)
show_terminal	bool	True	Print progress to terminal
print_tune	bool	False	Print best tune on completion

---

LSGA — Local Search Genetic Algorithm

Genetic algorithm that inherits QPSO internals. Adds a population, crossover, skew-memory penalties, and a walk-forward consistency gate.

from qtradex.optimizers import LSGA, LSGAoptions

opts = LSGAoptions()
opt = LSGA(data, options=opts)
best = opt.optimize(bot)

LSGAoptions parameters (inherits all QPSOoptions, adds):

Parameter	Type	Default	Description
population	int	20	Candidates per generation
offspring	int	10	Offspring generated per generation
top_ratio	float	0.05	Fraction kept as elite
processes	int	cpu_count()	Parallel workers
erode	float	0.9999	Candidate erosion rate
erode_freq	int	200	Erosion frequency in iterations
append_tune	str	""	File path to write tunes to
skew_check_period	int	2	Skew-memory check interval (0 = disabled)
skew_mc_iterations	int	70	Monte Carlo iterations per skew check
skew_perturbation	float	0.002	Perturbation for skew detection
skew_sigma	float	0.01	Skew penalty sigma
skew_memory_cap	int	1000	Max skew-memory entries
select_data	Data or None	None	Explicit walk-forward selection dataset
consistency_fn	callable or None	None	Walk-forward consistency function
consistency_target	float	3.0	Target train/select ADR ratio

Overridden defaults from QPSOoptions: fitness_period=20, cyclic_freq=25, improvements=10000, temperature=1.

Result dict includes wf_* keys with walk-forward consistency metadata when consistency_fn is set.

---

IPSE — Iterative Parametric Space Expansion

Expands the search space outward from profitable regions. Brute-forces one parameter at a time via linear sweep.

from qtradex.optimizers import IPSE, IPSEoptions

opts = IPSEoptions()
opt = IPSE(data, options=opts)
best = opt.optimize(bot)

IPSEoptions parameters:

Parameter	Type	Default	Description
acceleration	float	0.8	Space shrinking rate (lower = faster shrink)
space_size	int	25	Candidates per parameter per sweep
processes	int	cpu_count()	Parallel workers
show_terminal	bool	True	Print progress to terminal
print_tune	bool	False	Print best tune on completion

---

AION — Adaptive Intelligent Optimization Network

Multi-agent optimizer with separate Mutator, Filter, Evaluator, and Learner agents sharing state through OptState. Uses quantum tunneling, elite preservation, smart skip, and bad-region memory.

from qtradex.optimizers import AION, AIONoptions

opts = AIONoptions()
opt = AION(data, options=opts)
best = opt.optimize(bot)

AIONoptions parameters:

Parameter	Type	Default	Description
epochs	float	inf	Max iterations
improvements	int	100000	Stop after this many improvements
cooldown	int	0	Iterations after an improvement before checking limit
show_terminal	bool	True	Print progress to terminal
print_tune	bool	True	Print best tune on completion
plot_period	int	100	Plot interval
quantum_tunneling_prob	float	0.05	Probability of 5x escape jump
min_temperature	float	0.05	Minimum mutation temperature
max_temperature	float	3.0	Maximum mutation temperature
synapses	int	50	Successful parameter sets to remember
neurons	list	[]	Active parameter indices (empty = all)
fitness_ratios	list or None	None	Fit / out-of-fit scoring ratio
enable_cache	bool	True	Cache evaluated (tune → score) pairs
elite_preservation	int	3	Number of top candidates preserved per epoch
smart_skip_threshold	int	10	Max consecutive skips before forced exploration
bad_region_memory	int	50	Max bad-region entries to track

---

MouseWheelTuner — Interactive GUI

Launches a Tkinter window with scrollable knobs for each tune parameter. No options class.

from qtradex.optimizers import MouseWheelTuner

opt = MouseWheelTuner(data, wallet)
opt.optimize(bot)  # Blocks until window is closed

No return value. Manual tuning via the GUI only.

Result dict

opt.optimize() returns dict[str, tuple[dict, BaseBot]] mapping metric names to results:

result["roi"]       # -> (score_dict, best_bot_for_roi)
result["sharpe"]    # -> (score_dict, best_bot_for_sharpe)
# ... per metric

Each score_dict has the metric values for that bot's backtest. Each best_bot is a fresh bot instance with the optimal .tune set.

MouseWheelTuner returns None.