MCLib

Library "MCLib"

f_eval_validator_path(sim_buffer, run, setup_horizon, setup_entry_price, setup_direction, setup_tp1, setup_sl, fv_base, fv_drift_step, max_hold_bars)
Parameters:
sim_buffer (array<float>)
run (int)
setup_horizon (int)
setup_entry_price (float)
setup_direction (string)
setup_tp1 (float)
setup_sl (float)
fv_base (float)
fv_drift_step (float)
max_hold_bars (int)

f_calc_validator_rr(entry_price, eval_direction, mae_price, mfe_price)
Parameters:
entry_price (float)
eval_direction (string)
mae_price (float)
mfe_price (float)

f_run_lite_antithetic_mc(sim_buffer_A, sim_buffer_B, lite_runs, setup_horizon, setup_entry_price, mc_pool_idx, mc_master_pool, mc_current_state, mc_pool_size, per_bar_vol, mc_squeeze_intensity, fv_cyclic_kalman, fv_drift, c0, is_stretched, is_coupled, shadow_price)
Parameters:
sim_buffer_A (array<float>)
sim_buffer_B (array<float>)
lite_runs (int)
setup_horizon (int)
setup_entry_price (float)
mc_pool_idx (array<int>)
mc_master_pool (array<float>)
mc_current_state (int)
mc_pool_size (int)
per_bar_vol (float)
mc_squeeze_intensity (float)
fv_cyclic_kalman (float)
fv_drift (float)
c0 (float)
is_stretched (bool)
is_coupled (bool)
shadow_price (float)

f_run_realtime_mc_chunk(mc_sim_buffer_A, mc_sim_buffer_B, mc_runs_done, mc_target_runs, mc_chunk_size, mc_horizon, mc_pool_idx, mc_master_pool, mc_current_state, mc_pool_size, per_bar_vol, mc_vol_scalar_base, mc_squeeze_intensity, mc_breakout_multiplier, fv_cyclic_kalman, fv_drift, c0, is_stretched, is_coupled, shadow_price)
Parameters:
mc_sim_buffer_A (array<float>)
mc_sim_buffer_B (array<float>)
mc_runs_done (int)
mc_target_runs (int)
mc_chunk_size (int)
mc_horizon (int)
mc_pool_idx (array<int>)
mc_master_pool (array<float>)
mc_current_state (int)
mc_pool_size (int)
per_bar_vol (float)
mc_vol_scalar_base (float)
mc_squeeze_intensity (float)
mc_breakout_multiplier (float)
fv_cyclic_kalman (float)
fv_drift (float)
c0 (float)
is_stretched (bool)
is_coupled (bool)
shadow_price (float)

f_update_progressive_percentiles(mc_sim_buffer_A, mc_sim_buffer_B, mc_horizon, mc_runs_done, mc_progressive_p10, mc_progressive_p50, mc_progressive_p90)
Parameters:
mc_sim_buffer_A (array<float>)
mc_sim_buffer_B (array<float>)
mc_horizon (int)
mc_runs_done (int)
mc_progressive_p10 (array<float>)
mc_progressive_p50 (array<float>)
mc_progressive_p90 (array<float>)

Notas de prensa

v2

Added:
MCPhysicsParams
Monte Carlo physics parameters for generic simulation configuration
Fields:
squeeze_intensity (series float): Squeeze intensity multiplier (0.0 = no amplitude prediction, per U10)
use_mean_reversion (series bool): Enable mean reversion pull toward fair value
mr_strength (series float): Mean reversion strength coefficient
use_vector_coupling (series bool): Enable vector coupling to shadow price
vector_strength (series float): Vector coupling strength coefficient
shadow_price (series float): Shadow price for vector coupling (if enabled)

Notas de prensa

v3

Updated:
f_eval_validator_path(sim_buffer, run, setup_horizon, setup_entry_price, setup_direction, setup_sl, max_hold_bars)
Parameters:
sim_buffer (array<float>)
run (int)
setup_horizon (int)
setup_entry_price (float)
setup_direction (string)
setup_sl (float)
max_hold_bars (int)

f_run_lite_antithetic_mc(sim_buffer_A, sim_buffer_B, lite_runs, setup_horizon, setup_entry_price, mc_pool_idx, mc_master_pool, mc_current_state, mc_pool_size, per_bar_vol, mc_squeeze_intensity, fv_cyclic_kalman, fv_drift, c0, is_stretched, is_coupled)
Parameters:
sim_buffer_A (array<float>)
sim_buffer_B (array<float>)
lite_runs (int)
setup_horizon (int)
setup_entry_price (float)
mc_pool_idx (array<int>)
mc_master_pool (array<float>)
mc_current_state (int)
mc_pool_size (int)
per_bar_vol (float)
mc_squeeze_intensity (float)
fv_cyclic_kalman (float)
fv_drift (float)
c0 (float)
is_stretched (bool)
is_coupled (bool)

f_run_realtime_mc_chunk(mc_sim_buffer_A, mc_sim_buffer_B, mc_runs_done, mc_target_runs, mc_chunk_size, mc_horizon, mc_pool_idx, mc_master_pool, mc_current_state, mc_pool_size, per_bar_vol, mc_vol_scalar_base, mc_squeeze_intensity, mc_breakout_multiplier, fv_cyclic_kalman, fv_drift, c0, is_stretched, is_coupled)
Parameters:
mc_sim_buffer_A (array<float>)
mc_sim_buffer_B (array<float>)
mc_runs_done (int)
mc_target_runs (int)
mc_chunk_size (int)
mc_horizon (int)
mc_pool_idx (array<int>)
mc_master_pool (array<float>)
mc_current_state (int)
mc_pool_size (int)
per_bar_vol (float)
mc_vol_scalar_base (float)
mc_squeeze_intensity (float)
mc_breakout_multiplier (float)
fv_cyclic_kalman (float)
fv_drift (float)
c0 (float)
is_stretched (bool)
is_coupled (bool)

Notas de prensa

v4

Added:
f_draw_mc_cones(cone_gate, p10, p50, p90, horizon, l10_arr, l50_arr, l90_arr, fill_arr, show_med, opacity, current_bar_index, mintick)
Parameters:
cone_gate (bool)
p10 (array<float>)
p50 (array<float>)
p90 (array<float>)
horizon (int)
l10_arr (array<line>)
l50_arr (array<line>)
l90_arr (array<line>)
fill_arr (array<linefill>)
show_med (bool)
opacity (float)
current_bar_index (int)
mintick (float)

Notas de prensa

v6

Added:
f_get_mc_family(f1_vec, f2_corr, f3_innov, f4_te, f5_orth, f6_phi)
Classify current market state into MC Family for path interpretation
Parameters:
f1_vec (float): VectorOsc (z-scored)
f2_corr (float): BasketCorr (normalized)
f3_innov (float): InnovZ (z-scored)
f4_te (float): TE_Osc (normalized)
f5_orth (float): OrthoZcvb (z-scored)
f6_phi (float): PhiDiv (normalized)
Returns: MC Family ID: 0=Continuation, 1=Mean-Revert, 2=Transition, 3=Velocity, 4=Uncertain

Notas de prensa

v7

Added:
f_welford_knn_bootstrap(final_knn_ids, cumw, arr_close, arr_ATR, mc_horizon, mc_runs, mc_noise_scale, current_close, pathsUpAtH, mc_progressive_p10, mc_progressive_p50, mc_progressive_p90, bar_idx, time_val)
Run Welford MC bootstrap with kNN-weighted path resampling and antithetic noise
Parameters:
final_knn_ids (array<int>): kNN neighbor indices
cumw (array<float>): Cumulative weights for neighbor selection
arr_close (array<float>): Close price history
arr_ATR (array<float>): ATR history
mc_horizon (int): Simulation horizon
mc_runs (int): Maximum MC runs (capped at 500)
mc_noise_scale (float): Noise scaling factor
current_close (float): Current close price
pathsUpAtH (array<int>): Output: paths above current price at each horizon step (mutated)
mc_progressive_p10 (array<float>): Output: 10th percentile path (mutated)
mc_progressive_p50 (array<float>): Output: 50th percentile path (mutated)
mc_progressive_p90 (array<float>): Output: 90th percentile path (mutated)
bar_idx (int): Current bar index
time_val (int): Current time value
Returns: [paths_up, total_paths, wMean, wM2, wCount] for further analysis

Notas de prensa

v8

Added:
f_welford_knn_chunk(knn_ids, cumw, arr_close, arr_ATR, mc_horizon, chunk_runs, mc_noise_scale, current_close, wMean, wM2, wCount, pathsUp, run_offset, bar_idx, time_val)
Run a CHUNK of MC runs, updating persistent Welford accumulators across ticks
Parameters:
knn_ids (array<int>): kNN neighbor indices
cumw (array<float>): Cumulative weights for neighbor selection
arr_close (array<float>): Close price history
arr_ATR (array<float>): ATR history
mc_horizon (int): Simulation horizon
chunk_runs (int): Number of runs to execute in this chunk
mc_noise_scale (float): Noise scaling factor
current_close (float): Current close price
wMean (array<float>): Persistent Welford mean accumulator (mutated)
wM2 (array<float>): Persistent Welford M2 accumulator (mutated)
wCount (array<int>): Persistent Welford count accumulator (mutated)
pathsUp (array<int>): Persistent paths-up counter (mutated)
run_offset (int): Starting run index for unique PRNG seeds
bar_idx (int): Current bar index
time_val (int): Current time value
Returns: int Number of paths executed (chunk_runs × 2 for antithetic)

Notas de prensa

v9

Added:
f_render_backtest_table(tbl, raw_total, raw_wins, raw_pnl_sum, raw_mae_sum, raw_mfe_sum, raw_mean, raw_m2, filt_total, filt_wins, filt_pnl_sum, filt_mae_sum, filt_mfe_sum, filt_mean, filt_m2, avg_agree)
Render backtest summary table (Phase 6)
Parameters:
tbl (table): Table object to populate
raw_total (int): Total raw setups
raw_wins (int): Raw wins count
raw_pnl_sum (float): Raw PnL sum
raw_mae_sum (float): Raw MAE sum
raw_mfe_sum (float): Raw MFE sum
raw_mean (float): Raw Welford mean
raw_m2 (float): Raw Welford M2
filt_total (int): Filtered setups (MC confirmed)
filt_wins (int): Filtered wins count
filt_pnl_sum (float): Filtered PnL sum
filt_mae_sum (float): Filtered MAE sum
filt_mfe_sum (float): Filtered MFE sum
filt_mean (float): Filtered Welford mean
filt_m2 (float): Filtered Welford M2
avg_agree (float): Average MC agreement %

Notas de prensa

v10

Notas de prensa

v11

Notas de prensa

v12

Added:
f_prng(seed1, seed2)
Deterministic pseudo-random number generator (0.0 to 1.0)
Parameters:
seed1 (int): Primary seed (e.g., bar_index or iteration counter)
seed2 (int): Secondary seed (e.g., run number or feature index)
Returns: Uniform random float in [0.0, 1.0)
note Deterministic - same seeds always produce same output (no time dependency)

Updated:
f_welford_knn_bootstrap(final_knn_ids, cumw, arr_close, arr_ATR, mc_horizon, mc_runs, mc_noise_scale, current_close, pathsUpAtH, mc_progressive_p10, mc_progressive_p50, mc_progressive_p90, bar_idx)
Run Welford MC bootstrap with kNN-weighted path resampling and antithetic noise
Parameters:
final_knn_ids (array<int>): kNN neighbor indices
cumw (array<float>): Cumulative weights for neighbor selection
arr_close (array<float>): Close price history
arr_ATR (array<float>): ATR history
mc_horizon (int): Simulation horizon
mc_runs (int): Maximum MC runs (capped at 500)
mc_noise_scale (float): Noise scaling factor
current_close (float): Current close price
pathsUpAtH (array<int>): Output: paths above current price at each horizon step (mutated)
mc_progressive_p10 (array<float>): Output: 10th percentile path (mutated)
mc_progressive_p50 (array<float>): Output: 50th percentile path (mutated)
mc_progressive_p90 (array<float>): Output: 90th percentile path (mutated)
bar_idx (int): Current bar index (used for deterministic PRNG seed)
Returns: [paths_up, total_paths, wMean, wM2, wCount] for further analysis

f_welford_knn_chunk(knn_ids, cumw, arr_close, arr_ATR, mc_horizon, chunk_runs, mc_noise_scale, current_close, wMean, wM2, wCount, pathsUp, run_offset, bar_idx)
Run a CHUNK of MC runs, updating persistent Welford accumulators across ticks
Parameters:
knn_ids (array<int>): kNN neighbor indices
cumw (array<float>): Cumulative weights for neighbor selection
arr_close (array<float>): Close price history
arr_ATR (array<float>): ATR history
mc_horizon (int): Simulation horizon
chunk_runs (int): Number of runs to execute in this chunk
mc_noise_scale (float): Noise scaling factor
current_close (float): Current close price
wMean (array<float>): Persistent Welford mean accumulator (mutated)
wM2 (array<float>): Persistent Welford M2 accumulator (mutated)
wCount (array<int>): Persistent Welford count accumulator (mutated)
pathsUp (array<int>): Persistent paths-up counter (mutated)
run_offset (int): Starting run index for unique PRNG seeds
bar_idx (int): Current bar index (used for deterministic PRNG seed)
Returns: int Number of paths executed (chunk_runs × 2 for antithetic)

Notas de prensa

v13

Removed:
f_run_lite_antithetic_mc(sim_buffer_A, sim_buffer_B, lite_runs, setup_horizon, setup_entry_price, mc_pool_idx, mc_master_pool, mc_current_state, mc_pool_size, per_bar_vol, mc_squeeze_intensity, fv_cyclic_kalman, fv_drift, c0, is_stretched, is_coupled)

f_run_realtime_mc_chunk(mc_sim_buffer_A, mc_sim_buffer_B, mc_runs_done, mc_target_runs, mc_chunk_size, mc_horizon, mc_pool_idx, mc_master_pool, mc_current_state, mc_pool_size, per_bar_vol, mc_vol_scalar_base, mc_squeeze_intensity, mc_breakout_multiplier, fv_cyclic_kalman, fv_drift, c0, is_stretched, is_coupled)

f_update_progressive_percentiles(mc_sim_buffer_A, mc_sim_buffer_B, mc_horizon, mc_runs_done, mc_progressive_p10, mc_progressive_p50, mc_progressive_p90)

f_get_mc_family(f1_vec, f2_corr, f3_innov, f4_te, f5_orth, f6_phi)
Classify current market state into MC Family for path interpretation

Biblioteca Pine

Fiel al espíritu de TradingView, el autor ha publicado este código de Pine como biblioteca de código abierto, para que otros programadores de nuestra comunidad puedan reutilizarlo. ¡Enhorabuena al autor! Puede usar esta biblioteca de forma privada o en otras publicaciones de código abierto, pero su reutilización en publicaciones está sujeta a nuestras Normas internas.

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Biblioteca Pine

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