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 Lançamento

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 Lançamento

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 Lançamento

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 Lançamento

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 Lançamento

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 Lançamento

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 Lançamento

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 Lançamento

v10

Notas de Lançamento

v11

Notas de Lançamento

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 Lançamento

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 do Pine

Em verdadeiro espírito TradingView, o autor publicou este código Pine como uma biblioteca de código aberto para que outros programadores Pine da nossa comunidade possam reutilizá-lo. Parabéns ao autor! Você pode usar esta biblioteca de forma privada ou em outras publicações de código aberto, mas a reutilização deste código em publicações é regida pelas Regras da Casa.

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Biblioteca do Pine

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