hedgequantx-prop-trading

USE FOR:

• "prop firm trading automation"
• "TopStep / Apex / Bulenox automated trading"
• "futures copy trading"
• "ProjectX / Rithmic API trading"
• "multi-account prop firm bot"
• "automated futures trading CLI"

tags: [prop-trading, futures, copy-trading, TopStep, Apex, Rithmic, ProjectX, Tradovate, CLI, multi-account] kind: tool category: execution-algo-trading

What Is HedgeQuantX?

CLI tool for automated futures trading across 37+ proprietary trading firms.

• Repo: https://github.com/HedgeQuantX/HedgeQuantX
• Install: npm i -g hedgequantx
• Architecture: Local-only, no external server, AES-256-GCM encrypted sessions

Installation & Launch

npm i -g hedgequantx
hqx          # or: hedgequantx

Supported Platforms & Firms

ProjectX (19 firms)

TopStep · TickTickTrader · TradeDay · Goat Futures · + 15 more

Rithmic (16 firms)

Apex Trader Funding · MES Capital · Bulenox · + 13 more

Tradovate (3 firms)

Apex · TakeProfitTrader · MyFundedFutures

Operating Modes

Mode 1: One Account (HQX Strategy)

Single account → runs proprietary HQX systematic strategy

Mode 2: Copy Trading

Lead account → executes primary trades
    ↓ mirrors to
Follower accounts (multiple) → same trades replicated

Key Features

Use Case: Pass Prop Firm Challenge

# 1. Install and launch
npm i -g hedgequantx && hqx

# 2. Connect to TopStep (via ProjectX API)
# 3. Select "One Account Mode" → HQX strategy
# 4. Monitor P&L in real-time dashboard
# 5. Meet daily/max drawdown limits automatically

── KNOWLEDGE INJECTION: All of Statistics in 1 Hour (JensenMath) ──

Source: https://www.youtube.com/watch?v=_Pyi12dn4Kw

Channel: JensenMath (MDM4U Grade 12 Data Management)

Routed to: trading.md → statistics-timeseries

Date: 2026-03-17

Statistics Complete Reference (JensenMath MDM4U)

A comprehensive statistics study guide covering all foundational concepts relevant to trading, quant research, and data science.

1. Data Types & Graphical Displays

Variable types:

• Qualitative (categorical): bar graphs, pie charts
• Quantitative (numeric): histograms, box plots, scatter plots

Distribution shapes:

• Symmetric (normal) · Left-skewed · Right-skewed · Bimodal · Uniform

Scatter plots & correlation:

• Positive / negative / no correlation
• Strong vs weak (spread around line of best fit)
• Outliers and leverage points

Misleading graphs — red flags:

• Truncated y-axis (starts non-zero)
• Unequal intervals on axis
• 3D effects distorting area/volume
• Cherry-picked time ranges

2. Data Collection & Bias

Sampling methods:

Sources of bias:

• Sampling bias: non-representative sample
• Response bias: wording influences answers
• Non-response bias: certain groups don't respond
• Voluntary response bias: self-selected strong opinions

3. Descriptive Statistics

Measures of central tendency:

Mean   = Σx / n
Median = middle value (or avg of two middle)
Mode   = most frequent value

Measures of spread:

Range      = max − min
Variance   = Σ(x − x̄)² / (n−1)        [sample]
Std Dev    = √Variance
IQR        = Q3 − Q1                    [robust to outliers]

Choosing the right measure:

Symmetric distribution  → use mean + std dev
Skewed / outliers       → use median + IQR

Z-score (standardization):

z = (x − μ) / σ

Interpretation:
z = +1.5 → value is 1.5 standard deviations ABOVE mean
z = −2.0 → value is 2.0 standard deviations BELOW mean

4. Normal Distribution

Properties:

• Bell-shaped, symmetric about μ
• Mean = Median = Mode
• Total area under curve = 1
• Defined by μ (mean) and σ (std dev)

Empirical Rule (68-95-99.7):

μ ± 1σ → 68.27% of data
μ ± 2σ → 95.45% of data
μ ± 3σ → 99.73% of data

Using z-tables / standard normal:

from scipy import stats

# P(X < 75) where μ=70, σ=5
z = (75 - 70) / 5          # z = 1.0
p = stats.norm.cdf(z)      # p ≈ 0.8413 → 84.13%

# P(65 < X < 75)
p = stats.norm.cdf(1.0) - stats.norm.cdf(-1.0)  # ≈ 68.27%

# Find value at 90th percentile
x = stats.norm.ppf(0.90, loc=70, scale=5)        # x ≈ 76.4

Confidence intervals:

CI = x̄ ± z* · (σ / √n)

Common z* values:
  90% CI → z* = 1.645
  95% CI → z* = 1.960
  99% CI → z* = 2.576

5. Probability

Fundamental rules:

P(A) = favourable outcomes / total outcomes     [theoretical]
P(A) = successes / trials                       [experimental]

0 ≤ P(A) ≤ 1
P(A) + P(A') = 1                               [complement rule]

Addition rule:

P(A ∪ B) = P(A) + P(B) − P(A ∩ B)            [general]
P(A ∪ B) = P(A) + P(B)                         [mutually exclusive]

Multiplication rule:

P(A ∩ B) = P(A) · P(B|A)                       [general / dependent]
P(A ∩ B) = P(A) · P(B)                         [independent events]

Conditional probability:

P(B|A) = P(A ∩ B) / P(A)

"Probability of B GIVEN A has occurred"

Set notation:

A ∪ B  → A OR B  (union)
A ∩ B  → A AND B (intersection)
A'     → NOT A   (complement)

6. Counting Methods

Fundamental counting principle:

m choices for event 1 × n choices for event 2 = m × n total

Permutations (ORDER matters):

nPr = n! / (n−r)!

All arrangements of n items:  n!
Arrangements with repeats:    n! / (a! · b! · ...)

Combinations (ORDER doesn't matter):

nCr = n! / (r! · (n−r)!)     also written C(n,r) or (n choose r)

Key identity: nCr = nC(n−r)

from math import factorial, comb, perm

# Permutations: arrange 3 from 5
perm(5, 3)       # = 60

# Combinations: choose 3 from 5
comb(5, 3)       # = 10

7. Probability Distributions

Discrete probability distribution requirements:

1. 0 ≤ P(x) ≤ 1 for all x
2. ΣP(x) = 1

Expected value and variance:

E(X) = μ = Σ[x · P(x)]
Var(X) = σ² = Σ[(x−μ)² · P(x)]

#### Binomial Distribution B(n, p)

Conditions: fixed n trials, constant p, independent, binary outcome

P(X = k) = C(n,k) · pᵏ · (1−p)^(n−k)

μ = np
σ² = np(1−p)
σ = √(np(1−p))

from scipy.stats import binom
# 10 flips, p=0.5, P(exactly 6 heads)
binom.pmf(6, n=10, p=0.5)    # ≈ 0.2051

# P(X ≤ 6)
binom.cdf(6, n=10, p=0.5)    # ≈ 0.8281

#### Geometric Distribution

Conditions: repeated trials until FIRST success

P(X = k) = (1−p)^(k−1) · p    [k = trial of first success]

μ = 1/p
σ² = (1−p) / p²

#### Hypergeometric Distribution

Conditions: sampling WITHOUT replacement from finite population

Population N, K successes in population, draw n items:
P(X = k) = C(K,k) · C(N−K, n−k) / C(N,n)

μ = nK/N
σ² = nK(N−K)(N−n) / [N²(N−1)]

8. Linear Regression

Least squares regression line:

ŷ = a + bx

b = r · (Sy / Sx)           [slope]
a = ȳ − b·x̄                [intercept]

where r = correlation coefficient (−1 ≤ r ≤ 1)

Correlation coefficient r:

|r| = 1.0       perfect linear relationship
|r| > 0.8       strong
0.5 < |r| < 0.8 moderate
|r| < 0.5       weak
r = 0           no linear relationship

Coefficient of determination r²:

r² = proportion of variance in y explained by x
r² = 0.81 → x explains 81% of variation in y

import numpy as np
from scipy import stats

slope, intercept, r, p_value, std_err = stats.linregress(x, y)
print(f"r = {r:.3f}, r² = {r**2:.3f}")
print(f"ŷ = {intercept:.2f} + {slope:.2f}x")

Trading Applications of Each Topic