Hedgequantx prop trading

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

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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

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Installation & Launch

npm i -g hedgequantx
hqx          # or: hedgequantx

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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

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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

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Key Features

Feature	Detail
Multi-account	Manage multiple prop accounts simultaneously
Real-time monitoring	Live balance, P&L, positions, orders
Market hours validation	Auto-validates trading hours per instrument
Session encryption	AES-256-GCM, machine-bound keys
Local execution	Direct API, no server middleman
Credential security	Never stored in plaintext, 0600 file permissions

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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

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── 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.

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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

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2. Data Collection & Bias

Sampling methods:

Method	Description
Simple random	Every member equally likely
Systematic	Every Nth member
Stratified	Proportional subgroups
Cluster	Random groups (not individuals)
Convenience	Easiest to reach (biased)

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

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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

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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

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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)

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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

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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)]

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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")

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Trading Applications of Each Topic

Statistics Topic	Trading Use Case
Normal distribution	Return distribution, VaR, z-score signals
Confidence intervals	Entry zones, expected price ranges
Correlation (r)	Pair trading, hedge ratios, sector correlation
Regression	Price prediction, beta calculation, factor models
Binomial dist.	Win rate modeling, position sizing (Kelly)
Conditional probability	Bayesian signal updating
Hypergeometric	Sampling from finite order book
Z-score	Mean reversion entries (Bollinger Bands logic)
Standard deviation	Volatility measurement, ATR normalization
Combinations nCr	Portfolio combinations, basket construction

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