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 --- ## 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 ```bash 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 | 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 | --- ## Use Case: Pass Prop Firm Challenge ```bash # 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:** | 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 --- ### 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:** ```python 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) ``` ```python 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)) ``` ```python 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 ``` ```python 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 | 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 | ---