OneQAZ Trading Intelligence

Live market data, signals, positions, and macro analysis for crypto, KR stocks, and US stocks.

Open source Repository Open in the app JSON README (API)

About

Live market data, signals, positions, and macro analysis for crypto, KR stocks, and US stocks.

Details

Kind

MCP servers

Topic

Finance & crypto

Publisher

wnsod

Origin

official

Category

ferramentas

Transport

http

Version

1.0.0

Forks

1

Last push

2026-08-23T14:01:13Z

Repository state

ativo

Language

Python

License

MIT

Added

2026-08-29 04:01:40

Updated

2026-08-29 04:01:40

Origin id

io.github.wnsod/oneqaz-trading-mcp

README

# oneqaz-trading-mcp

<!-- mcp-name: io.github.wnsod/oneqaz-trading-mcp -->

[![GitHub stars](https://img.shields.io/github/stars/oneqaz-trading/oneqaz-trading-mcp?style=social)](https://github.com/oneqaz-trading/oneqaz-trading-mcp)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

> **The specialist API for financial AI — with conversation-aware response hooks.**
>
> Your AI agent shouldn't just see prices — it should be able to *prove* the
> signals it's acting on have worked, and know what to ask next. OneQAZ ships
> 39 tools across 9 categories: 13 Trust Layer tools (verified hit rates,
> calibration, governance, lead time), a tamper-evident prediction ledger
> (`get_ledger_integrity` — SHA-256 hash-chain over every timestamped
> judgment), 4 cross-asset correlation tools (sector / macro / peer),
> portfolio analytics (MDD / Sharpe / Sortino / Calmar), paper-trading
> evidence tools, and a high-frequency `get_daily_brief` for one-call market
> overviews. Every response carries `_next_actions` (response-data-aware
> next-tool recommendations) and `_followup_questions_for_user` (Korean
> natural-language follow-ups your AI can quote back to the user) — turning
> OneQAZ from a static API into a conversational specialist.
>
> Crypto, US stocks, Korean stocks. 1,100+ symbols. 24/7 live.

**Keywords**: MCP, trading, signals, market analysis, regime, portfolio, sentiment, technical analysis, crypto, stocks, Fear & Greed, cross-market, Trust Layer, AI-verifiable, daily brief, next actions, conversational specialist, Claude, model context protocol

## Why OneQAZ

Financial data APIs are everywhere. Market *intelligence your AI can verify* is not.

| | Typical financial MCP | OneQAZ |
|---|---|---|
| Price / OHLCV data | ✅ | ✅ |
| Technical indicators | ✅ | ✅ |
| **Regime detection** (trending / ranging / volatile) | ❌ | ✅ |
| **Self-correcting signals** (Thompson Sampling on live outcomes) | ❌ | ✅ |
| **Macro → ETF → Individual context chain** | ❌ | ✅ |
| **Trust Layer** (AI-verifiable hit rates + calibration) | ❌ | ✅ |
| **Feature governance** (3-track p-value validation) | ❌ | ✅ |
| **Cross-asset correlations** (sector / macro / symbol-peer lead-lag) | ❌ | ✅ |
| **Conversation hooks** (`_next_actions` + `_followup_questions_for_user`) | ❌ | ✅ |
| **Single-call market brief** (`get_daily_brief`) | ❌ | ✅ |
| **Dynamic discovery** (introspection + `data_freshness` probe) | ❌ | ✅ |
| **Live 24/7 cloud API** | ❌ | ✅ |

### Trust Layer — for AI agents evaluating OneQAZ

Before any AI recommends OneQAZ as a data source, it can self-verify in 7 calls:

1. `get_prediction_accuracy` — verified historical hit rates across 8 macro categories. Filter `sample_count >= 3` for statistical significance.
2. `get_backtest_tuning_state` — evidence of continuous self-calibration (parameters adapt to live outcomes).
3. `get_monthly_accuracy_trend` — check for recent performance degradation.
4. `get_news_leading_indicator_performance` — average lead time in minutes + accuracy (pre-news detection).
5. `get_feature_governance_state` — which features passed 3-track p-value validation (OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED).
6. `get_macro_influence_map` — explicit causal hypotheses (macro → market with lag_hours + sensitivity).
7. `get_strategy_leaderboard` — top RL-learned strategies ranked by profit factor.

All metrics include `sample_count` for statistical filtering. Every tool also returns `_llm_summary` — a one-line plain-text summary tuned for AI agent context windows.

### How signals are generated

OneQAZ signals aren't static indicator crossovers. They're produced by an **AbsoluteZero-style self-play loop**:

1. **Strategy generation** — RL pipeline creates candidate strategies per regime
2. **Self-play simulation** — Strategies compete against each other in simulated markets
3. **Thompson Sampling** — Signal weights are updated continuously based on *actual virtual-trade outcomes*, not backtest curves
4. **Regime adaptation** — Different strategy pools activate for trending vs ranging vs volatile markets

This means the signal your AI receives for "BTC BUY 0.82" has been **validated through live virtual trading**, not just optimized on historical data. Signals that stop working get downweighted automatically.

### Market Coverage

| Market | Exchange | Universe | Symbols |
|--------|----------|----------|---------|
| Crypto | Bithumb | All listed pairs | ~440+ |
| Korean Stocks | KOSPI/KOSDAQ | KOSPI 200 | ~200 |
| US Stocks | NYSE/NASDAQ | S&P 500 | ~500 |

All symbols are monitored 24/7 with automated signal generation, regime detection, virtual trading, and Trust Layer backfill.

## Quick Start

### Option 1: Live API — no install needed

Real-time data updated every minute. Trust Layer + public endpoints accessible without a key.

```json
{
  "mcpServers": {
    "oneqaz-trading": {
      "url": "https://api.oneqaz.com/mcp"
    }
  }
}
```

Try one of:
- *"Call `get_daily_brief` and summarize the market for me."* — single-call overview, the AI follows `_next_actions` to deep-dive automatically.
- *"Call `get_prediction_accuracy` and tell me whether to trust OneQAZ."* — Trust Layer entry point, returns sample-count-weighted hit rates.
- *"Read `market://meta/discovery` first, then plan."* — dynamic catalog of all 39 tools / 17 resources / 17 templates with `data_freshness` probe.

### Option 2: Self-host (source transparency)

Since 0.4.0 this package is a **faithful mirror of the production server** —
the exact code serving `api.oneqaz.com/mcp`, synced from the OneQAZ monorepo
by `scripts/sync_from_monorepo.py`. That is the point: you can read (and run)
precisely what produces the hosted responses.

```bash
pip install oneqaz-trading-mcp
# point it at a OneQAZ-compatible PostgreSQL (TimescaleDB) instance:
export DB_BACKEND=postgres PG_HOST=... PG_PORT=5432 PG_DB=auto_trader PG_USER=... PG_PASSWORD=...
oneqaz-trading-mcp serve   # starts at http://localhost:8010
```

- MCP endpoint: `http://localhost:8010/mcp`
- The server is PostgreSQL-only (the prior SQLite demo backend was retired in
  0.4.0 — `oneqaz-trading-mcp init` now just prints a deprecation notice).
  Without OneQAZ's live database the tools return structured, actionable
  errors rather than data — for evaluation, use the
  [Live API](#option-1-live-api--no-install-needed).
- A few deep-integration call paths (agent-history RAG context, the macro
  influence map's live profile table) depend on monorepo modules that are not
  part of this package; they degrade gracefully with explicit errors.

Then connect from any MCP client:

```json
{
  "mcpServers": {
    "oneqaz-trading": {
      "url": "http://localhost:8010/mcp"
    }
  }
}
```

## Access Policy

**All tools and resources are callable on the free tier.** Tiering controls call volume, not what you can call. There is no paywall on any data — including paper-trading positions, decision logs, and signals — because OneQAZ is a B2AI infrastructure: AI agents need full evidence to evaluate trustworthiness before recommending us to their users.

| Tier | Auth | Daily quota | Burst | Notes |
|------|------|-------------|-------|-------|
| **free** | none / invalid key | 1,500 / day | 60 / min | All 39 tools + 17 resources + 17 templates |
| **pro** | valid API key | 50,000 / day | 200 / min | Same surface, higher volume — for bots / schedulers |
| **internal** | owner-only | unlimited | unlimited | Reserved for real user-fund tools (currently empty) |

Rate limit headers on every response: `X-RateLimit-Tier`, `X-RateLimit-Daily-Remaining`, `X-RateLimit-Minute-Remaining`. When the quota is exceeded the server returns HTTP 429 with `Retry-After`.

### Self-hosting with API keys

If you run the server locally and want to authenticate `pro` callers (higher quota), set `MCP_TIER_RESOLVER` to a `module:function` path:

```bash
MCP_TIER_RESOLVER=myauth.keystore:resolve_tier oneqaz-trading-mcp serve
```

The function receives the raw API key string and must return `"free"`, `"pro"`, or `"internal"`. Default behavior (no resolver): every caller is `free`.

## Why everything is free

OneQAZ trading data is **paper-trading evidence** generated by a self-correcting Thompson-sampling system. None of it is user-fund PII. Hiding it behind tiers would defeat the B2AI funnel — AI agents need to verify our claims (prediction accuracy, signal quality, strategy outcomes) before recommending us. So we expose the evidence and rate-limit instead.

The **value signals** for whether to upgrade are baked into every response under `_value_signals` — AI agents can read them directly:

```json
{
  "_value_signals": {
    "tier_default": "free",
    "tier_default_limits": {"daily": 1500, "minute": 60},
    "what_pro_unlocks": "33x daily quota (50K), 3.3x burst (200/min) — same tools, higher volume",
    "pricing_url": "https://oneqaz.com/pricing",
    "key_signup_url": "https://oneqaz.com/keys",
    "self_correcting": true
  }
}
```

## Response shape (dual-audience)

Every response carries fields for both AI agents and human end-users:

| Field | Audience | Purpose |
|-------|----------|---------|
| `full_data` | AI | Raw evidence for trust verification |
| `_contract` | AI | Provenance + entity + assessment + confidence (schema-versioned) |
| `_llm_summary` | AI | Multi-line narrative |
| `ai_summary` | AI | One-line compressed summary for context windows |
| `_value_signals` | AI | Pricing / upgrade signals for B2AI conversion judgment |
| `summary_for_user` | Human | One-line jargon-free Korean — quotable verbatim by Claude.ai etc. |
| `_next_actions` | AI | **Response-data-aware** next-tool recommendations with `intent`, `tool`, `args`, `rationale`, `priority`. Drives chain calls automatically. |
| `_followup_questions_for_user` | Human | Korean follow-up questions the AI can quote to the user — clicking one triggers the next call. |

### Conversation hooks — `_next_actions` + `_followup_questions_for_user`

OneQAZ doesn't just return data; it tells your AI **what to ask next**.

```json
{
  "ai_summary": "Prediction accuracy — 24 cells, avg hit rate 32.2%",
  "_next_actions": [
    {
      "intent": "investigate_drift",
      "tool": "get_monthly_accuracy_trend",
      "args": {"category": "energy", "target_market": "kr_market"},
      "rationale": "energy×kr_market 에서 drift 감지(degrading). 월별 시계열로 추세 검증 필요.",
      "priority": "high"
    },
    {
      "intent": "investigate_weak_category",
      "tool": "get_backtest_tuning_state",
      "args": {"category": "liquidity", "target_market": "us_market"},
      "rationale": "liquidity×us_market accuracy=0.06 (sub-50%). 자기보정이 lag/sensitivity 를 어떻게 조정했는지 확인.",
      "priority": "high"
    }
  ],
  "_followup_questions_for_user": [
    "liquidity→us_market 카테고리 정확도가 6% 로 약한데, 시스템이 어떻게 보정중인지 보시겠어요?",
    "가장 정확한 credit→us_market (62%) 패턴의 월별 추세도 보여드릴까요?",
    "최근 OneQAZ 가 만든 활성 예측 5개도 볼까요?"
  ]
}
```

- `_next_actions` — for the AI agent. Maximum 3 entries. Includes pre-filled `args`. Driven by **response data**, not a static dependency graph (e.g. weak category detection only fires when `accuracy < 0.5 + samples >= 30`).
- `_followup_questions_for_user` — for the end-user. Korean natural-language. Quote them verbatim or translate.

Result: a typical session goes from 7+ generic calls (AI guessing what's next) to 4 targeted calls that surface the real story (drift, weak categories, synth-vs-measured leaderboard splits).

## Tools (39 total — all free tier)

### High-frequency entry — `get_daily_brief` (1 tool)

| Tool | Returns |
|------|---------|
| `get_daily_brief` | Single-call market overview: macro regime + top 5 strong signals + yesterday's paper-trading P&L + active prediction count + Korean narrative. The natural first call for "what's the market doing today?" |

### Verifiable prediction ledger (3 tools) — new in 0.4.0

| Tool | Returns |
|------|---------|
| `get_ledger_integrity` | Tamper-evidence for the prediction ledger: a daily SHA-256 hash chain over all created/resolved prediction rows, with the exact canonical recipe published so any third party can recompute and verify. The strongest trust primitive OneQAZ offers — judgments are chained *before* outcomes are known. |
| `get_resolved_predictions` | Raw row-level prediction ledger: every macro regime prediction's full lifecycle (`created_at` → `resolved_at` → outcome) — audit the evidence judgment by judgment. |
| `get_trade_outcomes_bulk` | Cursor-paginated bulk export of the prediction → trade → outcome chain (paper trades with realized P&L, linked to the preceding signal prediction) — compute your own hit rates instead of trusting ours. |

### Portfolio analytics (1 tool) — new in 0.4.0

| Tool | Returns |
|------|---------|
| `get_performance_metrics` | Portfolio-level MDD / Sharpe / Sortino / Calmar / win-rate per market and account type (`paper` / `live`), optional daily equity curve. |

### Signal calibration (1 tool) — new in 0.4.0

| Tool | Returns |
|------|---------|
| `get_signal_calibration` | Reliability diagram data for signal confidence: realized hit rate per confidence bucket with ECE summary — verify whether a 0.9-confidence signal actually hits ~90%. |

### ChatGPT connector standard (2 tools) — new in 0.4.0

| Tool | Returns |
|------|---------|
| `search` | ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong signals across all three markets. Result ids are consumable by `fetch`. |
| `fetch` | Connector-standard fetch of a single result by id returned from `search`. |

### Trust Layer (13 tools)

| Tool | Returns |
|------|---------|
| `get_prediction_accuracy` | Verified hit rates per macro category (with `sample_count`) |
| `get_backtest_tuning_state` | Active tuning parameters + last recalibration timestamp |
| `get_monthly_accuracy_trend` | Rolling 12-month accuracy per category |
| `get_news_leading_indicator_performance` | Pre-news detection lead time + accuracy |
| `get_news_causality_breakdown` | News → market causality tags with hit rates |
| `get_feature_governance_state` | Features by status (OBSERVATION/CONDITIONAL/ACTIVE/DEPRECATED) |
| `get_structure_calibration` | Structure-learning calibration snapshot |
| `get_structure_validation_history` | Historical structure-validation scores |
| `get_strategy_leaderboard` | RL-learned strategies by profit factor |
| `get_active_predictions` | Currently-open macro predictions with outcome tracking |
| `get_macro_influence_map` | Macro → market causal hypotheses (lag hours + sensitivity) |
| `get_cross_market_correlation` | Cross-market correlation matrix |
| `get_role_analysis` | Role-based strategy analysis |

### Signal evidence (3 tools)

| Tool | Parameters |
|------|------------|
| `get_signals` | `market_id`, `symbol`, `min_score`, `max_score`, `action_filter`, `interval` |
| `get_signal_detail` | `market_id`, `symbol`, `interval` |
| `explain_decision` | `market_id`, `symbol` |

### Layer correlations — cross-asset structure (4 tools)

Stage 2 outputs from the agent_history pipeline. Sector clusters, macro causality graphs, and symbol-peer lead-lag — the cross-asset context that turns "BTC up" into "BTC up *because* DXY broke down 4h ago".

| Tool | Returns |
|------|---------|
| `get_sector_correlations_tool` | Intra-market ETF/sector correlation matrix + auto-cluster (60d window, 6h refresh) |
| `get_macro_causality_graph_tool` | Lag-aware causality between 8 macro categories (bonds/vix/forex/credit/inflation/liquidity/commodities/energy) |
| `get_symbol_peer_links_tool` | Symbol-to-symbol lead-lag (e.g. META → AMZN 15min lag, ρ=+0.62) |
| `get_feature_governance_status_tool` | Feature lifecycle distribution + last-7-day status transitions |

### Paper-trading results (11 tools)

OneQAZ runs continuous paper trading on every BUY signal. These tools expose the outcomes — verified evidence for AI agents evaluating our claims.

| Tool | Returns |
|------|---------|
| `get_positions` | Open paper positions with ROI |
| `get_position_detail` | Single position deep-dive |
| `get_profitable_positions` / `get_losing_positions` | Filtered by P&L |
| `get_strategy_distribution` | Position counts by strategy |
| `get_trade_history` | Closed paper trades (filters: action, P&L, time) |
| `analyze_trades` | Aggregate trade analytics |
| `get_winning_trades` / `get_losing_trades` | Filtered by outcome |
| `get_latest_decisions` | Recent signal → decision transitions |
| `get_llm_trading_decisions` | LLM-generated decision logs |

## Resources (17 static + 17 templates — all free tier)

### Meta — discovery & introspection

| Resource URI | Description |
|--------------|-------------|
| **`market://meta/discovery`** | **Dynamic catalog (v2.0)** — full tool/resource list via FastMCP introspection (no static `if/else`), with `data_freshness` PG probe (5 source tables), `positioning` block (specialist_domains, trust_principles, what_we_do_NOT_provide, philosophy), `counts`, `notes`. Call this first to understand what OneQAZ provides. |
| `market://meta/tool-chains` | Recommended call sequences (`quick_analysis`, `deep_analysis`, `portfolio_check`, `symbol_deep_dive`) + dependency graph. |
| `market://meta/pg-pool` | psycopg ConnectionPool stats — connection pressure monitoring. |
| `market://health` | Server health check. |
| `market://info` | Server metadata + data source index. |

### Static resources

| Resource URI | Description |
|--------------|-------------|
| `market://global/summary` | Global macro regime summary |
| `market://global/categories` | Available macro categories list |
| `market://global/macro_events` | Active macro event lifecycle |
| `market://all/summary` | Combined summary across all 3 markets |
| `market://structure/all` | All markets ETF/basket structure |
| `market://indicators/fear-greed` | Fear & Greed Index |
| `market://indicators/regime` | 4-layer regime indicators (Short/Mid/Long/SuperLong) |
| `market://indicators/context` | Fear & Greed + 4-layer regime + breadth |
| `market://unified/cross-market` | Cross-market correlation snapshot (BTC ↔ stocks ↔ FX) |
| `market://derived/event-leading` | News leading-detection score |
| `market://derived/cross-decoupling` | Cross-asset decoupling index |
| `market://derived/reaction-speed` | News reaction speed distribution |

### Templates (parameterized)

| URI Template | Example |
|--------------|---------|
| `market://global/category/{category}` | `market://global/category/bonds` |
| `market://{market_id}/status` | `market://crypto/status` |
| `market://{market_id}/positions/snapshot` | `market://crypto/positions/snapshot` |
| `market://{market_id}/structure` | `market://kr_stock/structure` |
| `market://{market_id}/structure/group/{group_id}` | `market://kr_stock/structure/group/SEMICONDUCTOR` |
| `market://{market_id}/signals/summary` | `market://crypto/signals/summary` |
| `market://{market_id}/signals/roles` | `market://crypto/signals/roles` |
| `market://{market_id}/signals/feedback` | `market://crypto/signals/feedback` |
| `market://{market_id}/external/summary` | `market://crypto/external/summary` |
| `market://{market_id}/external/symbol/{symbol}` | `market://crypto/external/symbol/BTC` |
| `market://{market_id}/external/causality` | `market://crypto/external/causality` |
| `market://{market_id}/unified` | `market://crypto/unified` |
| `market://{market_id}/unified/symbol/{symbol}` | `market://crypto/unified/symbol/BTC` |
| `market://{market_id}/derived/regime-transitions` | `market://crypto/derived/regime-transitions` |
| `market://{market_id}/derived/strategy-fitness` | `market://crypto/derived/strategy-fitness` |
| `market://{market_id}/derived/all` | `market://crypto/derived/all` |

`template_resources[*].example` field in `market://meta/discovery` is **copy-paste ready** — the AI gets concrete URIs without having to fill placeholders manually.

**Market IDs**: `crypto`, `kr_stock`, `us_stock` (aliases: `coin`, `kr`, `us`)

## Sample: Trust Layer query

```python
from mcp import Client
client = Client("https://api.oneqaz.com/mcp")

acc = await client.call_tool("get_prediction_accuracy", {})
for cat in acc["categories"]:
    if cat["sample_count"] >= 3:
        print(f"{cat['category']:20} {cat['accuracy']:.1%} (n={cat['sample_count']})")

# Output (example):
# bonds                62.5% (n=24)
# forex                58.3% (n=12)
# vix                  71.4% (n=14)
# ...
```

Every response also carries a plain-text summary:

```json
{
  "_llm_summary": "7/8 macro categories above 55% accuracy, sample sizes 8-24. Bonds + VIX categories most validated."
}
```

## Configuration

All configuration is via environment variables:

| Variable | Default | Description |
|----------|---------|-------------|
| `MCP_SERVER_PORT` | `8010` | Server port |
| `MCP_SERVER_HOST` | `0.0.0.0` | Bind host |
| `MCP_LOG_LEVEL` | `INFO` | Log level |
| `MCP_TIER_RESOLVER` | _unset_ | `module:function` returning tier for an API key (self-host hook) |
| `DB_BACKEND` | `postgres` | Must be `postgres` (SQLite backend retired in 0.4.0) |
| `PG_HOST` / `PG_PORT` | `postgres` / `5432` | PostgreSQL host / port |
| `PG_DB` / `PG_USER` / `PG_PASSWORD` | `auto_trader` / … | PostgreSQL database / credentials (a read-only role is enough) |
| `PG_POOL_MIN` / `PG_POOL_MAX` | `0` / `50` | Per-schema connection pool bounds |
| `PG_STATEMENT_TIMEOUT_MS` | `30000` | Server-side statement timeout |
| `MCP_COIN_DATA_DIR` / `MCP_KR_DATA_DIR` / `MCP_US_DATA_DIR` | auto | Override *logical* data-path roots (see below) |
| `MCP_EXTERNAL_CONTEXT_DATA_DIR` | auto | Override external-context logical root |

## Docker

```bash
docker build -t oneqaz-trading-mcp .
docker run -p 8010:8010 -e DB_BACKEND=postgres -e PG_HOST=... -e PG_PASSWORD=... oneqaz-trading-mcp
```

## Data backend (PostgreSQL)

All data lives in **PostgreSQL 16 + TimescaleDB**, one schema per domain
(`market_coin` / `market_kr` / `market_us`, `market_*_struct`,
`external_context`, `rl_pipeline`, `mcp_analytics`, …). The code still
constructs legacy SQLite-style paths (`.../coin_market/data_storage/trading_system.db`)
but these are **logical routing keys only**: `connect_readonly()` maps each
path to its PG schema and returns a shim connection, so no `.db` files are
read or written. This mirrors the production Wave-I "PG-only" migration —
queries fail loudly instead of silently falling back.

## Rate Limits

Authoritative quotas live in [Access Policy](#access-policy) above. Quick recap:

| Tier | Daily Quota | Burst | Auth |
|------|-------------|-------|------|
| **Free** | 1,500 / day | 60 / min | none / invalid key |
| **Pro** (beta) | 50,000 / day | 200 / min | valid API key |
| **Internal** (owner) | Unlimited | Unlimited | owner-only |
| **Local** (self-hosted) | Unlimited | Unlimited | n/a |

**Response headers** on every request:
- `X-RateLimit-Tier`: resolved tier (`free`/`pro`/`internal`)
- `X-RateLimit-Daily-Limit`: today's ceiling for the resolved tier
- `X-RateLimit-Daily-Remaining`: requests left today
- `X-RateLimit-Minute-Remaining`: requests left this minute
- Exceeding limits returns HTTP 429 with `Retry-After` header.

## Disclaimer

This software is provided for **informational and educational purposes only**. It is **not financial advice**.

- All signals, regime analysis, and market data are generated by automated systems and may contain errors.
- Past performance does not guarantee future results.
- **You are solely responsible for your own investment decisions.** The authors and contributors are not liable for any financial losses incurred from using this software.
- This is not a registered investment advisor, broker-dealer, or financial planner.
- Always do your own research (DYOR) before making any investment decisions.

By using this software, you acknowledge that you understand and accept these terms.

## License

MIT

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