Coder Legion - Recent posts tagged trading

🚨⚖️ THE AI TRADING COURTROOM IS OPEN. AND JEV.AI IS TAKING THE JURY SEAT. 🔥

Wed, 21 Oct 2026 22:21:35 +0000

🚨⚖️ THE AI TRADING COURTROOM IS OPEN. AND JEV.AI IS TAKING THE JURY SEAT. 🔥

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And we're not afraid to put our own technology on trial.

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Our Fractal Trading OS recorded:

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SCRIPTMASTERLABS × JEV.AI

⚖️ EVERY PREDICTION MAKES A CLAIM.
🔥 EVERY CLAIM FACES THE EVIDENCE.
🏛️ THE JURY WILL HAVE ITS SAY.

@TradingView @Stocktwits @Coinbase

ForecastCourt #JevAI #ScriptMasterLabs #AITrading #ArtificialIntelligence #AIAgents #AgenticAI #TradingAI #StockMarket #WallStreet #QuantTrading #MachineLearning #FinTech #Stocktwits #TradingCommunity #AlgorithmicTrading #GME #AMC #IWM #SPY #QQQ #NVDA #TSLA #BuildInPublic #AIInnovation #FinancialTechnology #EvidenceBasedAI #FutureOfTrading

Research and educational technology. JEV.AI jury integration is planned; existing live verdicts are evidence-based rule evaluations. No automated trading or guaranteed returns.

Deriv in Colombia: COP Deposits, Synthetic Indices & Pip Math

Tue, 13 Oct 2026 13:58:06 +0000

Two questions dominate Colombian search around Deriv: does it accept Colombian pesos (COP)? and what are synthetic indices, mathematically? This post answers both with verifiable mechanics — no signals, no account handling, no return claims.

1. COP Deposits: Routes, Not Promises

Deriv is a foreign broker (not supervised by Colombia's SFC — same framework as every international CFD broker in our Colombia brokers guide). For funding from Colombia, the routes that exist in practice are:

Local payment rails (where available): cards and e-wallets that settle in COP and convert at the processor's rate. Availability changes — verify on Deriv's cashier page before assuming anything.
P2P / payment agents: third-party exchangers. Highest counterparty risk of the three; never share account credentials, and test with a minimum amount first.
Crypto on-ramp: fund with crypto, convert inside. Adds a second volatile leg — track the conversion rate separately from trading P&L.

General rule: the COP→USD conversion cost is part of your trading cost. A 2–3% round-trip conversion drag means your strategy must earn that back before it earns anything. Log it like commission.

2. Synthetic Indices: RNG Markets, 24/7

Synthetic indices are audited random-number-generator markets with constant volatility parameters (e.g., Volatility 100 claims roughly constant 100% annualized vol). Consequences:

They trade 24/7 — no sessions, no fundamentals, no closes. Your backtest has no weekend gaps to handle, but also no mean-reverting session effects to exploit.
Price is path-independent noise by design. Trend-following assumptions imported from Forex need re-validation; falsify them on demo before sizing up.
Spikes and jumps are part of the generator spec on crash/boom variants — read the contract specs, don't discover them with real money.

3. Pip and Lot Math on Deriv

# Generic pip-value sketch (verify contract size per symbol on Deriv first)
balance, risk_pct = 500.0, 0.005
stop_pips, pip_value_per_lot = 50.0, 1.0   # EXAMPLE values, not Deriv quotes
lots = (balance * risk_pct) / (stop_pips * pip_value_per_lot)
print(f"Budgeted lots: {lots:.3f}")

Two Deriv-specific traps: (a) contract sizes differ per synthetic — never reuse a Forex lot figure; (b) multipliers/options have built-in stops with different payoff math than spot CFDs. Paper-trade each instrument type separately — our MT5 position sizer explains the clamp bug that silently doubles small-account risk when minimums bind.

4. Taxes and Legality in One Paragraph

Own-account trading with a foreign broker is the same SFC/DIAN framework as everywhere on this site: the SFC does not supervise Deriv, you carry counterparty risk, and profits are declarable income in Colombia (see the DIAN guide). Full mechanics in Spanish: Deriv en Colombia.

Study material under Colombia SFC Decreto 2555 de 2010 — education, not advice. No signals.

By Mahdi Goodarzi (g.dev/mahdigoodarzi), Gueta Quant.

Auditing Broker Execution Latency, FIX Protocol & Asymmetric Slippage in Algorithmic Trading

Mon, 05 Oct 2026 09:41:30 +0000

Most quantitative trading models fail when transitioned from historical backtest to live execution not because the mathematical alpha was flawed, but because the backtesting engine assumed frictionless, zero-latency execution.

In a simulation, your order fills instantly at the exact millisecond bar close. In live market routing, every order traverses network topologies, broker matching engines, liquidity provider aggregation pools, and clearing gateways. During that round-trip window (50ms to 400ms), asset prices move.

When that movement is structurally skewed against you, your edge evaporates to asymmetric slippage.

This engineering paper details how to model, record, and audit broker execution quality programmatically, measure round-trip time (RTT), and verify whether your counterparty operates under enforceable regulatory execution standards.

1. The Zero-Latency Fallacy in Retail Backtests

Retail backtesting frameworks (such as standard vectorbt, TradingView Pine Script, or naive MT5 tick simulations) make three fatal assumptions:

Infinitely Deep Top-of-Book: The entire order volume fills at the posted Best Bid / Best Offer without price impact.
Deterministic Fill Price: Fill Price = Order Price.
Symmetric Slippage Distribution: If slippage occurs, price variance is assumed to be a Gaussian normal distribution centered at zero ($\mu = 0$).

In real-world retail CFD and FX execution, slippage is asymmetric. Market makers and B-book brokers frequently route trades where favorable price movement (positive slippage) during the execution lag is swallowed by the dealer or requoted, while unfavorable price movement (negative slippage) is passed entirely to the client account.

Even a modest execution drag of 0.3 pips per side destroys high-frequency and intraday momentum strategies where the expected payoff per trade is only 1.5 to 2.5 pips.

2. Anatomy of FIX Protocol 4.4 Execution Lag

For algorithmic traders connecting via FIX (Financial Information eXchange) Protocol 4.4 or broker REST/WebSocket APIs, the execution lifecycle consists of discrete network and processing hops:

[Quant Server]                 [Broker Gateway]              [Liquidity Provider]
      |                                |                               |
   T0 |-- NewOrderSingle (35=D) ------>|                               |
      |                                |-- Order Routing (A/B-Book) -->|
      |                                |                               |-- Fill Match
      |                                |<-- ExecutionReport (35=8) ----|
   T1 |<-- ExecutionReport (35=8) -----|                               |
      |    (ExecType=0 New/Ack)        |                               |
      |                                |                               |
   T2 |<-- ExecutionReport (35=8) -----|                               |
      |    (ExecType=F Filled)         |                               |

The critical metric is the Tick-to-Fill Latency ($\Delta T_{fill} = T2 - T0$), composed of:

Network Ingress / Egress: Distance between client VPS and broker server (e.g., Equinix LD4 London or NY4 New York).
Internal Queue Delay: The broker's internal risk check (pre-trade margin validation and book routing).
LP Fill & Acknowledgment: Market execution by external Tier-1 liquidity providers or internal matching engine.

3. Detecting Asymmetric Slippage: The Python Audit Engine

To audit your broker objectively, log the target signal price ($P{target}$), the requested order price ($P{order}$), the actual executed price ($P_{fill}$), and the round-trip latency ($\Delta T$) on every single order.

Here is a standalone Python analytics engine to compute the Execution Drag Index (EDI) and test for statistical asymmetry using the Wilcoxon signed-rank test:

"""
Broker Execution Quality & Asymmetric Slippage Auditor
Gueta Quant Open Research — AGPLv3
"""

import numpy as np
import pandas as pd
from scipy import stats

def audit_broker_execution(trades_df: pd.DataFrame, pip_size: float = 0.0001):
    """
    Analyzes execution records for asymmetric slippage and latency drag.
    
    Expected DataFrame columns:
    - order_type: 'BUY' or 'SELL'
    - target_price: float (price when signal triggered)
    - fill_price: float (actual executed price from broker)
    - rtt_ms: float (round-trip execution time in milliseconds)
    """
    df = trades_df.copy()
    
    # Calculate slippage in pips
    # For BUY: Fill > Target is negative slippage (worse price)
    # For SELL: Fill < Target is negative slippage (worse price)
    is_buy = df['order_type'].str.upper() == 'BUY'
    
    df['slippage_points'] = np.where(
        is_buy,
        df['target_price'] - df['fill_price'],  # Positive if filled better, negative if worse
        df['fill_price'] - df['target_price']
    )
    df['slippage_pips'] = df['slippage_points'] / pip_size
    
    # Segment positive vs negative slippage
    positive_slip = df[df['slippage_pips'] > 0]['slippage_pips']
    negative_slip = df[df['slippage_pips'] < 0]['slippage_pips']
    zero_slip = df[df['slippage_pips'] == 0]['slippage_pips']
    
    total_trades = len(df)
    neg_slip_rate = len(negative_slip) / total_trades if total_trades else 0
    pos_slip_rate = len(positive_slip) / total_trades if total_trades else 0
    
    mean_slippage = df['slippage_pips'].mean()
    median_slippage = df['slippage_pips'].median()
    
    # Asymmetry ratio: Negative volume / Positive volume
    total_neg_pips = abs(negative_slip.sum())
    total_pos_pips = abs(positive_slip.sum())
    asymmetry_ratio = total_neg_pips / (total_pos_pips + 1e-9)
    
    # Statistical Hypothesis Testing: H0: Slippage distribution is symmetric around 0
    # Wilcoxon signed-rank test
    stat, p_value = stats.wilcoxon(df['slippage_pips']) if len(df) >= 20 else (0, 1.0)
    
    # Execution Drag Index (EDI): Average pip drag per 100ms latency
    mean_rtt = df['rtt_ms'].mean()
    edi = (abs(mean_slippage) / (mean_rtt / 100.0)) if mean_rtt > 0 else 0
    
    report = {
        "Total Trades Audited": total_trades,
        "Mean Round-Trip Latency (ms)": round(mean_rtt, 2),
        "Negative Slippage Frequency": f"{neg_slip_rate * 100:.1f}%",
        "Positive Slippage Frequency": f"{pos_slip_rate * 100:.1f}%",
        "Zero Slippage Frequency": f"{(len(zero_slip) / total_trades) * 100:.1f}%",
        "Mean Net Slippage (pips)": round(mean_slippage, 3),
        "Median Slippage (pips)": round(median_slippage, 3),
        "Asymmetry Ratio (Loss/Gain)": round(asymmetry_ratio, 2),
        "P-Value (Asymmetry Significance)": f"{p_value:.5f}",
        "Is Systematically Skewed": (p_value < 0.05 and mean_slippage < 0),
        "Execution Drag Index (pips/100ms)": round(edi, 3)
    }
    
    return report

if __name__ == "__main__":
    # Simulated sample of 200 trades from a questionable dealer:
    np.random.seed(42)
    n = 250
    # Skewed execution: 65% suffer adverse slippage, only 15% get positive
    slips = np.concatenate([
        np.random.exponential(scale=0.4, size=int(n * 0.65)) * -1, # adverse
        np.random.exponential(scale=0.15, size=int(n * 0.15)),      # positive
        np.zeros(int(n * 0.20))                                     # zero slip
    ])
    rtts = np.random.normal(loc=120, scale=30, size=len(slips))
    
    mock_data = pd.DataFrame({
        "order_type": np.random.choice(["BUY", "SELL"], size=len(slips)),
        "target_price": 1.0850,
        "fill_price": 1.0850 - (slips * 0.0001),
        "rtt_ms": np.maximum(20, rtts)
    })
    
    results = audit_broker_execution(mock_data)
    for k, v in results.items():
        print(f"{k:35}: {v}")

If the p-value is below $0.05$ and the Asymmetry Ratio exceeds 2.0x, your broker is not providing symmetric market execution. You are absorbing the costs of a dealing desk.

4. The Regulatory Layer: Offshore Subsidiarity vs. Tier-1 Mandates

Why does this happen predominantly in retail emerging markets (Latin America, Southeast Asia)?

Brokers operating under Tier-1 regimes (such as the UK FCA or Australian ASIC) are legally bound by Best Execution mandates (formerly RTS 27/28 in MiFID II). Under these frameworks, brokers must publish quarterly execution quality metrics and demonstrate that positive slippage is passed to retail clients in full.

However, retail brokers frequently employ Entity Switching:

They market the reputation of their UK FCA or Australian ASIC license.
When an algorithmic trader or resident from Colombia, Mexico, or Chile registers, they are onboarded under a subsidiary domiciled in the Bahamas, Seychelles, or St. Vincent.
Under these offshore subsidiaries, statutory protections (such as the UK FSCS £85,000 compensation fund) and Best Execution auditing are legally null and void.

In Colombia, the Superintendencia Financiera de Colombia (SFC) explicitly notes under Decreto 2555 de 2010 that foreign CFD brokers possess no banking license within Colombian territory, and counterparty risks are assumed 100% by the investor.

To verify whether your broker operates with genuine Tier-1 regulatory backing or an offshore pass-through entity, refer to our comprehensive independent audit:
👉 Brokers Regulados y Autorizados en Colombia: Auditoría Legal y de Ejecución.

5. Pre-Deployment Execution Checklist

Before deploying real capital to an algorithmic strategy:

Audit Contract Multipliers: Confirm SYMBOL_TRADE_CONTRACT_SIZE and SYMBOL_TRADE_TICK_VALUE in MT5 or cTrader to avoid position-sizing explosions on non-USD quote pairs.
Measure Tick Latency to Matching Engine: Ensure VPS latency to the broker's real trading server is $<5\text{ms}$.
Log Fill Discrepancies: Record every fill price against local quote prices during news releases (CPI, NFP) to compute the Execution Drag Index.
Inspect Regulatory Jurisdiction: Always demand proof of account segregation and check the registration number directly on the regulator's portal (register.fca.org.uk or asic.gov.au).

Educational research paper strictly compliant with SFC Colombia Decreto 2555 de 2010. No investment advice, no signals, no managed accounts.

By Mahdi Goodarzi (g.dev/mahdigoodarzi), Founder & Product Builder at Gueta Quant. Explore open-source quantitative toolkits at guetaquant.com.

Algorithmic Risk Management in MT5: ATR Volatility Position Sizing & MQL5 Architecture

Wed, 09 Sep 2026 10:35:32 +0000

Algorithmic Risk Management in MT5: Dynamic Position Sizing & MQL5 Architecture

Most retail trading failures stem from static lot sizing. Trading a fixed 1.0 lot on EUR/USD creates vastly different dollar drawdowns compared to 1.0 lot on Gold (XAU/USD) or NASDAQ (NAS100) due to differing point values, contract multipliers, and underlying volatility regimes.

In professional quantitative finance, position sizing is dynamically governed by the asset Average True Range (ATR) and a fixed risk budget per trade:

\[\\text{Position Size (Lots)} = \\frac{\\text{Account Balance} \\times \\text{Risk \\%}}{\\text{Stop Loss Distance (Points)} \\times \\text{Tick Value}}\]

In this technical guide, we implement an open-source, production-grade MQL5 risk engine that dynamically computes lot sizes and routes orders with slippage protection.

Original interactive tool and web calculator available at GuetaQuant MT5 Position Sizer.

1. MQL5 Dynamic Lot Calculation Class

//+------------------------------------------------------------------+
//|                                             GQ_RiskEngine.mqh    |
//|                             Copyright 2026, Gueta Quant (AGPLv3) |
//|                                       https://guetaquant.com     |
//+------------------------------------------------------------------+
#property copyright "Gueta Quant"
#property link      "https://guetaquant.com"

class CGQRiskManager
{
private:
   string   m_symbol;
   double   m_risk_pct;
   
public:
   CGQRiskManager(string symbol, double risk_pct) : m_symbol(symbol), m_risk_pct(risk_pct) {}
   
   double CalculateLots(double sl_distance_price, double &effective_risk_usd, bool &clamped_min)
   {
      clamped_min = false;
      if (sl_distance_price <= 0) return 0.0;
      
      double balance     = AccountInfoDouble(ACCOUNT_BALANCE);
      double risk_amount = balance * (m_risk_pct / 100.0);
      
      double tick_size   = SymbolInfoDouble(m_symbol, SYMBOL_TRADE_TICK_SIZE);
      double tick_value  = SymbolInfoDouble(m_symbol, SYMBOL_TRADE_TICK_VALUE);
      double min_lot     = SymbolInfoDouble(m_symbol, SYMBOL_VOLUME_MIN);
      double max_lot     = SymbolInfoDouble(m_symbol, SYMBOL_VOLUME_MAX);
      double lot_step    = SymbolInfoDouble(m_symbol, SYMBOL_VOLUME_STEP);
      
      if (tick_size == 0 || tick_value == 0) return 0.0;
      
      double loss_per_lot = (sl_distance_price / tick_size) * tick_value;
      if (loss_per_lot <= 0) return 0.0;
      
      double raw_lots = risk_amount / loss_per_lot;
      
      // Normalize to broker lot step
      double normalized_lots = MathFloor(raw_lots / lot_step) * lot_step;
      
      // Guard against silent risk overshoot on micro accounts
      if (normalized_lots < min_lot) 
      {
         normalized_lots = min_lot;
         clamped_min = true;
      }
      if (normalized_lots > max_lot) normalized_lots = max_lot;
      
      effective_risk_usd = normalized_lots * loss_per_lot;
      return normalized_lots;
   }
};

2. ATR Volatility Trailing Stops

Using historical volatility avoids getting stopped out during normal market noise while protecting capital during structural trend shifts:

double GetATRDistance(string symbol, ENUM_TIMEFRAMES tf, int period, double multiplier)
{
   int handle = iATR(symbol, tf, period);
   if (handle == INVALID_HANDLE) return 0.0;
   
   double atr_val[1];
   if (CopyBuffer(handle, 0, 0, 1, atr_val) <= 0) return 0.0;
   
   return atr_val[0] * multiplier;
}

3. Open Source Tools & Quantitative Ecosystem

All our quantitative research, MQL5 scripts, and risk engines are published under open-source AGPLv3:

Web Calculator & EA Download: GuetaQuant MT5 Position Sizer
MQL5 CodeBase Official Release: CodeBase #77133
44 Open Source Quantitative Tools: GitHub Repository
CERN Zenodo DOI Registry: DOI 10.5281/zenodo.22012203
Local-First Trading Journal: GuetaQuant Journal

About the Author: Mahdi Goodarzi is the founder and quantitative developer at Gueta Quant. Developer profile: g.dev/mahdigoodarzi.

Your LLM Is Guessing Stock Prices. Here's How I Gave Mine a Real-Time Data Tool

Thu, 06 Aug 2026 06:59:01 +0000

A while back I was building a little research agent — nothing fancy, just an LLM that could answer questions about markets so I didn't have to keep five broker tabs open. The first time I asked it "what's Tesla trading at right now?" it answered instantly and confidently: $248.50.

The problem? The actual price that afternoon was nowhere near that. The model wasn't reading a quote. It was pattern-matching on numbers it had seen during training and handing me a plausible-looking hallucination with a straight face.

This is the thing nobody tells you when you start wiring LLMs into real workflows: a language model has no idea what any price is right now. Its weights froze on its training cutoff. Ask it for a live number and it will either refuse or, worse, make one up. For anything time-sensitive — prices, weather, inventory, your own database — the model is only as good as the tools you hand it.

So I gave mine a tool. This post is how I did it, two ways: the five-minute version using an MCP server, and the "build it yourself" version with plain function calling. Code is Python, but the pattern is identical in any language.

The core idea: tools turn "guessing" into "looking it up"

Modern LLMs support tool use (also called function calling). You describe a function — its name, what it does, its parameters — and the model, instead of answering from memory, can decide to call that function. Your code runs it, hands the result back, and the model writes its final answer grounded in real data.

The loop looks like this:

User: "What's NVIDIA trading at?"
  → Model decides: call get_price(symbol="NVDA.US", market="stock")
  → Your code hits a market-data API, gets 183.22
  → Model receives 183.22
  → Model: "NVIDIA is currently trading at $183.22."

The model never invents the number. It asks for it. That single architectural change is the difference between a toy and something you'd actually trust.

The only missing piece is a data source that returns a real-time quote from a plain HTTP call. I ended up using Infoway because one API key covers US stocks, HK, A-shares, Japan, India, crypto, forex, and commodities — so my one get_price tool works across every market without me stitching together three vendors. Any real-time feed works; the pattern below is what matters.

Path A: The five-minute version (MCP server)

If you're using Claude Desktop, Cursor, or Claude Code, you don't have to write any of the tool-calling code. The Model Context Protocol (MCP) is an open standard for exposing tools to AI assistants, and Infoway ships an official MCP server that registers 17 financial-data tools — real-time quotes, candlesticks, market breadth, sector heatmaps, company fundamentals — in one shot.

Install is a single command:

uvx infoway-mcp-server

Then point your assistant at it. For Claude Desktop, edit claude_desktop_config.json (on macOS: ~/Library/Application Support/Claude/, on Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "infoway": {
      "command": "uvx",
      "args": ["infoway-mcp-server"],
      "env": {
        "INFOWAY_API_KEY": "your_api_key_here"
      }
    }
  }
}

The config is identical for Cursor (.cursor/mcp.json) and Claude Code (.claude/settings.json). Restart the client and you can just talk:

"What are Apple and Tesla trading at right now?" → the assistant calls get_realtime_trade with AAPL.US,TSLA.US

"Show me Bitcoin's daily candles for the last 30 days." → it calls get_kline for BTCUSDT

"How's the US market doing today, and which sectors are leading?" → it calls get_market_temperature and get_leading_industries

No glue code, no loop to maintain. This is genuinely the right answer if you just want your existing AI client to stop guessing. But if you're building your own agent — an app, a Slack bot, a backend service — you need the next part.

Path B: Build the tool yourself (function calling)

Here's the same capability wired into your own agent from scratch. I'll use the Anthropic SDK because I find its tool-use API clean, but the shape is the same on any provider that supports function calling.

Step 1: The actual data fetch

First, the boring-but-critical part — a function that gets a real quote. Infoway's trade endpoint is a plain GET; you comma-separate symbols in the path and pass your key as a header. Different asset classes live under different path prefixes (/stock/, /crypto/, /common/ for forex and commodities), so I route on a market argument:

import requests

INFOWAY_KEY = "your_api_key_here"

MARKET_PREFIX = {
    "stock": "stock",     # AAPL.US, 700.HK, 600519.SH
    "crypto": "crypto",   # BTCUSDT, ETHUSDT
    "forex": "common",    # EURUSD, USDJPY
}

def get_price(symbol: str, market: str = "stock") -> dict:
    """Return the latest trade price for one symbol."""
    prefix = MARKET_PREFIX.get(market, "stock")
    url = f"https://data.infoway.io/{prefix}/batch_trade/{symbol}"
    headers = {"apiKey": INFOWAY_KEY, "Accept": "application/json"}

    resp = requests.get(url, headers=headers, timeout=10)
    resp.raise_for_status()
    payload = resp.json()

    if payload.get("ret") != 200 or not payload.get("data"):
        return {"symbol": symbol, "error": payload.get("msg", "no data")}

    quote = payload["data"][0]
    return {
        "symbol": quote["s"],
        "price": float(quote["p"]),
        "timestamp_ms": quote["t"],
    }

Quick sanity check before you let a model anywhere near it:

>>> get_price("NVDA.US", "stock")
{'symbol': 'NVDA.US', 'price': 183.22, 'timestamp_ms': 1781672609412}
>>> get_price("BTCUSDT", "crypto")
{'symbol': 'BTCUSDT', 'price': 96450.1, 'timestamp_ms': 1781672610233}

Step 2: Describe the tool to the model

The model needs a schema so it knows when and how to call your function. Be specific in the description and, crucially, spell out the symbol format — this is where most agents trip up:

TOOLS = [
    {
        "name": "get_price",
        "description": (
            "Get the current real-time trade price for a stock, "
            "cryptocurrency, or forex pair. Use this whenever the user "
            "asks about a current or latest price. Never guess a price."
        ),
        "input_schema": {
            "type": "object",
            "properties": {
                "symbol": {
                    "type": "string",
                    "description": (
                        "Ticker in Infoway format. US stocks: AAPL.US. "
                        "HK: 700.HK. A-shares: 600519.SH / 000001.SZ. "
                        "Crypto: BTCUSDT. Forex: EURUSD."
                    ),
                },
                "market": {
                    "type": "string",
                    "enum": ["stock", "crypto", "forex"],
                },
            },
            "required": ["symbol", "market"],
        },
    }
]

That "Never guess a price" line in the description is doing real work. Paired with a system prompt (below), it's what keeps the model from falling back on its old hallucinating habits.

Step 3: The agent loop

Now the loop that ties it together. The model may want to call the tool, so you keep going until it stops asking:

from anthropic import Anthropic

client = Anthropic()  # reads ANTHROPIC_API_KEY from env

SYSTEM = (
    "You are a market assistant. You do not know any live prices from "
    "memory. Whenever a user asks about a current price, you MUST call "
    "get_price and answer only from its result. Never state a price you "
    "did not fetch."
)

def run_agent(question: str) -> str:
    messages = [{"role": "user", "content": question}]

    while True:
        resp = client.messages.create(
            model="claude-sonnet-5",
            max_tokens=1024,
            system=SYSTEM,
            tools=TOOLS,
            messages=messages,
        )

        if resp.stop_reason != "tool_use":
            # Model is done — return its text answer.
            return "".join(b.text for b in resp.content if b.type == "text")

        # Model asked for one or more tool calls. Run them.
        messages.append({"role": "assistant", "content": resp.content})
        results = []
        for block in resp.content:
            if block.type == "tool_use":
                out = get_price(**block.input)
                results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": str(out),
                })
        messages.append({"role": "user", "content": results})


print(run_agent("Is ETH above 3000 right now? And what's Apple at?"))

Run it and you'll see the model fire off two get_price calls — one for ETHUSDT, one for AAPL.US — then answer with numbers that are actually current:

ETH is at $3,142.08, so yes, it's above 3,000. Apple is trading at $228.51.

That's the whole thing. Roughly 60 lines, one real function, and your agent went from confidently wrong to actually correct.

The parts that bit me (learn from my mistakes)

A working demo and a reliable agent are two different animals. Here's what I got wrong the first time:

1. Symbol format is a silent failure. The API doesn't throw if you send AAPL instead of AAPL.US — it just returns empty data, and the model gets nothing useful. I now put the exact format in the tool description and keep a small alias map ("apple" → "AAPL.US") so the model doesn't have to be perfect. Watch A-shares especially: 600519.SH for Shanghai, 000001.SZ for Shenzhen.

2. The model will still try to guess if you let it. Without the strict system prompt, I caught it "helpfully" answering from memory when a tool call timed out. Make refusal-to-guess an explicit instruction, and treat any tool error as a reason to say "I couldn't fetch that," not to improvise.

3. Markets close. A stock quote from a closed exchange is the last trade, not a live one — that's correct behavior, but your agent should know the difference. The timestamp is right there in the response (timestamp_ms); I feed it to the model so it can say "as of Friday's close" instead of implying the market is open. Crypto and forex don't have this problem (24/5 and 24/7 respectively), which is another reason I like having them behind the same tool.

4. Rate limits are real. Free tiers cap requests (Infoway's free plan is 60/minute). If your agent fans out across a watchlist, batch the symbols — the batch_trade endpoint takes a comma-separated list in one call — instead of looping one request per ticker.

Where this goes next

Once you have one tool wired in, adding more is trivial — the loop doesn't change, you just append to TOOLS. The natural next steps are a get_candles tool for "how has this moved this week," a get_fundamentals tool for valuation questions, or a market-breadth tool for "what's the mood today." (That's exactly the set the MCP server exposes out of the box, if you'd rather not hand-roll each one.)

But the lesson generalizes way past finance. Any time you want an LLM to reason about the present — your database, an internal API, live sensor data — the recipe is the same: stop expecting the model to know, and start giving it the ability to look up. The model's job isn't to be a database. It's to decide when to query one.

If you want to try the market-data version, Infoway has a 7-day free trial with no credit card, which is enough to build and test the whole thing. Grab a key, drop it into either path above, and watch your agent stop making up prices.

FAQ

Does this work with OpenAI / Gemini / open models instead of Claude?
Yes. Every major provider supports function calling with the same three-step shape — describe the tool, let the model request it, feed back the result. Only the SDK method names and the tool-schema field names differ. The get_price function itself doesn't change at all.

Do I need the MCP server and the custom loop, or just one?
Just one. Use the MCP server if you want tools inside an existing assistant (Claude Desktop, Cursor, Claude Code) with zero code. Write the loop if you're building your own app or service where you control the agent.

Why not just let the model browse the web for prices?
Web browsing is slow, flaky, and you're scraping a rendered number you can't validate. A typed API call returns structured data in tens of milliseconds with a real timestamp — far more reliable for anything a user might act on.

How do I stop the model from hallucinating a price when the tool fails?
Two things: a system prompt that forbids stating any un-fetched price, and error handling that returns an explicit error object to the model (not a fabricated fallback). When the model sees an error in the tool result, it'll tell the user it couldn't fetch — which is the honest answer.

Can one tool really cover stocks, crypto, and forex?
Yes, as long as your data source does. That's why I route on a market parameter and use a provider with unified coverage — the model picks the market, the code picks the endpoint, and you maintain one function instead of three integrations.

What's the latency like?
The REST quote call is a single round trip — typically well under a second including the model's turn. If you need sub-100ms streaming (a trading terminal, a live alert engine), switch the data layer to a WebSocket feed; the tool-use pattern on top stays exactly the same.

Preparing Your Business for a Successful Crypto Futures Trading Platform Launch

Wed, 22 Jul 2026 12:25:17 +0000

If you are starting any business, most people will tell you the same thing: plan everything carefully before you begin.

Doing this helps you avoid unexpected issues and costly mistakes after your business is up and running.

The same applies if you plan to launch a crypto futures trading platform.

Take time to understand the market, the technology, and the essential requirements before making any major decisions.

However, many startups skip this step and move forward without enough preparation, which can create challenges later.

If you're unsure what you need before launching a crypto futures trading platform, this blog explains the key things every startup should know.

Read on!

How the Crypto Futures Market Is Creating New Opportunities for Startups

The crypto futures market continues to attract more traders, investors, and businesses every year.
This growing demand creates fresh opportunities for startups to introduce new trading platforms, improve customer experiences, and serve a rapidly expanding global audience.

Growing Demand for Futures Trading

More people are participating in crypto futures trading than ever before. This gives startups a chance to attract active traders with reliable products and practical trading tools.

Expanding Global User Base

Crypto trading is no longer limited to a few regions. Startups can reach customers from different countries by offering multilingual support, multiple payment options, and local compliance.

Rising Interest from Institutional Traders

Investment firms and professional traders are entering the market in larger numbers. This creates opportunities for businesses that provide advanced trading features and dependable platform performance.

Demand for Better Trading Experience

Many traders look for faster order execution, simple navigation, and real-time market data. Startups that focus on these areas can build lasting customer relationships.

New Revenue Opportunities

A crypto futures platform can generate income through trading fees, premium accounts, listing services, and other business models that support long-term growth.

As the crypto futures market continues to expand, startups that understand industry trends and market expectations can build a stronger foundation and create lasting value in an increasingly competitive environment.

Understand the Legal and Regulatory Requirements Before You Launch

Before launching a crypto futures trading platform, it is important to understand the legal and regulatory requirements in the countries where you plan to operate.

Every region has different rules for licensing, customer verification, tax reporting, and financial compliance.

Taking time to research these requirements can help you avoid delays, penalties, or service restrictions later. You should also prepare clear terms of service, privacy policies, and risk disclosures so users understand how your platform works.

Working with legal professionals who have experience in digital assets can help you make informed decisions from the beginning.

Following the right regulations not only supports long-term business growth but also builds confidence among traders, investors, and financial partners who expect responsible platform operations.

Important Steps to Take Before Launching Your Crypto Futures Trading Platform

Launching a crypto futures trading platform takes careful planning and the right preparation.
From legal requirements to technical development, every stage plays an important role in creating a reliable platform that attracts users and supports long-term business growth.

Here's what they found:

Study the Market

Research current trading trends, user preferences, and competitor platforms. This helps identify market gaps and build features that match customer expectations.

Choose the Right Technology

Select a dependable technology stack that supports fast trade execution, stable platform performance, and future feature additions.

Build a User-Friendly Interface

A simple layout makes it easier for beginners and experienced traders to navigate the platform and complete transactions without confusion.

Add Essential Trading Features

Include features such as multiple order types, real-time market charts, wallet integration, and risk management tools to support different trading needs.

Focus on Data Protection

Apply account verification, multi-factor authentication, and regular security checks to help protect user accounts and digital assets.

Test the Platform Thoroughly

Perform detailed testing to identify technical issues before the official release. This improves platform reliability and creates a better experience for users.

Plan Customer Support

Offer responsive support through live chat, email, or ticket systems so users can quickly resolve questions and technical concerns.

Preparing every aspect before launch gives startups a stronger foundation for future growth.
Careful planning and smart decisions make developing a crypto futures trading platform more practical and valuable for both businesses and traders.

Cost of Building a Crypto Futures Trading Software Platform

The cost of building a crypto futures trading software platform depends on its features, trading engine, liquidity integration, wallet support, security, and compliance requirements.

A basic platform typically starts between $20,000 and $40,000.

A mid-level solution with additional trading tools usually ranges from $40,000 to $90,000.

An enterprise-grade platform with advanced functionality, high trading capacity, and custom integrations can cost $100,000 to $150,000 or more.

The final budget depends on your business goals, technical requirements, and long-term expansion plans.

Final Thought

Launching a crypto futures trading platform takes more than a good idea.

It requires careful planning, the right technology, legal compliance, and a clear understanding of what traders expect.

Taking time to prepare these essentials can help your startup avoid common challenges and build a stronger foundation for future growth.

If you are looking for an experienced technology partner, Hashcodex can help you build a crypto futures trading platform that matches your business goals.

Their team builds crypto futures trading platforms based on your business goals and provides support from planning and development to deployment and ongoing maintenance.

Driving TradingView Desktop From an AI Agent

Sat, 02 May 2026 14:21:08 +0000

Anyone who's tried AI-assisted trading research has hit the same wall.

The agent has no native access to your charts. You end up copy-pasting symbols, indicator values, screenshots, and Pine Script back and forth between TradingView and Claude or Cursor. The tools that try to fix this fall into two camps: route market data through a third-party API (added latency, added cost, their interpretation of your bars), or pollute your TradingView chart with helper indicators just so an agent can read them back.

There's a third path that's more obvious in retrospect: drive your local TradingView Desktop through the Chrome DevTools Protocol that the app already exposes on port 9222 when you launch it with --remote-debugging-port=9222. The agent talks to the same TradingView you already use, reads what your chart actually shows, executes Pine Script through TradingView's own runtime. Same data, same indicators, same features, just with an agent in the loop.

A project called tradesdontlie/tradingview-mcp started down this path. Then it stalled with TV Desktop 3.1.0 incompatibilities and a handful of bugs, and the maintainer went quiet on PRs. I forked it, and now 71 commits later just tagged 1.0.0 of the fork. This post covers what I added and what the tool doesn't do.

What's in 1.0.0

The release is at https://github.com/iliaal/tradingview-mcp/releases/tag/1.0.0 with the per-tool changelog. The high-level numbers:

78 → 96 tools (18 new MCP tools)
A tv CLI (30 commands, 66 subcommands) mirroring the MCP surface
TV Desktop 3.1.0 compatibility across the whole surface
338 offline tests (pattern detection, multi-timeframe, replay, CLI routing)
Removed ui_evaluate from the upstream surface (it accepted arbitrary JavaScript in the user's authenticated TV session)

Pine Script Lifecycle

The original project let an agent inspect Pine Script, but only barely. Editing, saving, switching, and version history all required the user to click through TradingView's UI. The 1.0.0 fork closes that gap with eight new tools:

pine_save_as and pine_rename for naming and copying scripts
pine_version_history, pine_delete, pine_switch_script for managing the Pine library
pine_smart_compile (auto-detects whether to add or update an indicator on the chart; returns elapsed_ms)
pine_analyze (offline static analysis; catches typos, unused vars, deprecated patterns before compile)
pine_check (server-side compile without putting the script on a chart; useful for CI-style validation)

The pine_check + pine_analyze pair is the unlock for AI-assisted Pine debugging. The agent writes a draft, runs pine_analyze for cheap structural checks, then pine_check for the real compile, reads errors, fixes, repeats. The user never has to add an unfinished script to the chart just to see whether it compiles.

Pine Drawing Readers

This is the part I keep using most. Five new tools let the agent read what a Pine indicator actually drew, after it's running on the chart:

data_get_pine_lines: horizontal price levels (support, resistance, trend lines)
data_get_pine_labels: text annotations (signal labels, divergence callouts)
data_get_pine_tables: table cells (stats panels, multi-symbol scanners)
data_get_pine_boxes: price zones (supply, demand, order blocks)
data_get_pine_shapes: plotshape and plotchar markers

Default behavior deduplicates and caps output (50 labels by default). Override with verbose=true or max_labels=N for cases where the agent needs the raw set.

Why this matters: agents writing Pine Script could previously check whether the script compiled, but couldn't verify whether the logic was correct. Now the agent can write an indicator, compile it, and read the labels back to confirm the right bars were tagged. Without this, AI-assisted Pine work stops at "syntax is fine, hope you wrote what you meant."

This also replaces the workflow that an agent would otherwise fall back to: screenshot the chart, run OCR over the image, hope the agent pieces together what the indicator drew. That path is slow (a screenshot round-trip plus OCR latency on every read), unreliable (overlapping labels and thin lines confuse the OCR), and expensive (chart images in the agent's context burn thousands of tokens before any reasoning happens). The structured readers skip all three problems.

Pine indicators rarely live alone. A real chart layout might have three panes (price + RSI + volume) and four tabs, one per ticker. The original project assumed a single chart context. The fork adds eleven tools that make the layout addressable:

pane_list, pane_focus, pane_set_layout, pane_set_symbol, pane_set_timeframe
pane_read_batch (single call that reads across all visible panes)
tab_list, tab_new, tab_close, tab_switch, tab_switch_by_name

pane_read_batch was the practical motivator: agents that need to compare indicator values across panes used to need 5+ round-trips. The batch reader collapses that to one CDP call, which matters when the agent is iterating quickly.

A non-obvious detail: pane focus needs a 300ms wait after pane.focus() for _activeChartWidgetWV to update, required since TV 3.1.0. The fork bakes this in so users don't have to discover it.

Multi-Timeframe and Pattern Detection

data_get_multi_timeframe reads indicator values across a list of timeframes (W → D → 4H → 1H → 15m alignment) in one call. It saves and restores the original timeframe, so the agent gets multi-timeframe context without disrupting the user's view.

data_detect_candlestick_patterns runs 17 classic patterns over raw OHLC bars: doji, hammer, hanging_man, inverted_hammer, shooting_star, marubozu, spinning_top, bullish_engulfing, bearish_engulfing, bullish_harami, bearish_harami, piercing_line, dark_cloud_cover, morning_star, evening_star, three_white_soldiers, three_black_crows. Each match returns the pattern name, a direction (bullish/bearish/neutral), and a strength score.

The point of doing pattern detection natively, instead of via a Pine indicator overlay, is chart hygiene. Pattern detection informs the agent; the user never wants to see it plotted on the chart. Running it over OHLC bars directly means no Pine indicator gets added to the chart, no scripts get pushed to TV's Save dialog, and the user's chart stays clean.

The `tv` CLI

The MCP surface is meant for AI agents. But the same operations are useful from a shell, and shipping both interfaces from the same code path (instead of building a separate CLI program) keeps them in sync.

tv ships 30 commands with 66 subcommands. A few examples:

tv quote get AAPL                          # current quote
tv data ohlcv AAPL --tf 1H --bars 200      # OHLCV bars
tv pine check ./my_indicator.pine          # offline compile
tv pine smart_compile ./my_indicator.pine  # add or update on chart
tv hotlist volume_gainers --country US     # scanner
tv stream all                              # poll-and-diff JSONL output

tv stream polls the chart and emits JSONL whenever something changes: a new bar, an indicator value update, a new label drawn. Useful for piping into tail-and-watch workflows or for sanity-checking a strategy as it runs.

Cross-Platform Launch

tv_launch auto-detects native macOS, Linux, Windows, and Windows MSIX (Microsoft Store) installs of TradingView Desktop, resolves Windows paths correctly when invoked from WSL2, and handles the case where a binary refuses --remote-debugging-port from a direct spawn. macOS Electron 38 is the worst offender there; the fix is to fall back to open -a with the port flag in --args.

The motivation for sinking this much code into launch behavior is operational: in a CI-like setup or a containerized agent loop, you want tv_ensure to be a no-op when TV is already running and a clean launch when it isn't, on whichever OS the agent happens to be on. The original project assumed the user had TradingView running and never had to think about how it got there.

A Quiet Architectural Change

One refactor: per-call _deps dependency injection across ten core modules (chart, data, drawing, pane, pine, replay, ui, watchlist, alerts, capture).

Before: each module imported connection.js directly. State lived in module globals. Tests had to monkey-patch connection.js to swap CDP behavior.

After: each function accepts a _deps parameter, so the test harness passes in a mock CDP wrapper without touching globals. The installCdpMocks and mockEvaluateFromTable helpers in tests/helpers/mock-cdp.js give every test an isolated environment.

Practical result: 338 offline tests run in seconds without needing TradingView Desktop open. The previous test surface was much smaller because anything beyond a unit test required the real app running. The DI refactor unlocked the test count, which is what makes the 3.1.0 compat fixes safe to ship.

What Was Removed: `ui_evaluate`

The upstream had a tool called ui_evaluate that accepted arbitrary JavaScript and ran it in the authenticated TradingView session. Anyone with MCP access to the agent had effective full read/write access to the user's TradingView account state: watchlists, alerts, saved scripts, anything the session could see.

Dropped it. There's no use case for arbitrary JavaScript that isn't better served by a specific tool with a narrow boundary. If you need to read or modify something the existing tools don't cover, that's a missing tool, not a reason to expose the JS runtime.

What It Doesn't Do

The agent talks to your running TradingView Desktop via CDP. There's no API spoofing, no auth bypass, no headless TV. If you don't have TV Desktop installed, the tool can't help you.

TradingView's free tier covers most of the lifecycle tools. Some features need paid tiers (multi-pane, replay mode, second-resolution data, more indicators per chart). The tools wrap whatever the local TV exposes; they don't unlock features you don't have.

It doesn't bypass anything. It doesn't get you data TradingView doesn't already give you. The improvement is the agent surface, not the data access.

Install

git clone https://github.com/iliaal/tradingview-mcp
cd tradingview-mcp
npm install

Add the server to ~/.claude/.mcp.json (or your MCP config). Launch TradingView with --remote-debugging-port=9222. The README has the paste-into-Claude-Code one-liner that covers the agent-side wiring.

How to Add Real-Time Orderflow Intelligence to Your Freqtrade Bot in 10 Lines of Python

Sat, 25 Apr 2026 06:59:58 +0000

How to Add Real-Time Orderflow Intelligence to Your Freqtrade Bot in 10 Lines of Python

Stop letting RSI lie to your bot. Here's how to give it actual market intelligence.

The Problem Every Freqtrade User Knows

You've tuned your strategy. The backtests look great. You go live — and your bot walks straight into a trap.

The RSI said oversold. The EMA said buy. Everything looked perfect.

But something in the orderbook was screaming danger — and your bot had no way to hear it.

What Actually Happened

I ran a live experiment: I fed the same market data to both traditional indicators and a real-time L2 orderbook analyzer simultaneously, then recorded what each one saw.

Here's the exact 60-second timeline on BTCUSDT:

Time	Orderflow Signal	RSI	What Was Happening
22:03:40	`BUY_PRESSURE` / delta_accel: 0.4	43	Institutional buying detected
22:03:49	`SELL_PRESSURE` / delta_accel: 10.4	43	26x selling spike — RSI silent
22:03:57	`NEUTRAL` / delta_accel: 2.37	43	Absorption phase begins
22:04:15	`BUY_PRESSURE` / delta_accel: 0.05	43	Buyers returning

RSI stayed at 43 the entire time.

While a complete institutional selling cycle — spike, absorption, recovery — happened in the orderbook, the indicator that millions of bots rely on did not move a single point.

This is not a bug. This is how lagging indicators work by design.

The Fix: Pre-Entry Orderflow Confirmation

The solution isn't to replace your strategy. It's to add one layer of reality-checking before any order is submitted.

Freqtrade has a perfect hook for this: confirm_trade_entry().

This function is called exactly once, right before Freqtrade submits a buy order. If it returns False, the trade is blocked. If it returns True, execution proceeds.

Here's the complete integration:

Step 1: Install

pip install horus-flow-mcp requests

Get your free API key at RapidAPI — Horus Flow Intelligence

Step 2: The Connector (save as `horus_connector.py`)

import requests
import logging
from typing import Optional

logger = logging.getLogger("HorusConnector")

class HorusConnector:
    """
    Pre-trade orderflow filter for Freqtrade.
    Blocks entries when institutional orderflow is adversarial.
    Fail-OPEN: if API is unreachable, trade proceeds normally.
    """

    DANGER_SIGNALS = {
        "SELL_PRESSURE",
        "LIQUIDITY_EVENT",
        "EMERGENCY_DUMP",
        "BAILOUT"
    }

    ENTRY_SIGNALS = {
        "BUY_PRESSURE",
        "BUY_ABSORPTION"   # Strongest signal: whales absorbing sell pressure
    }

    def __init__(self, api_key: str, min_confidence: float = 0.70):
        self.min_confidence = min_confidence
        self.headers = {
            "x-rapidapi-key": api_key,
            "x-rapidapi-host": "horus-flow-intelligence.p.rapidapi.com"
        }
        self.base_url = "https://horus-flow-intelligence.p.rapidapi.com"

    def is_safe_to_enter(self, symbol: str) -> bool:
        """
        Returns True only when orderflow confirms favorable conditions.
        Returns True on API failure (fail-open = don't block on network issues).
        """
        symbol = symbol.replace("/", "").upper()

        try:
            response = requests.get(
                f"{self.base_url}/v1/flow/crypto/{symbol}",
                headers=self.headers,
                timeout=2
            )
            data = response.json()

            signal = data.get("signal", "NEUTRAL")
            confidence = data.get("confidence", 0)
            flags = data.get("metrics", {}).get("flags", [])

            # Hard block: dangerous market conditions
            if signal in self.DANGER_SIGNALS:
                logger.info(f"[Horus ] {symbol} blocked — {signal} ({confidence:.0%})")
                return False

            # Hard block: spoofing detected regardless of signal
            if any("SPOOFING_DETECTED" in f for f in flags):
                logger.info(f"[Horus ] {symbol} blocked — Spoofing: {flags}")
                return False

            # Green light: confirmed institutional buy-side activity
            if signal in self.ENTRY_SIGNALS and confidence >= self.min_confidence:
                logger.info(f"[Horus ✅] {symbol} confirmed — {signal} ({confidence:.0%})")
                return True

            # Gray zone: neutral or low conviction
            logger.info(f"[Horus ⏳] {symbol} waiting — {signal} ({confidence:.0%})")
            return False

        except Exception:
            # Network issue: fail-open, let Freqtrade decide
            return True

Step 3: Add to Your Strategy (10 lines)

import os
from horus_connector import HorusConnector
from freqtrade.strategy import IStrategy
from pandas import DataFrame

class HorusFilteredStrategy(IStrategy):
    """
    Any existing strategy + Horus orderflow pre-entry confirmation.
    Replace populate_indicators/entry/exit with your own logic.
    """

    timeframe = "5m"
    minimal_roi = {"0": 0.03}
    stoploss = -0.02

    # Initialize Horus — reads key from environment variable
    horus = HorusConnector(
        api_key=os.getenv("RAPIDAPI_KEY", ""),
        min_confidence=0.70
    )

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Your existing indicator logic here
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Your existing entry conditions here
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Your existing exit conditions here
        return dataframe

    def confirm_trade_entry(self, pair: str, **kwargs) -> bool:
        """
        ← This is the only addition to your existing strategy.
        Horus checks the live orderbook before every single entry.
        If orderflow is adversarial, the trade is blocked.
        """
        return self.horus.is_safe_to_enter(pair)

Step 4: Run

export RAPIDAPI_KEY="your_key_here"
freqtrade trade --strategy HorusFilteredStrategy --dry-run

What This Does in Practice

Your bot's existing logic still generates entry signals. Nothing changes there.

The only difference: before Freqtrade submits the order, it now asks one additional question — "What is the live orderbook actually saying right now?"

If the answer is SELL_PRESSURE, LIQUIDITY_EVENT, or SPOOFING_DETECTED, the trade is blocked.
If the answer is BUY_PRESSURE or BUY_ABSORPTION with sufficient confidence, execution proceeds.
If the answer is NEUTRAL or low confidence, the system waits.

Here's what the logs look like:

[Horus ✅] ETHUSDT confirmed — BUY_ABSORPTION (90%)
[Horus ] BTCUSDT blocked — SELL_PRESSURE (85%)
[Horus ] SOLUSDT blocked — Spoofing detected
[Horus ⏳] ADAUSDT waiting — NEUTRAL (60%)

What Selective Filtering Looks Like in Practice

In a typical live session, the system scans thousands of assets continuously. Of all the technical signals generated, only a small fraction pass the orderflow confirmation layer.

This low pass-rate is not a bug — it's the filter working correctly.

Most market conditions are not suitable for entry. A bot that knows when not to trade is more valuable than one that trades constantly. Every blocked entry is a potential loss avoided.

The logs tell the story:

[Horus ✅] ETHUSDT confirmed — BUY_ABSORPTION (90%)  → trade executed
[Horus ] BTCUSDT blocked — SELL_PRESSURE (85%)    → entry skipped
[Horus ] SOLUSDT blocked — LIQUIDITY_EVENT (99%)  → entry skipped
[Horus ⏳] ADAUSDT waiting — NEUTRAL (60%)           → no conviction

The ratio of blocked to confirmed entries will vary by market condition. In volatile or manipulated markets, the filter becomes more selective. In clean trending conditions, more signals pass through.

Understanding the Key Signals

Signal	What It Means	Action
`BUY_PRESSURE`	Institutional aggression on ask side	✅ Allow entry
`BUY_ABSORPTION`	Whales absorbing sell pressure — strongest reversal signal	✅ Allow entry
`SELL_PRESSURE`	Institutional selling dominant	Block entry
`LIQUIDITY_EVENT`	Flash crash setup detected	Block entry
`EMERGENCY_DUMP`	Aggressive institutional exit	Block entry
`SPOOFING_DETECTED`	Fake bid/ask walls placed to mislead	Block entry
`NEUTRAL`	No clear institutional intent	⏳ Wait

What BUY_ABSORPTION Actually Looks Like

This is the signal worth understanding in depth. During a period when BTC and SOL were both showing SELL_PRESSURE, ETH showed this:

{
  "symbol": "ETHUSDT",
  "signal": "BUY_ABSORPTION",
  "confidence": 0.80,
  "market_state": "COUNTER_TREND",
  "metrics": {
    "bid_ask_ratio": 21.32,
    "buy_sell_ratio": 0.78,
    "whale_activity": true
  }
}

bid_ask_ratio: 21.32 means there's 21x more bid liquidity than ask. Someone is systematically absorbing every sell order. RSI at this moment was in the low 40s — suggesting bearish conditions.

A bot running on indicators alone would see no reason to buy.
A bot reading the orderbook would see institutions quietly loading.

Tuning the Filter

The min_confidence parameter controls how selective the filter is:

# Conservative — only high-conviction signals pass
horus = HorusConnector(api_key=key, min_confidence=0.80)

# Balanced — good for most strategies
horus = HorusConnector(api_key=key, min_confidence=0.70)

# Permissive — catches more opportunities, more noise
horus = HorusConnector(api_key=key, min_confidence=0.60)

Start at 0.70 and adjust based on your strategy's characteristics.

Backtest Safety

One important note: confirm_trade_entry() is only called in live and dry-run modes. During backtesting, Freqtrade skips it entirely — so your backtests won't be affected, and you won't get errors from missing API calls.

This is by design. Orderflow data from the past doesn't exist in the way historical OHLCV data does. The filter is a live tool only.

Getting Started

# Install
pip install horus-flow-mcp requests

# Get free API key (500 calls/month on free tier)
# https://rapidapi.com/horustechltd/api/horus-flow-intelligence

# Clone the strategy template
git clone https://github.com/horustechltd/freqtrade-horus-strategy

# Run dry
export RAPIDAPI_KEY="your_key"
freqtrade trade --strategy HorusFlowStrategy --dry-run

Free tier: 500 API calls/month — enough for testing and low-frequency strategies.
Pro tier ($29/month): 100,000 calls — suitable for active bots across multiple pairs.

What's Next

If you want to go deeper:

Emergency exit protection: Use custom_exit() to close positions when LIQUIDITY_EVENT is detected mid-trade
US Equities: The same API works for stocks — get_equity_flow("NVDA") returns institutional orderflow for US markets
Multi-pair scanner: scan_crypto_flow() checks multiple symbols in one call — useful for portfolio-level decisions

The full strategy template with emergency exit logic is available at:
github.com/horustechltd/freqtrade-horus-strategy

All trade data and orderflow readings in this article come from live production systems. Past performance does not guarantee future results. This is not financial advice.

About the author:
Ashraf Hanafy is the founder of Horus Tech Ltd, building institutional-grade market microstructure intelligence for AI trading agents.

GitHub: horustechltd
MCP Server: pip install horus-flow-mcp
LinkedIn: Ashraf Hanafy

Adaptive Local Linear Regression for Short-Term Growth Stock Trend-Following in Python

Sat, 11 Apr 2026 21:02:57 +0000

Most trend-following systems apply a fixed lookback window — a 20-day moving average, a 50-day exponential smoother — uniformly across all market conditions. The problem is that volatility regimes change, momentum cycles compress and expand, and a static window that works in trending markets bleeds badly during choppy ones. Adaptive local linear regression addresses this by dynamically adjusting the bandwidth of the regression kernel based on local price behavior, fitting the trend estimate to the data rather than forcing the data into a predetermined frame.

In this article, we implement an adaptive local linear regression trend-following strategy applied specifically to growth stocks — securities where momentum is structurally more persistent but also more violently mean-reverting when it breaks. We will build the full signal pipeline from scratch: kernel-weighted regression, bandwidth selection via local volatility scaling, entry and exit logic, and a vectorized backtest with performance metrics. Everything runs on live data pulled from Yahoo Finance.

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This article covers:

Section 1 — Core Concept:** What local linear regression is, how adaptive bandwidth works, and why growth stocks are the right testing ground
Section 2 — Python Implementation:** Full pipeline from data fetch through signal generation, backtest, and visualization (Sections 2.1–2.4)
Section 3 — Results and Analysis:** What the strategy produces, how to read the performance output, and realistic expectations
Section 4 — Use Cases:** Where this technique fits in a broader quant workflow
Section 5 — Limitations and Edge Cases:** Where the model breaks down and what to watch for

1. Local Linear Regression as an Adaptive Trend Filter

A simple moving average treats every observation in its window equally — the price from 19 days ago carries the same weight as yesterday's close. A linear regression over a rolling window is slightly better: it fits a slope through recent prices, giving you both a trend level and a rate of change. But both approaches share the same structural flaw — the window is fixed, and the market does not care about your window.

Local linear regression (LLR) solves this with a kernel function. Instead of equal weights, each observation receives a weight that decays with distance from the current point. The most common choice is a Gaussian or Epanechnikov kernel. The fitted value at any point is the intercept of a weighted least-squares regression, where nearby observations dominate and distant ones fade. The key parameter is bandwidth — the width of the kernel, which controls how local or global the fit is.

Adaptive bandwidth takes this one step further. Rather than setting bandwidth to a fixed constant, you tie it to a local measure of variability — typically realized volatility over a short rolling window. In high-volatility regimes the bandwidth widens, smoothing over noise. In low-volatility, trending regimes it narrows, making the filter more responsive. The result is a trend estimate that breathes with the market rather than fighting it. Conceptually, it is similar to how a human trader instinctively ignores small fluctuations during a choppy consolidation but pays close attention to each tick when price is breaking out cleanly.

Growth stocks amplify every characteristic that makes this technique interesting. Their trends are steeper, their volatility is higher, and their regime changes are sharper. A fixed-window system applied to a basket of high-beta growth names will over-smooth the entries in trending phases and over-trade the noise in consolidation. Adaptive LLR is structurally well-suited to this behavior — the bandwidth automatically compresses when a stock is trending cleanly and expands when it enters a volatile, directionless phase.

2. Python Implementation

2.1 Setup and Parameters

The strategy has four key parameters. BANDWIDTH_BASE sets the baseline kernel width in days. VOL_LOOKBACK controls the rolling window for computing local realized volatility used in the adaptive scaling. VOL_SCALE determines how aggressively bandwidth responds to volatility changes — higher values produce wider smoothing in noisy regimes. SIGNAL_THRESHOLD is the minimum normalized slope required to trigger a long entry.

import numpy as np
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
from scipy.linalg import lstsq
import warnings
warnings.filterwarnings("ignore")

# --- Strategy Parameters ---
TICKERS        = ["QQQ", "ARKK", "SOXX", "IGV", "WCLD"]  # growth proxies
START_DATE     = "2020-01-01"
END_DATE       = "2024-12-31"
BANDWIDTH_BASE = 15       # baseline kernel bandwidth (days)
VOL_LOOKBACK   = 21       # rolling window for realized vol estimate
VOL_SCALE      = 2.0      # sensitivity of bandwidth to local volatility
SIGNAL_THRESHOLD = 0.003  # minimum daily slope (normalized) to go long
TRANSACTION_COST = 0.001  # one-way cost per trade (0.1%)

2.2 Adaptive Local Linear Regression Signal Engine

This function computes the LLR fitted value and slope at every point in a price series. For each index position, it builds a Gaussian kernel centered at that point with a bandwidth scaled by local volatility — wider when the recent vol is high, narrower when conditions are calm. It then solves a weighted least-squares problem to extract the local slope, which becomes the raw trend signal.

def adaptive_llr_signals(prices: pd.Series,
                         bandwidth_base: int = BANDWIDTH_BASE,
                         vol_lookback: int = VOL_LOOKBACK,
                         vol_scale: float = VOL_SCALE) -> pd.DataFrame:
    """
    Compute adaptive local linear regression fitted values and slopes.
    Returns a DataFrame with columns: ['fitted', 'slope', 'bandwidth'].
    """
    log_prices = np.log(prices.values)
    n = len(log_prices)
    t = np.arange(n, dtype=float)

    # Realized volatility (rolling std of log returns)
    log_returns = np.diff(log_prices, prepend=log_prices[0])
    vol_series  = (pd.Series(log_returns)
                   .rolling(vol_lookback, min_periods=5)
                   .std()
                   .fillna(method="bfill")
                   .values)

    # Normalize vol to get a bandwidth multiplier centered at 1.0
    vol_mean      = vol_series.mean()
    vol_norm      = vol_series / (vol_mean + 1e-10)
    bandwidths    = bandwidth_base * (1 + vol_scale * (vol_norm - 1))
    bandwidths    = np.clip(bandwidths, bandwidth_base * 0.3,
                            bandwidth_base * 3.0)

    fitted    = np.zeros(n)
    slopes    = np.zeros(n)

    for i in range(n):
        h   = bandwidths[i]
        u   = (t - i) / h
        w   = np.exp(-0.5 * u ** 2)        # Gaussian kernel weights
        w  /= w.sum()

        W   = np.diag(w)
        X   = np.column_stack([np.ones(n), t - i])   # [intercept, time]
        XtW = X.T @ W
        try:
            coeffs, _, _, _ = lstsq(XtW @ X, XtW @ log_prices)
            fitted[i]  = coeffs[0]
            slopes[i]  = coeffs[1]          # daily log-return slope
        except Exception:
            fitted[i]  = log_prices[i]
            slopes[i]  = 0.0

    return pd.DataFrame({
        "fitted":    fitted,
        "slope":     slopes,
        "bandwidth": bandwidths
    }, index=prices.index)

2.3 Backtest Engine with Signal-to-Position Logic

The backtest converts slopes into binary positions. A long signal fires when the normalized slope exceeds SIGNAL_THRESHOLD. Positions are held until the slope falls below zero — no partial sizing, no leverage. Transaction costs are applied on every position change. We compute cumulative returns, maximum drawdown, and a Sharpe ratio for each ticker.

def run_backtest(ticker: str) -> dict:
    raw  = yf.download(ticker, start=START_DATE, end=END_DATE,
                       auto_adjust=True, progress=False)
    px   = raw["Close"].squeeze().dropna()

    sig_df   = adaptive_llr_signals(px)
    slope    = sig_df["slope"]

    # Binary long/flat position
    position = (slope > SIGNAL_THRESHOLD).astype(float)
    position = position.shift(1).fillna(0)   # avoid look-ahead

    daily_ret      = px.pct_change().fillna(0)
    trade_flag     = position.diff().abs().fillna(0)
    strategy_ret   = position * daily_ret - trade_flag * TRANSACTION_COST

    cum_strategy   = (1 + strategy_ret).cumprod()
    cum_bh         = (1 + daily_ret).cumprod()

    # Sharpe (annualized, risk-free = 0 for simplicity)
    sharpe = (strategy_ret.mean() / strategy_ret.std()) * np.sqrt(252)

    # Max drawdown
    roll_max   = cum_strategy.cummax()
    drawdown   = (cum_strategy - roll_max) / roll_max
    max_dd     = drawdown.min()

    return {
        "ticker":       ticker,
        "prices":       px,
        "signals":      sig_df,
        "position":     position,
        "strat_ret":    cum_strategy,
        "bh_ret":       cum_bh,
        "sharpe":       sharpe,
        "max_dd":       max_dd,
        "total_return": cum_strategy.iloc[-1] - 1
    }

results = {t: run_backtest(t) for t in TICKERS}

2.4 Visualization

The chart overlays the raw price, the adaptive LLR fitted curve, and shaded long-signal regions for the selected ticker. The second panel plots the local slope with a dashed threshold line, making it easy to see exactly when and why positions are entered and exited.

def plot_strategy(result: dict, ticker: str):
    px     = result["prices"]
    fitted = np.exp(result["signals"]["fitted"])
    slope  = result["signals"]["slope"]
    pos    = result["position"]

    fig, axes = plt.subplots(3, 1, figsize=(14, 10),
                              gridspec_kw={"height_ratios": [3, 1.5, 1.5]})
    plt.style.use("dark_background")
    fig.patch.set_facecolor("#0d0d0d")
    for ax in axes:
        ax.set_facecolor("#0d0d0d")

    # Panel 1 — Price and fitted trend
    axes[0].plot(px.index, px.values, color="#4a90d9", lw=1.0,
                 alpha=0.7, label="Price")
    axes[0].plot(px.index, fitted, color="#f5a623", lw=1.8,
                 label="Adaptive LLR Fit")
    axes[0].fill_between(px.index, px.values.min(), px.values,
                         where=(pos > 0), alpha=0.12,
                         color="#7ed321", label="Long Signal")
    axes[0].set_title(f"{ticker} — Adaptive LLR Trend Filter",
                      color="white", fontsize=13)
    axes[0].legend(loc="upper left", fontsize=9)
    axes[0].set_ylabel("Price (USD)", color="white")

    # Panel 2 — Local slope
    axes[1].plot(slope.index, slope.values, color="#bd10e0", lw=1.2)
    axes[1].axhline(SIGNAL_THRESHOLD, color="#f5a623", ls="--",
                    lw=0.9, label=f"Entry threshold ({SIGNAL_THRESHOLD})")
    axes[1].axhline(0, color="white", ls=":", lw=0.6, alpha=0.5)
    axes[1].set_ylabel("LLR Slope", color="white")
    axes[1].legend(loc="upper left", fontsize=9)

    # Panel 3 — Cumulative returns
    axes[2].plot(result["strat_ret"].index, result["strat_ret"].values,
                 color="#7ed321", lw=1.5, label="Strategy")
    axes[2].plot(result["bh_ret"].index, result["bh_ret"].values,
                 color="#4a90d9", lw=1.0, alpha=0.7, label="Buy & Hold")
    axes[2].set_ylabel("Cum. Return", color="white")
    axes[2].legend(loc="upper left", fontsize=9)

    for ax in axes:
        ax.tick_params(colors="white")
        for spine in ax.spines.values():
            spine.set_edgecolor("#333333")

    plt.tight_layout()
    plt.savefig(f"{ticker}_adaptive_llr.png", dpi=150,
                bbox_inches="tight", facecolor="#0d0d0d")
    plt.show()

plot_strategy(results["QQQ"], "QQQ")

# Print summary table
print(f"\n{'Ticker':<8}{'Total Return':>14}{'Sharpe':>10}{'Max DD':>10}")
print("-" * 44)
for t, r in results.items():
    print(f"{t:<8}{r['total_return']:>13.1%}{r['sharpe']:>10.2f}"
          f"{r['max_dd']:>10.1%}")

Figure 1. QQQ price overlaid with the adaptive LLR trend estimate (orange), long signal regions shaded in green, local slope in the middle panel, and cumulative strategy vs. buy-and-hold in the bottom panel — the slope panel reveals the exact entry and exit logic at each regime transition.

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3. Results and Strategy Analysis

Running this pipeline across QQQ, ARKK, SOXX, IGV, and WCLD over 2020–2024 produces a clear bifurcation. On trending instruments — QQQ and SOXX in particular — the adaptive bandwidth consistently compresses during clean directional moves, making the slope signal responsive enough to catch momentum legs early. On high-volatility names like ARKK, the bandwidth widens during the 2021–2022 drawdown cycle, which correctly suppresses false entries during the violent whipsaws of that period.

The Sharpe ratio advantage over a naive buy-and-hold varies by ticker but tends to be most pronounced on the semiconductor and software ETFs, where sector momentum has historically been autocorrelated over 10–30 day windows. The strategy is not designed to beat buy-and-hold on total return in a persistent bull run — it will miss portions of extended uptrends during re-entry delays. What it does provide is a materially better return-per-unit-of-risk profile and significantly smaller maximum drawdown, because the flat position during negative slope regimes keeps the portfolio out of the worst decline phases.

The slope threshold parameter (SIGNAL_THRESHOLD = 0.003) is the most sensitive tuning variable. Set it too low and the strategy over-trades sideways markets. Set it too high and it misses the early phase of genuine breakouts. A practical calibration approach is to run the backtest across a grid of threshold values and select the one that maximizes Sharpe on an out-of-sample validation window — never on the full in-sample period.

4. Use Cases

Portfolio overlay signal: The LLR slope can serve as a regime filter on top of an existing long-only equity strategy — reduce gross exposure when slope turns negative across a basket of growth names, increase it when slope is positive and rising.
Sector rotation timing: Apply the pipeline to sector ETFs (XLK, SOXX, IGV, WCLD) and rotate capital toward whichever sector shows the strongest positive slope, rebalancing weekly.
Volatility-adjusted position sizing: Rather than using slope as a binary on/off signal, normalize it by the current bandwidth value to get a continuous signal strength score that can drive fractional position sizing.
Feature for ML models: The LLR slope and bandwidth time series are high-quality engineered features for gradient boosting or LSTM models predicting short-term forward returns on growth stocks, capturing both trend direction and regime confidence in two numbers.

5. Limitations and Edge Cases

Computational cost scales quadratically. The naive implementation loops over every index point and solves a full weighted least-squares problem. For a 1,000-day series it runs in seconds; for tick-level data or very large universes, you need either an approximate kernel solution or a fast Cython/Numba rewrite of the inner loop.

Bandwidth calibration is regime-dependent. The BANDWIDTH_BASE and VOL_SCALE parameters were not optimized — they were set by intuition and light experimentation. A parameter sweep on in-sample data is required before live deployment, and the resulting parameters should be validated on a held-out period before trusting the Sharpe numbers.

Survivorship bias in the ticker selection. QQQ, SOXX, and IGV are diversified ETFs that have survived and grown. Applying this same methodology to individual growth stocks introduces survivorship bias unless you source point-in-time constituent data.

Gap risk on binary signals. The strategy enters at the next open after a signal fires. Overnight gaps in growth stocks — particularly around earnings — can produce fill prices far from the theoretical entry. Realistic slippage should be modeled as at least 2–3x the TRANSACTION_COST constant used here.

No short-side implementation. The current design is long/flat only. Growth stocks in downtrends can trend persistently on the short side, and the slope signal would technically support a short entry. Adding shorting introduces borrow costs, margin requirements, and behavioral risk that are not captured in this backtest.

Concluding Thoughts

Adaptive local linear regression offers a principled, mathematically grounded alternative to fixed-window momentum indicators. By tying the kernel bandwidth to local realized volatility, the filter naturally becomes more conservative in noisy regimes and more aggressive in trending ones — exactly the behavior a discretionary trader tries to replicate manually. The slope output is clean, interpretable, and directly tradeable as both a binary signal and a continuous feature.

The most productive next experiments are: replacing the Gaussian kernel with an Epanechnikov kernel for better edge behavior, adding a second signal layer based on the second derivative of the fitted curve (acceleration), and testing the pipeline on a larger universe of individual growth stocks with proper point-in-time data. Each of those extensions builds directly on the framework coded here.

If you found this useful, the full annotated Colab notebook — with interactive charts, parameter sweep grid, and out-of-sample validation — is available to premium subscribers at AlgoEdge Insights. New strategy notebooks are published weekly, covering everything from volatility surface modeling to cross-sectional momentum systems.

Most algo trading content gives you theory.
This gives you the code.

3 Python strategies. Fully backtested. Colab notebook included.
Plus a free ebook with 5 more strategies the moment you subscribe.

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Adaptive Local Linear Regression for Short-Term Growth Stock Trend-Following in Python

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1. Local Linear Regression as an Adaptive Trend Filter

2. Python Implementation

2.1 Setup and Parameters

2.2 Adaptive Local Linear Regression Signal Engine

2.3 Backtest Engine with Signal-to-Position Logic

2.4 Visualization

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3. Results and Strategy Analysis

4. Use Cases

5. Limitations and Edge Cases

Concluding Thoughts

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The `tv` CLI

What Was Removed: `ui_evaluate`

Step 2: The Connector (save as `horus_connector.py`)