주제: Stock Trading Bot

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  • Starting a Stock Trading Bot: Five Checks Before Live Orders

    한국어로 읽기 TECH INSIGHT / EXPLAINED The first milestone for a trading bot is control over orders, not an attractive return chart. Turn broker connectivity, simulation, cost accounting, usage conditions, and recovery into five concrete deliverables.

  • Seven Trading Bot Checks: Timing, Costs and Unknown Orders

    한국어로 읽기 TECH INSIGHT / EXPLAINED A trading bot can lose money because of its strategy, implementation, costs or market conditions. These seven checks address implementation and evaluation errors; passing them does not establish a profitable strategy.

  • Five Python Trading Indicators: Specify Seeds, Warm-Up and Edge Cases

    한국어로 읽기 TECH INSIGHT / EXPLAINED Indicator names do not fully specify their calculation. Seed values, warm-up length, standard-deviation convention and missing bars can change the output even when the visible period is identical.

  • KIS API First Integration: Environment, Expiry and Business-Error Checks

    한국어로 읽기 TECH INSIGHT / EXPLAINED The first integration target is a verified quote response from the intended environment. Keep application credentials, access tokens, market selection and response validation as four explicit steps.

  • Reading the Order Book: Spread, Imbalance and Snapshot Consistency

    한국어로 읽기 TECH INSIGHT / EXPLAINED An order book is a snapshot of displayed resting liquidity. It helps estimate the conditions for an order, but it is neither a promise of a fill nor a direct window into every trader’s intentions.

  • Position Sizing: A Planned Loss Budget Is Not a Guaranteed Maximum

    한국어로 읽기 TECH INSIGHT / EXPLAINED Position sizing turns a loss scenario into a quantity. The scenario is a planning assumption: a stop order cannot make the realized loss equal to that budget in every market.

  • Slippage and Fill Quality: Measure Prices and Reconcile Replacements

    한국어로 읽기 TECH INSIGHT / EXPLAINED Execution quality needs a benchmark and a timeline. Measure the difference between the decision price and the quantity-weighted fills, then account for fees and unfilled quantity separately.

  • Backtest Data Pipelines: Collecting, Cleaning, and Blocking Look-Ahead Bias

    한국어로 읽기 TECH INSIGHT / EXPLAINED A reproducible backtest needs the data as it could have been known at each decision time, plus a record of every cleaning choice. Keep raw observations, normalized observations and derived features separate so a surprising result can be traced back.

  • Trading Strategy Comparison: Pair Trading vs. Grid vs. Trend Following

    한국어로 읽기 TECH INSIGHT / EXPLAINED Compare strategies by the hypothesis they need to hold and the exposure they leave when it fails. Pair trading, grids and trend following can all lose in the same period, so a neat regime label is not a diversification guarantee.

  • Broker OpenAPI Token Auto-Renewal: A Design That Fails Loudly

    한국어로 읽기 TECH INSIGHT / EXPLAINED Token lifetime is an expiry-time problem, not a calendar-date problem. A broker client should know which credential a request needs, when it expires, and how to coordinate renewal without replaying uncertain orders.

  • Safety Mechanisms in Trading Bots

    한국어로 읽기 TECH INSIGHT / EXPLAINED Safety controls must describe what the bot knows, what it is allowed to do, and how it recovers when a request has an uncertain result. A strategy can lose substantially even with these controls; they reduce specific failure modes rather than guarantee a loss limit.

  • Backtesting and Paper Trading: Gatekeepers Before Live Capital

    한국어로 읽기 TECH INSIGHT / EXPLAINED Backtesting studies a specified model on historical information. Paper trading exercises a live data pipeline with simulated orders. Each can expose failures, but neither certifies a strategy’s future return or a real exchange’s fill behavior.

  • Leverage/Inverse ETF Gap Strategy: Structure and Risks

    한국어로 읽기 TECH INSIGHT / EXPLAINED A leveraged/inverse ETF gap is a diagnostic measurement, not a risk-free arbitrage signal. Before coding it, define the actual benchmark, daily return convention, execution prices and the direction of each discrepancy.

  • Real-Time Stock Quotes via KIS WebSocket (H0STASP0/H0STCNT0)

    한국어로 읽기 TECH INSIGHT / EXPLAINED Streaming quotes are useful only when the parser, subscription state, and freshness checks agree. KIS exposes distinct real-time channels; treat each as a versioned schema, not as an interchangeable array of numbers.

  • Bot Development Milestones: Observe, Replay and Rehearse Recovery

    한국어로 읽기 TECH INSIGHT / EXPLAINED Incremental development works best when every stage ends with evidence, not merely another enabled feature. Keep the authority to submit orders separate from the ability to collect data or produce a signal. M1, M2 and M3 are the development stages proposed in this article, not an industry standard or a validated trading strategy. They mean observe, replay without orders, and rehearse recovery . Deciding whether to enable live execution comes afterward as a separate decision.

  • Stock Trading Bot Architecture: Signals, Risk Gates, and Order State

    한국어로 읽기 ENGINEERING NOTE / 01 A stock trading bot has two separate jobs: deciding what it wants to do and establishing what actually happened at the broker. Treating those jobs as one function is how a small connection problem turns into a duplicate order. This walkthrough builds the boundary between the two. It uses invented closing prices and a local Python example; it does not connect to a broker or demonstrate a profitable trading strategy.