DEV Community: Voren Tai

Building Trading Applications with the Fomo API The Fomo API

Voren Tai — Thu, 17 Sep 2026 07:59:06 +0000

Building Trading Applications with the Fomo API The Fomo API provides programmatic access to structured social and blockchain trading data from the Fomo ecosystem.

The Fomo API provides programmatic access to structured social and blockchain trading data from the Fomo ecosystem. Developers can use the API to connect trader activity, wallet information, performance metrics, and transaction history with their own applications.

This makes it possible to build data-driven tools without manually collecting or normalizing information from multiple sources.

Website: https://getfomoapi.fun

These resources can support portfolio monitoring, trader discovery, alert systems, research platforms, and automated analytics.

Getting Started

To begin, create an account at getfomoapi.fun and generate an API key from the developer dashboard.

API keys should be stored securely as environment variables:

export FOMO_API_KEY="fomo_live_YOUR_API_KEY"

Each authenticated request must include the key:

curl "https://getfomoapi.fun/api/leaderboard/7d?limit=10" \
  -H "X-API-Key: $FOMO_API_KEY"

The API also accepts Bearer authentication:

curl "https://getfomoapi.fun/api/leaderboard/7d?limit=10" \
  -H "Authorization: Bearer $FOMO_API_KEY"

Example: Finding a Trader

Developers can resolve a Fomo handle and obtain the associated profile and wallet information:

import os
import requests

api_key = os.environ["FOMO_API_KEY"]
handle = "starcatcher444"

response = requests.get(
    f"https://getfomoapi.fun/api/users/{handle}",
    headers={"X-API-Key": api_key},
    timeout=30,
)

response.raise_for_status()
profile = response.json()["responseObject"]

print(profile.get("displayName"))
print(profile.get("solana"))
print(profile.get("evm"))

The returned user ID can then be used to request additional information, including balances, positions, rankings, and swap activity.

Example: Monitoring Swaps

user_id = "USER_ID"

response = requests.get(
    f"https://getfomoapi.fun/api/users/{user_id}/swaps",
    headers={"X-API-Key": api_key},
    params={"limit": 20},
    timeout=30,
)

response.raise_for_status()
swap_data = response.json()

print(swap_data)

Swap responses are normalized so that developers can process transaction activity more consistently across their applications.

Common Use Cases

The Fomo API can serve as the data layer for:

Trader dashboards that display rankings, PnL, and holdings.
Wallet trackers that monitor verified on-chain addresses.
Trading alerts that notify users about notable swaps.
Research tools that compare trader performance across time periods.
Telegram bots that deliver market and trader updates.
AI applications that analyze social and trading behavior.

What Can You Access?

Through the API, applications can retrieve:

Trader profiles and handles
Verified Solana and EVM wallet addresses
Performance leaderboards
Token balances and holdings
Profit and loss data
Swap history
Featured trades and social activity
Historical portfolio snapshots

Request Limits

API access is subject to a limit of five requests per second per API key. Applications should implement appropriate error handling and respect the Retry-After header when receiving a 429 Too Many Requests response.

Developers should also protect their credentials, validate returned data, and avoid using the API as the sole basis for financial decisions.

Developer Resources

Website: https://getfomoapi.fun
Documentation: https://getfomoapi.fun/docs
Telegram: @dexlenai
X: @0xdexlenai

Fomo API is an independent developer service and is not an official Fomo Family product. Trading data may be delayed, incomplete, or inaccurate.

What Is the Fomo API? A Guide to Programmatic Access

Voren Tai — Thu, 17 Sep 2026 07:39:09 +0000

What Is the Fomo API? A Guide to Programmatic Access

The Fomo API is a developer-focused REST API that provides structured access to Fomo Family social and on-chain trading data. It enables applications to retrieve trader profiles, verified wallet addresses, leaderboards, balances, holdings, PnL, swap history, and social activity through consistent JSON responses.

Fomo API can be used to build:

Trading dashboards
Wallet and portfolio trackers
Smart-money alerts
Telegram trading bots
Copy-trading research tools
AI trading agents
Automated portfolio analytics

Fomo API is an independent developer service. It is not an official Fomo Family product and does not provide financial advice.

How to Access the API Programmatically

1. Create an API Key

API keys use the following format:

fomo_live_YOUR_API_KEY

Keep your API key private and do not expose it in browser-side code or public repositories.

2. Authenticate Requests

Requests must include the API key in either the X-API-Key header or the Authorization header.

curl "https://getfomoapi.fun/api/leaderboard/24h?limit=5" \
  -H "X-API-Key: fomo_live_YOUR_API_KEY"

Bearer authentication is also supported:

curl "https://getfomoapi.fun/api/leaderboard/24h?limit=5" \
  -H "Authorization: Bearer fomo_live_YOUR_API_KEY"

3. Retrieve Trading Data

For example, the following Python code retrieves the 24-hour leaderboard:

import os
import requests

api_key = os.environ["FOMO_API_KEY"]

response = requests.get(
    "https://getfomoapi.fun/api/leaderboard/24h",
    headers={"X-API-Key": api_key},
    params={"limit": 5},
    timeout=30,
)

response.raise_for_status()
data = response.json()

print(data)

4. Resolve a Trader

Trader handles can be resolved into user profiles and wallet addresses:

import os
import requests

handle = "starcatcher444"

response = requests.get(
    f"https://getfomoapi.fun/api/users/{handle}",
    headers={"X-API-Key": os.environ["FOMO_API_KEY"]},
    timeout=30,
)

response.raise_for_status()
trader = response.json()["responseObject"]

print("User ID:", trader.get("id"))
print("Solana wallet:", trader.get("solana"))
print("EVM wallet:", trader.get("evm"))

5. Retrieve Swap History

Once a trader’s user ID is available, applications can request normalized swap activity:

response = requests.get(
    "https://getfomoapi.fun/api/users/USER_ID/swaps",
    headers={"X-API-Key": os.environ["FOMO_API_KEY"]},
    params={"limit": 10},
    timeout=30,
)

response.raise_for_status()
swaps = response.json()

print(swaps)

Available API Resources

The primary Pro endpoints include:

Endpoint	Purpose
`/api/leaderboard/{window}`	Retrieve ranked traders
`/api/users/{handle}`	Resolve a trader profile
`/api/users/{userId}/balances`	Retrieve balances and positions
`/api/users/{userId}/spotlight`	Retrieve notable trades and comments
`/api/users/{userId}/swaps`	Retrieve paginated swap history
`/api/users/{userId}/leaderboard`	Retrieve trader rankings
`/api/user-tokens/aggregated-snapshot`	Retrieve historical PnL and equity

Supported leaderboard windows include 24h, 7d, 30d, and all.

Rate Limits and Responsible Use

Pro API keys are limited to five requests per second. When the limit is exceeded, the API returns an HTTP 429 response. Applications should respect the Retry-After header before retrying.

Developers should also:

Store API keys securely
Validate data before making trading decisions
Handle network and upstream errors
Avoid abusive scraping
Account for delayed or incomplete market data

Resources

Website: getfomoapi.fun
Documentation: getfomoapi.fun/docs
Telegram: @dexlenai
X: @0xdexlenai

With an API key and standard HTTP requests, developers can integrate Fomo trading and social data into applications, analytics platforms, bots, and automated workflows.

Poly Weather Analyzer: The Ultimate Guide to Polymarket Weather Markets (2026)

Voren Tai — Thu, 10 Sep 2026 09:45:24 +0000

How Poly Weather Analyzer Works

This guide explains the data flow, probability model, confidence score, scenarios, and analytical assumptions behind the project.

System Objective
Data Collection
Event Parsing
Location Resolution
Forecast Normalization
Uncertainty Estimation
Temperature Brackets
Probability Calculation
Market Probability
Pricing Edge
Expected Value
Confidence Score
Scenario Analysis
Sensitivity Analysis
Important Assumptions
Future Improvements

System Objective

The system estimates the probability distribution of a target-day temperature and compares that distribution with Polymarket outcome prices.

The central analytical question is:

How does the probability assigned by a weather ensemble differ from the probability implied by the prediction market?

The analyzer does not attempt to prove that either system is always correct. Instead, it quantifies their disagreement.

Data Collection

Polymarket Gamma API

The Gamma API provides:

Event ID
Event title
Event slug
Description
Resolution source
Start and end dates
Outcome markets
Yes and No prices
Liquidity
Volume
Bid and ask prices
Last trade price
CLOB token IDs

Polymarket CLOB API

The CLOB API provides historical token-price observations when available.

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Historical prices help answer:

Has the market recently moved?
Which bracket gained probability?
Did weather updates produce a price reaction?
Is the current price an outlier relative to recent trading?

Weather APIs

Each weather provider is converted into a common forecast record:

{
  "source": "Provider name",
  "model": "model_identifier",
  "maximum_c": 29.1,
  "minimum_c": 23.4,
  "low_c": 27.8,
  "high_c": 30.4
}

This common structure allows different providers to be combined.

Event Parsing

The analyzer extracts the city from titles such as:

Highest temperature in Shanghai on September 10?

It also reads the event date from:

eventDate
endDate
A date embedded in the title

Temperature outcomes are usually identified by groupItemTitle.

Examples:

25°C or below
26°C
27°C
28°C
29°C
30°C
34°C or higher

Location Resolution

The location name is passed to the Open-Meteo geocoding API.

The response includes:

Canonical location name
Country
Administrative region
Latitude
Longitude
Time zone
Elevation

The time zone is important because daily maximum temperature must be calculated using the target location's calendar day rather than UTC.

Resolution-station warning

The geocoded city center may not match the official market resolution station.

For example, a market may resolve using an airport station outside the city center. Differences can arise from:

Urban heat effects
Elevation
Coastal influence
Distance from the city center
Station exposure
Observation timing

Exact station matching should be preferred whenever station metadata is available.

Forecast Normalization

Different providers return different structures:

Provider Type	Typical Structure
Daily forecast	One daily maximum and minimum
Hourly forecast	Hourly temperatures aggregated to a daily maximum
Ensemble forecast	Many forecast members for each hour
Official forecast	Human-adjusted or national-service forecast periods

The data layer converts these formats into Celsius.

Hourly forecast values are filtered using the target location's local date. The highest valid hourly temperature becomes the source's daily maximum estimate.

Uncertainty Estimation

Each source needs both a forecast center and an uncertainty estimate.

The project derives a source standard deviation from its lower and upper interval:

A lower bound is applied so that a source cannot become unrealistically certain.

When an interval is unavailable, the configured default is used:

DEFAULT_TEMPERATURE_SIGMA=1.5

Temperature Brackets

Markets resolved to whole degrees need continuous probability boundaries.

Exact whole-degree bracket

For a 29°C outcome:

This method maps a continuous forecast distribution to whole-degree observations.

Normalization makes the modeled outcome set sum to one.

Market Probability

Each temperature outcome is represented by a binary Yes/No market.

The Yes price is interpreted as the raw market-implied probability:

Normalization compensates for small inconsistencies across separate binary books.

It does not eliminate:

Bid-ask spread
Fees
Stale orders
Thin liquidity
Execution costs

Pricing Edge

The probability edge is:

Interpretation

Edge	Meaning
(E_i > 0)	Model assigns more probability than the market
(E_i \approx 0)	Model and market approximately agree
(E_i < 0)	Market assigns more probability than the model

A positive edge is not automatically a trade recommendation.

The difference may be caused by:

Model bias
Incorrect geocoding
Resolution-station differences
Unmodeled weather behavior
Market information not included in the APIs
Forecast update timing
Liquidity distortions

Expected Value

The simplified expected-value ratio is:

[

EV_i

\frac{P_{\text{model},i}}
{Price_{\text{Yes},i}}
-1
]

If the model probability is 20% and the Yes price is 10%:

[
EV = \frac{0.20}{0.10}-1 = 1.0
]

The displayed value is +100%.

This calculation does not include execution costs or payout timing. It is best treated as a screening metric rather than a final return estimate.

Confidence Score

The confidence score combines four components.

Source coverage

More independent forecast sources increase coverage.

where (D) is the standard deviation of source maximum-temperature forecasts.

Outcome concentration

A stronger leading probability produces a higher concentration component.

Confidence labels

Score	Label
80–100	Very high
65–79.99	High
50–64.99	Moderate
35–49.99	Low
Below 35	Very low

The score measures internal model conditions. It is not a historical accuracy guarantee.

Scenario Analysis

The analyzer creates three scenarios:

Scenario	Temperature	Weight
Cool	Ensemble mean minus ensemble uncertainty	20%
Base	Ensemble mean	60%
Hot	Ensemble mean plus ensemble uncertainty	20%

Each scenario is mapped to its corresponding market bracket.

Scenario analysis provides an intuitive view of which outcome wins if the forecast shifts colder or warmer.

Sensitivity Analysis

A prediction can appear strong only because of a specific uncertainty assumption.

The sensitivity model varies:

Forecast bias from approximately -3°C to +3°C
Standard deviation from approximately 0.5°C to 4°C

For each pair of assumptions, it recalculates the selected outcome's probability edge.

Reading the sensitivity chart

Pattern	Interpretation
Large stable positive region	Edge survives many assumptions
Narrow positive region	Edge is fragile
Rapid sign changes	Small forecast changes alter the conclusion
Mostly negative region	Market generally assigns more probability
Strong uncertainty dependence	Distribution width drives the result

The sensitivity chart is one of the most important safeguards against overconfidence.

Important Assumptions

Normality

Source temperature uncertainty is approximated with a normal distribution. Real forecast errors may be:

Skewed
Heavy-tailed
Regime dependent
Seasonally biased
Location dependent

Source independence

Several providers may rely on the same underlying numerical models. Averaging providers does not necessarily create independent evidence.

Equal weighting

The mixture currently gives comparable influence to available normalized forecast sources. A calibrated system should weight sources using historical performance.

Stable uncertainty

Source uncertainty is estimated from intervals or defaults. True uncertainty changes with:

Forecast horizon
Weather regime
Geography
Season
Model initialization
Observation station

Resolution precision

Half-degree boundaries assume whole-degree rounding. The actual resolution procedure must be confirmed from the event rules.

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Future Improvements

A stronger production model could add:

Exact station coordinates
Station elevation correction
Historical source-bias calibration
Forecast-horizon-dependent uncertainty
Model-family deduplication
Bayesian source weighting
Heavy-tailed forecast distributions
Time-series forecast revisions
Proper scoring-rule backtesting
Market fee and spread modeling
Order-book execution simulation
Automated station observation tracking

The current system is designed as a transparent research framework. Its modular structure makes each assumption replaceable.

A futuristic Bloomberg-inspired smart-money intelligence terminal for Solana wallets

Voren Tai — Thu, 10 Sep 2026 09:39:01 +0000

🪐 Smart Wallet Terminal

Track influencers, smart-money wallets, top profit leaders, and whales from one animated terminal dashboard.

Features •
Quick Start •
Controls •
Project Structure •
Guides •
Disclaimer

📟 Overview

** Smart Wallet Terminal** is a Python terminal application that transforms 's smart-money wallet feeds into a live, keyboard-controlled market intelligence dashboard.

The interface is designed around the visual language of professional financial terminals:

High-density wallet tables
Bright market-status colors
Animated loading and printing effects
Live profit-and-loss indicators
Wallet ranking and activity analysis
Fast keyboard navigation
Automatic data synchronization
Compact 24-hour market statistics

The project is intended for wallet discovery, market research, activity monitoring, and educational analysis. It is not a trading bot and does not execute transactions.

✨ Features

🧠 Four Wallet Intelligence Channels

Channel	API Category	Purpose
Influencers	`influencer`	Tracks wallets associated with public social identities
Smart Money	`smartMoney`	Surfaces wallets classified as sophisticated market participants
Top PNL	`topPnl`	Ranks wallets by recent realized or calculated profit performance
Whales	`whale`	Highlights large and highly active market participants

📊 Live Terminal Metrics

The dashboard displays:

Wallet rank
Twitter or X username when available
Shortened wallet address
24-hour PNL in USD
24-hour trading volume
Transaction count
Win rate
Last activity time
Aggregate channel PNL
Aggregate volume
Aggregate transactions
Average win rate
PNL distribution sparkline
API cache timestamp
Current sorting mode
Search filter state
Synchronization status

⚡ Interactive Experience

Parallel API requests across all four categories
Non-blocking terminal rendering
Automatic refresh every 45 seconds
Manual refresh support
Animated boot sequence
Progressive status-message printing
Progressive wallet-row reveal
Keyboard-only navigation
Full-screen alternate terminal interface
Responsive page sizing based on terminal height
Positive and negative PNL color separation
Windows and Unix-like keyboard support

🖥️ Terminal Preview

╔══════════════════════════════════════════════════════════════════════════════╗
║   SMART WALLET TERMINAL ◆                   ● LIVE  2026-09-10 12:42 ║
╚══════════════════════════════════════════════════════════════════════════════╝

 [1 INFLUENCERS]   2 SMART MONEY   3 TOP PNL   4 WHALES   SORT PNL

┌─ 24H INFLUENCERS MARKET PULSE ───────────────────────────────────────────────┐
│ NET PNL       VOLUME          TRANSACTIONS    AVG WIN RATE    DISTRIBUTION   │
│ \$184.32K      \$2.91M          8,421           52.8%           ▅▂█▄▃▆▁▇       │
└──────────────────────────────────────────────────────────────────────────────┘

┌─ WALLET INTELLIGENCE FEED · 100 RECORDS · PAGE 1/8 ─────────────────────────┐
│  #   IDENTITY       WALLET          PNL 24H    VOLUME    TXNS   WIN RATE     │
│  1   @TraderOne     CyaE1V…ga54o    \$43.86K    \$386.39K  1,523  ████░ 42.0%  │
│  2   @WalletAlpha   9yYya3…r4jqL    \$25.10K    \$64.48K      79  █████ 53.1%  │
│  3   ANONYMOUS      6Lcx2P…8FaaQ    \$18.92K    $92.11K     203  █████ 56.4%  │
└──────────────────────────────────────────────────────────────────────────────┘

› DATA GRID SYNCHRONIZED · 400 WALLETS INDEXED

1-4 CHANNEL  ←/→ SWITCH  ↑/↓ SELECT  S SORT  / FILTER  X CLEAR  R REFRESH  Q QUIT

🗂️ Project Structure

-smart-wallet-terminal/
├── smart_wallet_terminal.py
├── requirements.txt
├── README.md
├── socials.txt
└── docs/
    ├── INSTALLATION.md
    ├── USAGE.md
    ├── DATA_AND_METRICS.md
    ├── CUSTOMIZATION.md
    └── TROUBLESHOOTING.md

Path	Description
`smart_wallet_terminal.py`	Main terminal application, API client, renderer, keyboard handler, and synchronization engine
`requirements.txt`	Python package dependencies
`README.md`	Project overview, setup instructions, controls, features, and documentation index
`socials.txt`	Ready-to-publish introductory social posts
`docs/INSTALLATION.md`	Detailed installation instructions for Windows, macOS, and Linux
`docs/USAGE.md`	Complete terminal navigation and workflow guide
`docs/DATA_AND_METRICS.md`	Explanation of wallet categories, metrics, and research use cases
`docs/CUSTOMIZATION.md`	Instructions for adjusting appearance, refresh settings, categories, and behavior
`docs/TROUBLESHOOTING.md`	Solutions for installation, API, terminal, networking, and rendering problems

✅ Requirements

Requirement	Recommended
Python	Python 3.9 or newer
Terminal	Windows Terminal, iTerm2, Kitty, Alacritty, WezTerm, GNOME Terminal, Konsole
Internet	Required for API requests
Terminal colors	True color or 256-color support recommended
Terminal size	At least 100 columns by 28 rows
Operating system	Windows, macOS, or Linux

The application uses the following Python packages:

Package	Purpose
`requests`	Retrieves smart-wallet data from the API
`rich`	Renders the full-screen dashboard, tables, panels, colors, and animations

🚀 Quick Start

1. Clone or download the project

git clone https://github.com/your-name/-smart-wallet-terminal.git
cd -smart-wallet-terminal

If the repository was downloaded as a ZIP archive, extract it and open a terminal inside the extracted directory.

2. Create a virtual environment

python -m venv .venv

3. Activate the virtual environment

Windows PowerShell

.venv\Scripts\Activate.ps1

Windows Command Prompt

.venv\Scripts\activate.bat

macOS or Linux

source .venv/bin/activate

4. Install dependencies

python -m pip install --upgrade pip
pip install -r requirements.txt

5. Start the terminal

python smart_wallet_terminal.py

⌨️ Keyboard Controls

Key	Action
`1`	Open the Influencers channel
`2`	Open the Smart Money channel
`3`	Open the Top PNL channel
`4`	Open the Whales channel
`Left Arrow`	Open the previous channel
`Right Arrow`	Open the next channel
`Tab`	Open the next channel
`Up Arrow` or `K`	Select the previous wallet
`Down Arrow` or `J`	Select the next wallet
`Page Up`	Move up by one page
`Page Down`	Move down by one page
`Home`	Select the first wallet
`End`	Select the last wallet
`S`	Cycle through sorting modes
`/` or `F`	Enter wallet search mode
`X`	Clear the active search filter
`R`	Refresh all data channels
`Q`	Exit the application
`Ctrl+C`	Immediately exit the application

Search-mode controls

Key	Action
Any printable character	Add text to the search query
`Backspace`	Remove the previous character
`Delete`	Clear the search input
`Enter`	Apply the search filter
`Escape`	Cancel search editing

🔍 Search and Filtering

Search works against:

Full wallet addresses
Partial wallet addresses
Twitter or X usernames
Partial usernames

Press /, enter a wallet fragment or username, and press Enter.

Example searches:

CyaE1V

Cented7

9yYya3F5

To remove the active filter, press X.

Search is applied locally to the currently selected category. It does not send additional search requests to the API.

↕️ Sorting Modes

Press S repeatedly to cycle through the available sorting modes.

Mode	Behavior
Rank	Uses the ranking returned by the data provider
PNL	Places the highest 24-hour PNL first
Volume	Places the highest trading volume first
Win Rate	Places the highest win rate first
TXNS	Places the highest transaction count first
Activity	Places the most recently active wallet first

The active sorting mode appears in the navigation bar.

🌐 Data Source

The terminal retrieves data from 's smart-money data API:

https://datapi.jup.ag/smart-money/v1/top

Each request includes:

Parameter	Value
`category`	`influencer`, `smartMoney`, `topPnl`, or `whale`
`window`	`1d`
`limit`	`100`

The four feeds are requested in parallel to reduce total synchronization time.

Example response fields:

{
  "rank": 1,
  "walletId": "CyaE1VxvBrahnPWkqm5VsdCvyS2QmNht2UFrKJHga54o",
  "pnlUsd": 43862.32,
  "volumeUsd": 386392.61,
  "txnCount": 1523,
  "lastActiveAt": "2026-09-10T00:15:55Z",
  "winRate": 0.42005,
  "twitterUsername": "Cented7",
  "profileImage": "https://example.com/profile.jpg"
}

API availability, fields, classifications, rate limits, and response behavior are controlled by the data provider and may change without notice.

🎯 What Is This Terminal For?

The terminal can support research workflows such as:

Wallet discovery

Find active wallets with strong PNL, meaningful volume, or consistent win rates.

Market-participant comparison

Compare influencers, smart-money wallets, top performers, and whales without switching between multiple browser pages.

Activity monitoring

Observe whether notable wallets have traded recently and identify channels with elevated transaction activity.

Research-list creation

Use usernames and wallet addresses as starting points for deeper analysis with explorers, portfolio tools, or internal research systems.

Educational analysis

Learn how wallet performance metrics differ across categories and how PNL, volume, transaction count, and win rate interact.

High historical PNL does not guarantee future performance. Wallet activity can involve transfers, hedges, private strategies, multiple addresses, or positions not represented in a single feed.

🎨 Interface Color Language

Color	Meaning
Orange	Terminal branding, section titles, and active commands
Cyan	Active navigation, usernames, filters, and system accents
Green	Positive PNL, healthy connection state, and favorable rates
Red	Negative PNL, warnings, and data errors
Blue	Trading-volume values
Gray	Secondary labels, unavailable values, and inactive controls

🔄 Refresh Behavior

The application automatically refreshes all categories every 45 seconds.

A refresh performs four parallel API requests:

Influencers
Smart Money
Top PNL
Whales

Existing data remains available while a new refresh is running. If only some feeds fail, successful feeds are still applied and the terminal reports a partial synchronization.

Press R at any time to request a manual refresh.

📚 Documentation Guides

Guide	What You Will Learn
Installation Guide	How to install and launch the project on Windows, macOS, and Linux
Usage Guide	How to navigate, sort, search, refresh, and build a research workflow
Data and Metrics Guide	What each category and metric means and how the terminal can be used
Customization Guide	How to change colors, refresh timing, API limits, layout, and categories
Troubleshooting Guide	How to resolve common Python, dependency, API, network, and terminal problems

🔐 Privacy and Security

The terminal:

Does not request a private key
Does not request a seed phrase
Does not connect to a local wallet
Does not sign transactions
Does not place trades
Does not modify blockchain state
Does not require exchange credentials
Only retrieves publicly available wallet analytics

Never paste a seed phrase, private key, API secret, or exchange credential into this application.

🛣️ Potential Roadmap

Additional time windows
CSV and JSON exports
Wallet watchlists
Wallet detail panels
Solana explorer links
Alert thresholds
Historical snapshots
PNL trend charts
Category comparison screens
Configurable themes
Optional proxy support
Offline snapshot viewing
Configurable refresh intervals
Token-level wallet analysis

🤝 Contributing

Contributions are welcome.

A useful contribution may include:

Terminal compatibility improvements
Better response validation
New sorting or filtering modes
Accessibility improvements
New visual themes
Export functionality
Documentation corrections
Test coverage
Performance improvements

Suggested workflow:

git checkout -b feature/my-improvement
git add .
git commit -m "Add my improvement"
git push origin feature/my-improvement

Open a pull request with:

A summary of the change
The reason for the change
Testing details
Screenshots for interface changes
Any compatibility considerations

⚠️ Disclaimer

This project is an independent terminal interface and is not an official product.

The information displayed by the application is provided for educational and informational purposes only. It is not financial, investment, legal, tax, or trading advice.

Wallet classifications and performance metrics may be incomplete, delayed, cached, inaccurate, or based on assumptions made by the data provider. A wallet's prior profitability does not establish that future trades will be profitable.

Always perform independent research before making financial decisions.

Can Trading Automation Realistically Produce an Extra $20–$50?

Voren Tai — Fri, 28 Aug 2026 08:16:18 +0000

Small Edges. Smarter Execution.

Small pricing inefficiencies appear across crypto and prediction markets every day.

A price difference of one, two, or three cents may not look exciting. However, when an opportunity is executable at sufficient size, survives fees, and can be completed without leaving an unmatched position, that small difference can become meaningful.

This is the idea behind MicroaiBots.

Explore MicroaiBots →

⚠️ Start With the Reality

No trading strategy is completely risk-free.

Atomic execution can reduce directional exposure. Buying complementary outcomes can reduce market-direction risk. Strict filters can reject weak opportunities.

However, none of these protections eliminate:

Failed transactions
Trading fees
Slippage
Partial fills
Smart-contract issues
Venue outages
Settlement risk

The realistic goal is not “free money.”

The goal is to:

Identify a measurable edge
Understand how it can fail
Account for all costs and risks
Automate only the opportunities that remain attractive

The Situation That Inspired the Product

The following is a realistic composite case study based on common execution problems.

This is not a claim of actual or guaranteed MicroaiBots profits.

A trader noticed a short-duration prediction market with the following displayed prices:

Outcome	Displayed Price
UP	$0.47
DOWN	$0.50
Combined Cost	$0.97
Potential Combined Settlement	$1.00

At first glance, the trade appeared to offer a gross edge of three cents per matched pair.

The trader manually purchased UP.

Before the DOWN order was submitted, its price moved from $0.50 to $0.54.

The original calculation had disappeared.

The Trader Now Had Three Options

Buy DOWN at a combined cost above $1.00
Leave the UP position unhedged
Attempt to sell UP and accept the available exit price

The idea was reasonable, but the execution workflow was incomplete.

The displayed prices did not answer the questions that actually mattered:

Was enough quantity available?
Were both prices executable?
How much would fees consume?

This is the problem MicroaiBots is designed to address.

Automation does not create an edge by itself.

It helps evaluate and execute predefined rules faster and more consistently than a manual workflow.

Displayed Edge vs. Executable Edge

Displayed Prices

UP      $0.47 ─────┐
                  ├── Combined displayed cost: $0.97
DOWN    $0.50 ─────┘

Potential settlement:  $1.00
Displayed gross edge:  $0.03

The real calculation must include more information:

Estimated Net Edge

= Potential settlement

− Executable UP cost

− Executable DOWN cost

− Trading fees

− Slippage

Example Calculation

Component	Amount
Gross displayed edge	$0.030
Trading and execution costs	−$0.008
Safety buffer	−$0.004
Estimated net edge	$0.018

The opportunity may also become negative and should then be rejected.

Approach 1: Complementary-Outcome Pricing

Binary prediction markets commonly have two opposing outcomes, such as:

UP / DOWN
YES / NO

Under normal binary settlement rules, one matched winning share may settle for $1.00, while the other settles for $0.00.

If one share of each side can be acquired for less than $1.00, there may be a pricing difference.

Example

Item	Amount
UP executable price	$0.475
DOWN executable price	$0.492
Combined cost	$0.967
Potential combined settlement	$1.000
Gross theoretical edge	$0.033

The gross edge is not the final result.

Assume estimated costs are $0.009 per pair:

$1.000 − $0.475 − $0.492 − $0.009 = $0.024

Estimated Net Edge

$0.024 per matched pair

At 1,000 matched pairs:

Metric	Estimate
Estimated capital used	$967
Estimated net result	$24

This shows how a small edge can potentially produce a $20–$50 result.

It also shows why meaningful capital, sufficient liquidity, and reliable execution are required. A tiny account cannot repeatedly generate $20–$50 from one-cent differences without taking disproportionate risk.

Approach 2: Atomic Solana Route Arbitrage

Prices can temporarily differ across Solana liquidity pools and routing combinations.

A cyclic route may look like this:

SOL
  ↓
USDC
  ↓
Token X
  ↓
SOL

A route may be considered when the final projected SOL amount exceeds the starting amount after all estimated costs.

Estimated Net Result

= Final SOL

− Starting SOL

− Network fees

− Priority fees

− Router fees

− Safety buffer

Where supported, multiple swap instructions can be included in one Solana transaction.

What Does “Atomic” Mean?

An atomic transaction means:

All included instructions succeed together, or
The state changes revert together

This can reduce the chance of being left with an intermediate token after only part of the route executes.

However, atomic execution does not eliminate every risk.

Potential issues include:

Failed transactions may still consume fees
Quotes can become stale
RPC endpoints can fail
Network congestion can delay inclusion
Software defects can build an incorrect transaction

Atomic Execution Flow

Quote found
    │
    ▼
Check price impact
    │
    ▼
Subtract estimated costs
    │
    ▼
Simulate transaction
    │
    ├── Simulation failed ──► Reject
    │
    ▼
Apply minimum output
    │
    ▼
Submit complete route
    │
    ├── All instructions succeed
    │
    └── Transaction reverts

Atomic should be understood as a way to reduce partial-execution risk — not as a synonym for risk-free.

Approach 3: Cross-Router Differences

Jupiter and DFlow may produce different route outputs because they can access or prioritize liquidity differently.

A cross-router cycle might use:

Jupiter:  SOL → Token X
DFlow:    Token X → SOL

The bot compares both directions and rejects routes that fail any configured requirement.

Typical Safety Checks

Maximum quote age
Minimum expected profit
Maximum price impact
Maximum slippage
Approved intermediate tokens
Transaction simulation
Priority-fee ceiling
Minimum final output

A route showing a profit before fees should not be considered sufficient.

Only the expected output after every known cost should be evaluated.

Approach 4: Late-Stage Prediction Markets

A market outcome trading at $0.99 may settle at $1.00 if it wins.

That produces a maximum gross difference of approximately one cent per share before costs.

However, the payoff is asymmetric:

Result	Approximate Outcome
Winning share bought at $0.99	+$0.01 gross
Losing share bought at $0.99	Up to −$0.99

One incorrect trade can offset many successful trades.

A high win rate does not automatically create a profitable strategy.

What a Late-Stage Bot Should Consider

Remaining market time
Current spread
Available liquidity
Maximum order size
Price stability
Settlement rules

This approach should never be marketed or understood as guaranteed income.

The Safer Approach

Trade only when the configured edge exists.

Do not manufacture trades because a daily target has not been reached.

Some days may have multiple valid opportunities.

Other days may have none.

A Practical Starting Workflow

Start Small. Configure Carefully.

Install the bot locally
Use a dedicated wallet
Add only limited funds
Configure strict exposure limits

The objective is not to make the bot trade frequently.

The objective is to make it reject weak opportunities consistently.

Final Example

Suppose a complementary-outcome bot detects the following opportunity:

Item	Value
Executable UP price	$0.475
Executable DOWN price	$0.492
Combined executable cost	$0.967
Potential settlement value	$1.000
Gross edge	$0.033
Estimated costs	$0.009
Safety buffer	$0.004
Estimated net edge	$0.020

The configured minimum edge is $0.018.

Result: Opportunity Passes

At 1,000 matched shares:

Metric	Estimate
Capital required	Approximately $967
Estimated result after costs	Approximately $20

If available depth supports only 300 matched shares:

Metric	Estimate
Capital required	Approximately $290.10
Estimated result after costs	Approximately $6

The bot should use the smaller executable quantity rather than assuming all 1,000 shares can fill at the displayed price.

If one side cannot be acquired, the trade should be rejected or handled under the configured partial-fill policy.

The Realistic Role of Automation

MicroaiBots is built to help you:

Measure the opportunity
Validate the assumptions
Enforce the limits
Execute consistently
Record the result

Automation is not about forcing more trades.

It is about applying your rules with greater speed, consistency, and discipline.

Explore MicroaiBots

Discover configurable bots for Solana and prediction markets.

Browse the Bot Gallery →

Priority Support: Contact us on Telegram →

🧠 How to Decode Polymarket Bots Like a Pro — Building a Real Market Analyzer Toolkit

Voren Tai — Tue, 03 Feb 2026 11:42:38 +0000

“The quietest automated wallet on Polymarket made $457K and nobody noticed.”

— and now, you’re going to learn how to see what it saw.

🚦 Introduction: Beyond “Buy YES + NO < 1.0”

If you’ve spent even five minutes around Polymarket Twitter or Discord, you’ve probably seen claims like:

“90% win rate bots, zero loss, 3% risk-free.”

Sounds glorious... until reality checks in.

Latency kills, Chainlink updates race through the blockchain, and Python scripts drown in microseconds.

But what if, instead of chasing mythical arbitrage, we decode how real strategies emerge — like momentum bots, oracle frontrunners, or micro-latency scalpers?

That’s what this guide will show you:

👉 Step-by-step, you’ll build and use a Polymarket Market Analyzer that lets you read trader behavior, market rhythm, and micro edges — just like the $457K wallet did.

▬▬▬◄▬▬▬◄▬▬▬【 ⚖️ 】▬▬▬►▬▬▬►▬▬▬

👇 START HERE 👇

👉 Polymarket-Market-Analyzer — GitHub Repository 👈

👆 CODE DETAILS 👆

▬▬▬◄▬▬▬◄▬▬▬【 🚀 】▬▬▬►▬▬▬►▬▬▬

⚙️ Step 1. Install Your Toolkit

Prerequisites

You’ll need:

Python 3.8+
pip
A curious mind 😎

Install Dependencies

pip install requests pandas numpy matplotlib scipy

🧩 Step 2. Start With Market-Level Analytics

Let’s start with understanding how prices evolve, how spreads move, and what volatility looks like during real trades.

from polymarket_analyzer import get_market_details_by_slug, get_price_history, plot_market_analyzer

market_slug = "bitcoin-up-or-down-january-29-8am-et"
market = get_market_details_by_slug(market_slug)
price_series = get_price_history(market)
plot_market_analyzer(market_slug)

🔍 What you get:

Gradient-filled price timeline (YES/NO sides)
Spread evolution (tightness → liquidity insight)
Volatility bursts detection

📊 These are the fingerprints of momentum shifts.

⚖️ Step 3. Understand Order Flow & Trade Dynamics

If you want to think like “gabagool22,” you watch how trades cluster, how sizes escalate, and how pressure builds.

from polymarket_analyzer import get_polymarket_trades, plot_trade_analytics

trades = get_polymarket_trades(market_id=market['conditionId'], limit=1000)
plot_trade_analytics(market_slug)

Visualization Includes:

Buy vs Sell volume distribution
Price vs trade size scatter
Cumulative trade flow (BUY/SELL imbalance)

💡 In momentum phases, “YES” side volume spikes milliseconds before price repricing.

🌈 Step 4. Dive Into Gradient Scatter Analytics

Sometimes, a standard line chart hides what really matters — when, how fast, and how big trades arrive.

plot_gradient_scatter_analytics(market_slug)

🔥 You’ll See:

Temporal gradient scatter: tracks emotional tempo of the market
Volume-weighted timeline: shows where big whales swim
Density heatmap: reveals liquidity choke points

🧠 These visuals help decode recurring micro-patterns bots exploit.

👤 Step 5. Spy Like an On-Chain Detective

Let’s go deeper — analyze a specific wallet’s strategy behavior.

user_address = "0x6031b6e..."
plot_trader_strategy_analysis(market_slug, user_address)
plot_trader_timing_analysis(market_slug, user_address)

📈 You’ll see:

Entry price evolution
Average sizing per trade
Frequency heatmaps per hour

And guess what the $457K bot did?

Small sizes
Rapid fire entries
Relentless consistency

No thrill, no gamble — just statistical grinding.

💥 Step 6. Risk, Behavior, and Bias Analysis

Once you aggregate trades, it’s time to plot bias, consistency, and patterned risk-taking.

plot_risk_assessment(market_slug, user_address)
plot_behavioral_insights(market_slug, user_address)

📊 Output metrics:

Volume-weighted risk distribution
Position sizing stability
Sequential trade flow

📍 The more consistent the curve, the less emotion — and the better the edge.

🧮 Step 7. Understand the Constraints

Type	Reality	Impact
Latency	12μs (Rust/C++) vs 300μs (Python)	You lose before trading starts
API Access	Dedicated RPC nodes, colocation	Keeps them milliseconds faster
Strategy	Pair trading + oracle frontrun	Requires infra, not luck
Risk Scaling	Micro-sized positions, high frequency	Low drawdown, steady curve

Lesson: simplicity wins only when it’s executed faster, smarter, and cheaper than others.

🧠 Step 8. Build Human + AI Intelligence

You don’t need to be a HFT veteran to build insight tooling.

Instead of coding full bots, build intelligence dashboards with:

LLM-assisted event labeling (“What pattern is this?”)
AI-based anomaly detection on price spreads
Telegram alert bots for irregular momentum

Think of it as your AI co-pilot — spotting market anomalies before your brain does.

💡 Conclusion: The Discipline of Silent Bots

The lesson from “gabagool22” isn’t luck or genius — it’s iteration and discipline.

More trades. Smaller sizes. Constant execution.

No big wins, no celebrations. Just an upward line.

And that’s exactly what this toolkit helps you uncover:

— the silent mechanics behind smart, small edges that compound.

🚀 TL;DR — What You’ll Achieve

Goal	Tool	Why It Matters
Market awareness	`plot_market_analyzer()`	Identify momentum lag
Liquidity mapping	`plot_trade_analytics()`	Understand how flow shifts
Hidden behavior	`plot_gradient_scatter_analytics()`	Detect pattern signals
Trader forensics	`plot_trader_strategy_analysis()`	Reveal execution style
Risk dynamics	`plot_risk_assessment()`	Visualize edge consistency

🔗 Ready to Try?

Clone the toolkit and start decoding your own markets today:

👉 Polymarket-Market-Analyzer — GitHub Repository

Or grab a VPS, feed it chain data, and watch how market edges truly emerge — in milliseconds.

🧭 Final Thought

Trading success often hides behind boring code, quiet bots, and relentless repetition.

This guide gives you the technical lens to finally see them.

How to decode any polymarket trading bot strategy ?

Voren Tai — Tue, 03 Feb 2026 11:03:15 +0000

🚀 Introduction

Prediction markets like Polymarket are shaping the future of on‑chain information.

But as the number of active markets grows into the hundreds, the real edge no longer lies in raw intuition — it lies in data.

If you’ve ever wondered how advanced traders detect micro trends, liquidity gaps, or hidden arbitrage opportunities, this post is for you.

Today, we’ll explore how to use the Polymarket Advanced Analytics Toolkit — a Python‑based analysis powerhouse that turns raw Polymarket data into deep, visual, and interactive insights.

💡 What We’re Building

You’ll learn how to use the toolkit to:

Goal	What You’ll Get
Analyze markets	Identify volatility, spreads, and price bias
Track trades	Map buy/sell flow and volume distributions
Profile traders	Decode decision patterns, risk, and timing
Visualize outcomes	Compare YES vs NO bias and VWAP deviations
Understand behavior	Spot automated patterns and trade velocity

In short, we’ll turn Polymarket’s complex event streams into readable stories that guide profitable strategies.

Decode crack the strategy with the help of this toolkit.

Read more on code details about it here:

Polymarket-Market-Analyzer Code github repo

🧰 Step 1: Understanding the Toolkit

The Polymarket Advanced Analytics Toolkit is a modular Python project designed for developers, analysts, and quant researchers.

💼 Core Highlights:

🧠 Uses Polymarket’s official APIs (no scraping)
🎨 Generates 35+ professional visualizations
⚡ Performs advanced metrics: VWAP, volatility, trade velocity, risk scoring
📈 Outputs sleek, publication‑ready gradient plots

It transforms unpredictable price data into structured dashboards that help you answer questions like:

“When do volume spikes happen in YES markets?”

“Which traders are consistently early to trend reversals?”

“Where does hidden liquidity cluster below the surface?”

🧩 Step 2: Choose Your Analysis Type

The toolkit supports three primary kinds of analytics:

Type	Description	Example Use Case
🏪 Market Analysis	Study overall market trends, volatility, spreads	Identify good entry points
💸 Trade Analytics	Examine trade flow, volumes, and price impact	Detect manipulation or large moves
👤 Trader Analysis	Profile behavior of specific traders or bots	Learn profitable strategy patterns

🧩 Pro tip: Each analysis type can be combined — for example, start with a market overview and then drill into individual traders within that market.

🔬 Step 3: Perform Market Analysis

Once you fetch your market data, you can use the built‑in visualization suite to render different insights.

Example:

plot_market_analyzer("bitcoin-up-or-down-january-29-8am-et")

This will visualize:

YES/NO price curves with gradient fills
Spread movements over time
Volatility zones with color‑coded regions

Metric	What It Shows
Spread	Market confidence and liquidity
Volatility	Uncertainty or rapid opinion change
Momentum	Direction bias during active periods

These insights are gold for identifying where trades cluster and when markets move irrationally — critical for building automated arbitrage bots.

📈 Step 4: Trade Analytics – See What Others Can’t

Instead of reading a list of trades, visualize the rhythm of market activity.

plot_trade_analytics("bitcoin-up-or-down-january-29-8am-et")

This creates a dashboard containing:

🕒 Trade flow histograms across 50 time buckets
🔄 Buy vs. Sell pressure visualizations
📈 Price vs. Trade Size scatter plots
💧 Liquidity tracking charts

Example Output

A trader dumping several small BUY orders right before a large SELL?

That’s a liquidity bait — a clear pattern you can catch visually.

🎨 Step 5: Unlock Gradient Visualizations

Traditional charts tell you what happened.

Gradient analytics tell you why it happened.

plot_gradient_scatter_analytics("bitcoin-up-or-down-january-29-8am-et")

Each color represents a story:

🌄 Twilight palette – evolves with time
🔥 Plasma palette – shows trade volume intensity
🌊 Viridis + Coolwarm – reveal price‑to‑size relationships

Visual Types

Visualization	Meaning
Time Evolution Scatter	Detect trade bursts and pauses
Volume‑Weighted Timeline	Identify accumulation phases
Price‑Size Density Heatmap	Trace hidden liquidity
VWAP Analysis	Check how market trades cluster around institutional prices

These aren’t just pretty pictures — they’re direct signal decoders for quantitative traders.

🧠 Step 6: Decode Trader Behavior

Want to know who’s really driving the market?

Use the toolkit’s Trader Strategy & Timing Analysis modules:

plot_trader_strategy_analysis(market_slug, user_address)
plot_trader_timing_analysis(market_slug, user_address)

You can visualize:

🧭 Individual trade sequences
⏰ Trader activity by hour
⚖️ Buy/Sell ratios and risk preference
💰 Position sizing consistency

These charts help expose:

Algorithmic bots that rebalance too consistently
Major wallets that accumulate before volatility
Behavioral patterns like “revenge trading” or “scaling ladders”

📊 Step 7: Combine Results for Strategy Building

The best traders don’t rely on one graph.

The toolkit lets you connect all modules to create custom research dashboards.

Combine	Result
Market Analyzer + VWAP	Identify price mean‑reversion zones
Trade Analytics + Heatmaps	Detect potential liquidity zones
Trader Timing + Volume Density	Predict cluster‑driven entry timing

This multi‑layered view enables developers to:

Build smarter trading algorithms
Benchmark strategies against top performers
Detect real‑time manipulation or lag periods

🧮 Sample Outputs

Market: Bitcoin Up or Down - January 29, 8AM ET
Condition ID: 0x241b8e1b7...
Total Trades: 1,000
Price Range: 0.02 - 0.98
Spread Range: 0.01 - 0.15

Trader: ga
Total Trades: 47
Total Volume: 1,234.56
Win Rate: 68.1%
Most Active Hour: 14:00 UTC
Average Position Hold: 2.3 hours

✨ Within seconds, you can turn static Polymarket logs into actionable insights and strategy prototypes.

🧭 Step 8: Make It Your Own

Every visualization module supports custom gradients, filtering, and aggregation tuning. Example:

points_per_minute = 120   # high‑frequency
df_filtered = df[df['size'] > 10]

Or design your own gradient map:

from matplotlib.colors import LinearSegmentedColormap

custom_cmap = LinearSegmentedColormap.from_list(
    "sunset_glow", ["#fa709a", "#fee140"]
)

This lets you blend style and substance — making your dashboards both analytical and publication‑ready.

⚙️ Step 9: Interpret Like a Pro

Here’s a cheat‑sheet for interpreting your plots:

Pattern	Meaning
High spread	Low liquidity or high uncertainty
Tight clusters	Market consensus or support area
VWAP deviation	Reversion or institutional influence
Trade velocity spike	News event or bot trigger
Thick YES activity after dip	Smart money accumulation

These subtle hints can unlock consistent patterns — especially in data‑driven trading systems.

📚 Takeaways

✅ Prediction markets are a goldmine of structured behavioral data.

✅ With the Polymarket Advanced Analytics Toolkit, you can see under the surface — beyond charts and into trader psychology.

✅ Data visualization transforms randomness into pattern — and pattern into opportunity.

Whether you’re a quant developer building automated strategies or a trader optimizing execution, this toolkit turns Polymarket data into competitive insight.

🧩 Conclusion

Polymarket’s ecosystem is evolving fast.

The traders who win tomorrow aren’t guessing — they’re analyzing.

With this toolkit, you can:

Watch markets from every angle
Decode trading flow and strategy structure
Build bots grounded in hard data

🪄 Don’t predict. Analyze. Then act.

Made with ❤️ for the Polymarket developer and analytics community.

👉 Try it · Visualize it · Decode it.

Learn to play buy sell trade in c++ pump.fun

Voren Tai — Mon, 20 May 2024 05:20:33 +0000

Here is the script:

#include <iostream>
#include <string>
#include <map>
#include <curl/curl.h>
#include <json/json.h>
#include <unistd.h>

using namespace std;

size_t WriteCallback(void *contents, size_t size, size_t nmemb, void *userp) {
    ((string*)userp)->append((char*)contents, size * nmemb);
    return size * nmemb;
}

int main(int argc, char *argv[]) {
    // Parse command-line arguments
    string tradeType, mint, privateKey;
    float amount, priorityFee;
    int slippage, interval, frequency;

    for (int i = 1; i < argc; i += 2) {
        string arg = argv[i];
        if (arg == "--trade_type") {
            tradeType = argv[i + 1];
            if (tradeType != "buy" && tradeType != "sell") {
                cerr << "Error: Invalid trade type. Must be 'buy' or 'sell'." << endl;
                return 1;
            }
        } else if (arg == "--mint") {
            mint = argv[i + 1];
        } else if (arg == "--amount") {
            amount = stof(argv[i + 1]);
        } else if (arg == "--slippage") {
            slippage = stoi(argv[i + 1]);
        } else if (arg == "--priority_fee") {
            priorityFee = stof(argv[i + 1]);
        } else if (arg == "--private_key") {
            privateKey = argv[i + 1];
        } else if (arg == "--interval") {
            interval = stoi(argv[i + 1]);
        } else if (arg == "--frequency") {
            frequency = stoi(argv[i + 1]);
        } else {
            cerr << "Error: Unknown argument '" << arg << "'" << endl;
            return 1;
        }
    }

    if (tradeType.empty() || mint.empty() || privateKey.empty() || amount == 0 || slippage == 0 || priorityFee == 0 || interval == 0 || frequency == 0) {
        cerr << "Error: Missing required argument." << endl;
        return 1;
    }

    // Set up API request
    string url = "https://pumpapi.fun/api/trade";
    Json::Value payload;
    payload["trade_type"] = tradeType;
    payload["mint"] = mint;
    payload["amount"] = amount;
    payload["slippage"] = slippage;
    payload["priorityFee"] = priorityFee;
    payload["userPrivateKey"] = privateKey;

    CURL *curl;
    CURLcode res;
    string readBuffer;

    curl_global_init(CURL_GLOBAL_DEFAULT);
    curl = curl_easy_init();
    if (curl) {
        for (int i = 0; i < frequency; i++) {
            string payloadStr = payload.toStyledString();
            curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
            curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
            curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);
            curl_easy_setopt(curl, CURLOPT_POSTFIELDS, payloadStr.c_str());

            res = curl_easy_perform(curl);
            if (res != CURLE_OK) {
                cerr << "cURL error: " << curl_easy_strerror(res) << endl;
                return 1;
            }

            Json::Value response;
            Json::Reader reader;
            bool parsingSuccessful = reader.parse(readBuffer, response);
            if (!parsingSuccessful) {
                cerr << "Error parsing JSON response." << endl;
                return 1;
            }

            if (response.isMember("tx_hash")) {
                string transactionId = response["tx_hash"].asString();
                cout << "Transaction ID: " << transactionId << endl;
            } else {
                cerr << "Error: " << response.toStyledString() << endl;
            }

            readBuffer.clear();
            usleep(interval * 1000000); // sleep for interval seconds
        }
        curl_easy_cleanup(curl);
    }
    curl_global_cleanup();

    return 0;
}

Docs

https://docs.pumpapi.fun/

How to place a buy & sell trade on pump.fun tokens

Voren Tai — Mon, 20 May 2024 02:47:01 +0000

Use this snippet to place the buy trade for a pumpfun token.

const axios = require('axios');

const url = "https://pumpapi.fun/api/trade";
const data = {
    trade_type: "sell",
    mint: "YOUR_MINT_ADDRESS",
    amount: 1000,  # Example amount of tokens
    slippage: 2,
    priorityFee: 500,  # Optional
    userPrivateKey: "WALLET_PRIVATEKEY"
};

axios.post(url, data)
  .then(response => {
    const transactionId = response.data.tx_hash;
    console.log(`Transaction ID: ${transactionId}`);
  })
  .catch(error => {
    console.error(error);
  });

And to do the same using the python code:

import requests

url = "https://pumpapi.fun/api/trade"
payload = {
    "trade_type": "buy",                    # Buy or sell 
    "mint": "YOUR_MINT_ADDRESS",            # Token mint address
    "amount": 0.01,                         # Amount in SOL , if buying or of tokens if selling.
    "slippage": 5,                          # Desired slippage 
    "priorityFee": 0.003,                   # Value in SOL
    "userPrivateKey: "WALLET_PRIVATEKEY"   # Wallet private key 
}

response = requests.post(url, json=payload)

if response.status_code == 200:
    transaction_id = response.json()["tx_hash"]
    print(f"Transaction ID: {transaction_id}")
else:
    print(f"Error: {response.status_code} - {response.text}")

Endpoints:

API provides various endpoints for interacting with Pump.fun, including:

Feature	Description
Buy & Sell Trades	Place trades on Pump.fun programmatically
Newer Mints Details	Get details on new mints on Pump.fun
Token Metadata Fetching	Fetch metadata for tokens on Pump.fun
Pump.fun Specific Data	Query data specific to Pump.fun
Swap Quotes	Fetch swap quotes and input/output details

Read more on API documentation to learn how to use these features. 📄

API Endpoint 📍

Our API endpoint is:

https://pumpapi.fun/api

DEV Community: Voren Tai

Building Trading Applications with the Fomo API The Fomo API

Getting Started

Example: Finding a Trader

Example: Monitoring Swaps

Common Use Cases

What Can You Access?

Request Limits

Developer Resources

What Is the Fomo API? A Guide to Programmatic Access

What Is the Fomo API? A Guide to Programmatic Access

How to Access the API Programmatically

1. Create an API Key

2. Authenticate Requests

3. Retrieve Trading Data

4. Resolve a Trader

5. Retrieve Swap History

Available API Resources

Rate Limits and Responsible Use

Resources

Poly Weather Analyzer: The Ultimate Guide to Polymarket Weather Markets (2026)

Contents

System Objective

Data Collection

Polymarket Gamma API

Polymarket CLOB API

MicroaiBots — Solana & Prediction Market Trading Bots

Weather APIs

Event Parsing

Location Resolution

Resolution-station warning

Forecast Normalization

Uncertainty Estimation

Temperature Brackets

Exact whole-degree bracket

Market Probability

Pricing Edge

Interpretation

Expected Value

EV_i

Confidence Score

Source coverage

Outcome concentration

Confidence labels

Scenario Analysis

Sensitivity Analysis

Reading the sensitivity chart

Important Assumptions

Normality

Source independence

Equal weighting

Stable uncertainty

Resolution precision

MicroaiBots — Solana & Prediction Market Trading Bots

Future Improvements

A futuristic Bloomberg-inspired smart-money intelligence terminal for Solana wallets

🪐 Smart Wallet Terminal

📟 Overview

✨ Features

🧠 Four Wallet Intelligence Channels

📊 Live Terminal Metrics

⚡ Interactive Experience

🖥️ Terminal Preview

🗂️ Project Structure

✅ Requirements

🚀 Quick Start

1. Clone or download the project

2. Create a virtual environment

3. Activate the virtual environment

Windows PowerShell

Windows Command Prompt

macOS or Linux

4. Install dependencies

5. Start the terminal

⌨️ Keyboard Controls

Search-mode controls

🔍 Search and Filtering

↕️ Sorting Modes

🌐 Data Source

🎯 What Is This Terminal For?

🧰 Step 1: Understanding the Toolkit

🧩 Step 2: Choose Your Analysis Type

🔬 Step 3: Perform Market Analysis

📈 Step 4: Trade Analytics – See What Others Can’t

🎨 Step 5: Unlock Gradient Visualizations

🧠 Step 6: Decode Trader Behavior

📊 Step 7: Combine Results for Strategy Building

🧭 Step 8: Make It Your Own

⚙️ Step 9: Interpret Like a Pro