Complete Algorithmic Trading on Fyers Platform

Install IntelliJ IDEA community edition on Windows, install the Python plugin, and configure the Python SDK. Create a Python project, run a simple hello script, and verify the IDE works.

Explore Python basics for algorithmic trading, covering variables, data types (integers, floats, strings, booleans), lists, and arithmetic. Learn printing, input, string operations, concatenation, formatting, and basic debugging.

Master Python operators for algorithmic trading, covering arithmetic, assignment, comparison, and logical operators, plus floor division, remainder, and exponentiation with stock and portfolio examples.

Explore how Python conditional statements use if, elif, else, and nested if to drive decisions. Illustrate with moving averages, RSI, and Bollinger Bands for buy, sell, or exit signals.

Master NumPy basics for numerical arrays, indexing and slicing, reshaping, data types, and vectorized arithmetic to accelerate data processing in algorithmic trading.

Create and configure a Fyers trading API by signing in with otp, setting a redirect URL, granting permissions, and recording the app id and secret id for future login.

Fetch historical data for any symbol using the fyers API in Python, convert timestamps from UTC to IST, and export open, high, low, close, and volume to CSV.

Fetch long-horizon ohlc data from the fires api by pulling 50-day chunks from the inception date. Convert timestamps from UTC to Indian standard time for the 1-minute data.

learn how to start hourly candles at 9:15 by subtracting 15 minutes from the minute data index, resampling to 60-minute bars, then restoring the 9:15 start for accurate ohlc.

Retrieve live data and quotes for chosen stocks using the Fires quotes function, map symbols to a dictionary, and extract high, low, open, close, volume, LTP, bid, and ask.

Explore pivot point basics, including standard, fibonacci, and camarilla pivots, calculated from the previous session's high, low, and close, and apply bounce, breakout, and range trading strategies.

Learn how the pivot point indicator reveals pivot, support, and resistance levels (R1, R2, R3; S1, S2, S3) and how intraday range trading, breakouts, and reversals unfold on charts.

Learn to compute pivot points and R1, R2, S1, S2 in Excel using yesterday's high, low, and close from daily data.

Code the pivot point indicator in python with fyers data, resample 30-minute bars to daily, and compute yesterday's high, low, and close to derive pivot, r1, and s1.

Identify standard, long-legged, dragonfly, gravestone, and four-price doji patterns as key reversal signals. Apply doji strategy by prior trend, break of high/low, and volume near support or resistance.

Identify hammer candles in Excel by labeling days red or green and testing open-to-low vs the range and the two-times difference between high and close, for any time frame.

Identify the shooting star candlestick as a bearish pattern signaling a trend reversal, marked by a small body and a long upper wick, with a tiny lower wick.

Identify shooting star candles in Excel across any time frame by comparing open, high, low, and close, ensuring a bottom body and shadows at least twice as long.

Code a shooting star detector in Python using 30-minute historical data, compute per-row signals, and append a true/false shooting star flag to the dataframe for algorithmic trading.

Identify marubozu candles, a single candlestick continuation pattern with a large body and little to no shadows, signaling strong momentum and directional bias.

The lecture shows how to identify bullish and bearish engulfing candles in Excel using open, close, previous open/close, and a two-candle pattern on daily data.

Learn to implement bullish and bearish engulfing patterns in Python by processing candle data in a dataframe and applying conditions within separate functions to identify engulfing signals.

Moving averages identify market trends by smoothing price data with simple, exponential, and weighted variants. Use crossovers and price interactions, including golden and death crosses, for entries in trending markets.

Explore simple moving averages (SMA) on a five-minute chart, using 14 and 50 periods to identify uptrends, downtrends, crossovers, and trailing dynamic support and resistance.

Calculate the simple moving average (SMA) in Excel using a 14-period rolling window on closing prices, with almost 5600 data points, and explore SMA methods in Python.

Learn to compute the simple moving average (SMA) in Python using a rolling mean with a 14-period window on a one-minute CSV, including date parsing and validation against Excel.

Calculate a simple moving average using a Python for loop on a data frame, summing 14 closing values and adding an SMA column with NaN for the first 13 rows.

Learn to compute exponential moving average (EMA) in Python using a one-minute close series. The code uses ewm with a span and adds an EMA column, starting at first row.

Compute the weighted moving average in Python with four weights (40%, 30%, 20%, 10%) on closing prices; nan for the first three rows, then WMA from row four.

Create the Bollinger band indicator in Python, fetch data from Fyers, and plot upper and lower bands with a 15-window moving average on an MPL finance chart.

Learn the basics of the average true range (ATR) to gauge volatility, set stop losses, adjust position size, and spot breakouts with typical 1–3 ATR multiples.

Explore how to apply the ATR indicator in the Fyers app to gauge volatility, spot breakouts, and set stop losses based on ATR values for Nifty on an hourly chart.

Build an ATR indicator in Python by fetching 30-minute SBN data for five days, computing true range components, applying a 14-period EMA, and plotting ATR with prices.

Code the ATR indicator in Python using a for loop on a one-minute data frame, compute true range from high, low and previous close, then average over a 14-period window.

Explore the relative strength index (RSI), a popular technical indicator that measures price momentum, identifies overbought and oversold conditions, and signals divergences for risk management in trading.

Apply the relative strength index (rsi) on charts to identify overbought and oversold levels using 70 and 30, with a 14-period setting. Highlight bearish divergence to signal potential sell opportunities.

Compute RSI in Excel using a 14-period rolling average of gains and losses, and the relative strength formula. This method works with daily or intraday data and handles zero-loss cases.

Code RSI in Python using the Fyers API, compute a 14-period RSI from OHLC data, and plot closing prices with RSI and 70 red / 30 green thresholds.

Learn how the ADX measures trend strength, derives from plus/minus DM and ATR, and guides trend confirmation and risk management with other indicators.

Learn how to add ADX on the Fyers app to measure trend strength, not direction, and guide momentum-based trading strategies; combine ADX with moving averages or RSI for directional insight.

Calculate ADX in Excel by deriving true range from high, low, and previous close; compute plus DM and minus DM, then DI, DX, and a 14-day moving-average ADX.

Learn to compute the Supertrend indicator in Python using historical data, ATR, and basic and final upper and lower bands, then plot the result.

Code the supertrend indicator in python using pandas on a one-minute data feed, with a 14-period atr and a 3x multiplier, computing the upper and lower bands and trend direction.

Learn how stochastics measures trend reversals using the two-line oscillator, with k and d values, overbought and oversold levels, crossovers, and divergence.

Apply the stochastics indicator on charts to spot overbought and oversold zones, use %K/%D crossovers and divergence for potential reversals, demonstrated on a five-minute timeframe.

learn to calculate stochastics in excel by using highest high and lowest low 14 periods to compute percentage k, with a 3-period moving average for percentage d, and chart it.

Use a Python script on the Fyers platform to detect trends by analyzing the last three five-minute OHLC candles. Identify uptrends or downtrends via green candles and higher highs/lows.

Identify trend with five‑candle swing high/low pattern on a 15‑minute frame. See how green candles show higher highs and higher lows, while red candles show lower highs and lower lows.

This lecture shows building a Python candlestick pattern scanner that uses five minute and daily OHLC data, pivot points, and trend analysis to identify current pattern and momentum.